*4.5. Filtering Features for Clustering*

Standard Pearson correlation values were calculated between all sequence and structure features (Supplementary Table S2). If two features show a correlation with an absolute value above 0.7, only one was kept. In each case, we discarded the feature that shows a high correlation with a higher number of other features, or the one with the lower standard deviation. In total, none of the seven sequence parameters were discarded, but 13 out of the 24 structure parameters were omitted from subsequent clustering steps.


**Clinical situations that prefer cemented restoration:**

Very valuable information for clinicians was identified in a more recent review by Hamed et al, which comprised 12 clinical research (randomized controlled trials,

#### *Prosthetic Concepts in Dental Implantology DOI: http://dx.doi.org/10.5772/intechopen.104725*

clinical trials, prospective studies and retrospective cohort studies) with at least 2 years' follow-up time and published between 2010 and 2020 [16]. One of the most important advantages of cement-retained restorations is it's the passivity and simplicity in manufacturing process in comparison with screw-retained restorations. This feature comes to light especially when zirconia is used as material for framework. The review indicates that the cement-retained implant approach is appropriate when enhanced predictability, a patient's desire for superior aesthetic outcomes and a cost-effective method are present. Due to the significant complications associated with screw-retained restorations in terms of technical and prosthetic outcomes, cement-retained implant restoration results in more successful outcomes. Whereas a biological complication associated with the cemented implant promotes the use of screw-based implant reconstruction. Additionally, the screw-retained repair is more suitable for multiple unit implantation for patients with restricted inter-arch space. For instance, screw retention reconstruction is advised when inter-arch space is restricted (less than 4 mm) and retrievability is necessary. Similarly, cement retention can be used to compensate for inappropriately angled implants and when occlusion is easier to control without the hole.

It must be emphasized that prosthodontics plays a crucial role in maintaining mucosal homeostasis. Plaque accumulation and the soft tissue reaction are directly related to design, structural connections (screw-retained or cement-retained) and characteristics of materials. Proper prosthetic design with an appropriate emergence profile that promotes excellent oral hygiene and prevents plaque accumulation is unquestionably critical in preventing peri-implant mucositis [17]. According to de Tapia et al, when peri-implant tissue inflammation arises, the prosthetic design should be evaluated and, if necessary, adjusted to correct design issues that may obstruct good hygiene and to reduce biomechanical stress factors that may be involved [18].


*Edited by Başak Kılıç Taşeli*

Sewage management refers to the process of collecting, treating, and disposing of the millions of gallons of wastewater produced daily by households, industries, and commercial establishments. It is vital to treat and dispose of this wastewater appropriately to safeguard public health and the environment. The objective of sewage management is to decrease the number of pollutants in wastewater before it is discharged into water bodies or reused for other purposes. Sewage management is a crucial element of modern society, yet it is frequently disregarded or taken for granted. This book offers a comprehensive overview of the various aspects of sewage management, covering the basics of wastewater treatment and disposal, and the various technologies and processes involved in sewage treatment. Additionally, the book provides case studies of successful sewage management practices from around the world, highlighting best practices and innovative solutions. It is hoped that this e-book will serve as a valuable resource for anyone seeking to better understand this critical aspect of modern society.

> *Usha Iyer-Raniga, Sustainable Development Series Editor*

> > ISBN 978-1-83768-535-6

Sewage Management

ISSN 2753-6580

Published in London, UK © 2023 IntechOpen © Andre2013 / iStock


*Article* **Understanding the Relationship between Predictors of Alcohol Consumption in Pregnancy: Towards E**ff**ective Prevention of FASD**

**Isabel Corrales-Gutierrez 1,2, Ramon Mendoza 3,4,5, Diego Gomez-Baya 3,4,\* and Fatima Leon-Larios 6**

Received: 29 December 2019; Accepted: 18 February 2020; Published: 21 February 2020

**Abstract:** Background: Prenatal alcohol exposure can produce serious changes in neurodevelopment that last a lifetime, as well as a wide range of congenital abnormalities, and is the main non-hereditary, avoidable cause of intellectual disability in developed countries. It is therefore crucial to understand the determinants of alcohol consumption during pregnancy. This study is aimed at determining the factors that predict it, as well as the interactions between them. Methods: A cross-sectional study was carried out using a random sample of 426 pregnant women being treated at the outpatient clinic of a public university hospital in Seville (Spain), when they were in their twentieth week of pregnancy. A custom-designed questionnaire was used for data collection and applied in the course of an interview administered by trained health professionals. The data collected were analyzed using hierarchical regression, moderation analysis, and a structural equations model. Results: Alcohol consumption prior to pregnancy proved to be the most powerful predictor of alcohol intake during pregnancy. Other particularly significant predictors were the percentage of professionals who gave correct advice to the expectant mother—not to consume any alcohol during pregnancy—and perception of the risk from drinking wine during pregnancy. The number of pregnancies correlates positively with alcohol intake during pregnancy, while the expectant mother's level of education correlates negatively. Conclusions: Identifying these predictive factors will allow the design of more effective fetal alcohol spectrum disorder (FASD) prevention strategies.

**Keywords:** prevention; alcohol consumption; pregnancy; FASD; lifestyle; public health; Spain


**1. Introduction**

Tourist activity is one of the main sources of wealth in many areas. However, it also a ffects the environment, cultural resources, and the hosting population. Due to this, the World Tourism Organization (UNWTO) is urging di fferent governments to consider sustainability as a global goal.

The emergence of new 2.0 net collaborative economies has brought about an increase in the number of travelers and the intensification of mass tourism, induced by a change in paradigm on the tourist-housing sector in major cities around the world, due to the proliferation of tourist housing.

There does not seem to be consensus on the definition of the collaborative-economy concept [1]; neither the European legal system nor that of each of the member states seems to be able to solve the problems that could arise from these new forms of business [2]. Hence, the European Commission decided to publish the "European Agenda for the collaborative economy" in which recommendations were directed to national legislators to adapt the regulations of the member states to the new needs of the emerging market for a collaborative economy. The European Commission [3] defines a collaborative economy as "Business models in which activities are facilitated through collaborative platforms

that create an open market for the temporary use of goods or services often o ffered by individuals. In general, collaborative-economy transactions do not imply a change in ownership and can be done with or without profit". Within a collaborative economy, the services that have experienced the fastest growth have been those related to transport and accommodation, both being closely related to tourism. Regarding the accommodation sector, one can find modalities in which there is no compensation, such as "couch-surfing" or "warm showers" [4]; in others, such as "home-swapping" or "night-swapping", there is reciprocity between participants [5]. On the other hand, we find modalities in which monetary consideration is paid, which is the case with our study. This sector has already accounted for more than 50% of the total number of operations carried out in Europe in 2015 within the scope of the collaborative economy [6].

According to information provided by DataHippo [7], over 238,000 adverts on Airbnb, one of the most globally important collaborative-economy sites, colonizes cities and tourist areas around Spain. Madrid and Barcelona are at the top of the list, followed by accommodation adverted on the Mediterranean coastline and the Canary and Balearic archipelagos. This is a specialized market, where only 5% of property owners are professionals, and individuals with more than one property represent one-third of tourist-housing o ffers.

However, not all tourist increase has been positive in its entirety; there are critical movements of the recent tourist development and growth, which shows that this is a globally shared phenomenon. Some of these negative e ffects can be seen in issues such as Touristification and gentrification processes in Berlin [8,9], tensions due to socio-spatial transformations and touristification processes in the slums in Rio de Janeiro [10]; social unrest because of housing dispossession and the urban revalorization and touristification processes in Palma de Mallorca historical center [11]; the rising unrest and annoyance regarding the overcrowding and socio-spatial transformations in the center of Amsterdam [12,13]; the emergent mobilization related to the impact of tourism on Paris, especially regarding the proliferation of tourism housing [14]; the so-called Airbnb syndrome in Reykjavik [15]; the riots against cruises because of the increase in cruise passengers [16] and the consultative referendum held in Venice; the protests carried out by Hong Kong citizens against Chinese tourists [17]; and the emergence of people resisting the use of the land and local resources in Goa, India [18].

In many tourist destinations, the debate has focused on wider analysis of urban and political processes, and existing forces favor a growing "politicization from the grassroots" [19]. It should be noted that, in the tourist landscape, it is not only a matter of draining resources but also the rupture of necessary conditions for tourist activity to be satisfactory for all involved agents. Thus, every destination, depending on its particularities, products, and services, has to be assessed considering their capacity to bear tourist pressure [20].

One of the most significant cases is how tourist housing is a ffecting the prices of the real-estate market. In Spain, the average housing-rent price has increased by 18.6% in the last five years, between 2013 and 2018, Barcelona being the city with the highest increase (47.5%), followed by Madrid (38%), according to real-estate agency Fotocasa [21]. Moreover, five provinces, Baleares, Las Palmas, Salamanca, Barcelona, and Madrid, have already reached their historical maximum in 2018, exceeding the figures in 2007. Henceforth, although there are barely surveys to confirm it, many sectors relate this increase in rent prices to the proliferation of tourist housing, which is also said to be accelerating urban-gentrification processes.

As can be observed, the tourist-housing phenomenon is not free from controversy. There is confrontation between social and economic agents in the cities, since there is no global legal regulation regarding this phenomenon; in the case of Spain, autonomous communities and city councils are the responsible institutions for launching various regulatory initiatives.

The lack of a model regulating the tourist-housing phenomenon might involve serious risks. Before such a situation, deciding agents need to be provided with a tool that enables them to diagnose the situation, so that they can suggest initiatives to move towards a sustainable tourist model. It is necessary for them to analyze the concept of reception capacity that theoretically refers to the optimal

use of land pursuant to its sustainability. Gómez and Gómez [22] defines it as "an area's degree of adequacy or capacity for a certain activity, bearing in mind both how the environment meets its locational requirements and the e ffects of that activity on the environment," outlining the contribution by Canter [23–25], Clark and Bisset [26], Rau and Wooten [27], Hollick [28], and Lee [29,30], among others. To study reception capacity, di fferent authors have o ffered a scientific basis to techniques and procedures: Voogd [31], Janssens [32], Eastman et al. [33], Jankowski [34], Triantaphyllou [35], Roy [36], and Munda [37], and, in Spain, Romero [38], Barredo [39], Barba and Pomerol [40], Santos [41], Moreno [42,43], and Galacho and Arrebola [44]. In this sense, the bibliography highlighting multicriteria assessment techniques, combined with geographical information systems to evaluate an area's reception capacity on various topics, is extensive: Barredo and Bosque [45], Ocaña and Galacho [46], Bosque and Moreno [47], Gómez y Barredo [48], Molero et al. [49], Moreno and Buzai [50], and Galacho and Arrebola [44].

To face the issue of the development of tourist housing, the present work's objective is to o ffer a methodology supported by multicriteria decision methods in the field of geographical information systems, that enables us to assess tourist-housing reception capacity in Cordoba (Spain) based on tourist-sustainability criteria. Cordoba is a city with four UNESCO World Heritage Sites, with a great tourist claim, and with important threats and weaknesses regarding tourist housing according to a study carried out by the Council of Cordoba [51].

According to Galacho and Ocaña [46], "the advantage of the combined use of multicriteria decision methods and geographical information systems is the possibility of rigorously solving the interrelation between the di fferent variables of the area". As a result, we obtained an information layer about the city's central district that classifies every neighborhood based on an assigned rating according to value judgments. These judgments were defined following the guidelines set by the World Tourism Organization regarding issues that must be considered when planning a destination under sustainability goals.

#### **2. Materials and Methodology**

To analyze the tourist-housing reception capacity of Cordoba, we used the analytic hierarchical process (AHP), developed by Tomas L. Saaty [52]. This is a tool to address the discrete multicriteria decision problems, consisting of di fferent criteria and a certain number of alternatives, considering the opinions of all the agents that intervene in the decision. The problem is displayed on a hierarchical structure that indicates the objective, criteria, subcriteria, and corresponding alternatives to then calculate the influence of every factor that is part of the problem. The resulting choice is then justified since it is based on the obtained numerical results, favoring the transparency and objectivity of the process.

The chart below represents the phases of the analytic hierarchical process (see Figure 1).

**Figure 1.** Phases of analytic hierarchical process. Source: Casañ [53].

#### *2.1. Determining Criteria, Subcriteria, and Alternatives*

According to the World Tourism Organization (UNTWO) [54], sustainable tourism is defined as the one that "meets the needs of present tourists and host regions while protecting and enhancing opportunities for the future. It is envisaged as leading to the management of all resources in such a way that economic, social, and aesthetic needs can be fulfilled while maintaining cultural integrity, essential ecological processes, biological diversity, and life-support systems". To measure the degree of sustainability, the OECD [55] distinguishes two approaches, the accounting and the analytical; in our study, we opted for the analytical since it provides adequate multidimensional evaluation as a local planning tool according to the objective of our research. This instrument, according to this approach, is given by "a set of indicators of sustainable tourism, understanding as such the measures that provide the necessary information to better understand the links and impact of tourism with respect to the cultural and natural environment in which it develops activity and on which it is widely dependent" [56]. Therefore, to obtain an analytical measure of sustainability, it is necessary to disaggregate the sustainable-tourism objective by identifying the aspects that constitute each dimension, and identifying the indicators that allow measuring each of the above aspects. To ensure that their values show progress towards a more sustainable state, indicators must meet the criteria of scientific validity, representativeness, relevance, reliability, sensitivity, predictive nature, understandability, comparability, quantification, cost e fficiency, transparency, and geographical coverage [57]. Once the system was defined, we assigned the variables taking as reference specialized works that define sustainability indicators at the local level. Attending to the objective of our research and our area under study being the city of Cordoba (Spain), we took works as reference that defined a set of synthetic indicators of sustainable tourism for the tourist destinations of Andalusia (Spain): Blancas et al. [58]; Ávila et al. [59]; Dachary and Arnáiz [60]; Fullana and Ayuso [61]. For this, we developed a hierarchical structure with three levels (Figure 2). On the first level, the three main criteria (social, economic, and environmental dimension) are shown, each one defined based on new subcriteria corresponding to the second (13 subcriteria) and a third level (10 subcriteria), respectively. In the social dimension, issues related to the socio-cultural impact that tourist housing has on the environment, the resident population, and cultural heritage were collected; in the economic dimension, aspects related to tourism activity as economic activity and its viability are represented in the long term; finally, in the environmental-dimension criterion, aspects related to the protection and preservation of the environment, as well as the future viability of tourism, were considered.

**Figure 2.** Chart of criteria, subcriteria, and alternative hierarchies. Source: Information compiled from Blancas et al. [58]; Gallego and Moniche [62]; Sancho and García [63]; Bowen and Valenzuela [64].

The criteria and subcriteria obtained from the three previously mentioned dimensions were used to value the alternatives in the different neighborhoods in the central district of Cordoba (Figure 2). These are the possible approaches to the problem, although the choice does not imply that the chosen alternative is optimal to solve it, but the best among all available possibilities to reach the goal [53].

## *2.2. Determining Preferences*

To establish priorities, we needed to compare criteria, subcriteria, and alternatives in pairs. To do so, we made value judgments expressed numerically using Saaty's AHP fundamental scale [52]. This scale gives punctuations from 1 to 9, 1 being the same importance between two elements and 9 extreme importance of an element over the other. These value judgments were issued by a representation of different groups that are affected by the tourist-housing phenomenon, such as the public sector (public managers) and private sector (restaurant managers, taverns, souvenir shops, traditional commerce, resident residents, tourists, and neighborhood associations); through a total of 148 conducted interviews, nonprobabilistic sampling was carried out for convenience in the case of public officials, the private sector, and neighborhood associations, while for residents and tourists residents, simple random probabilistic sampling was followed. Subsequently, comparisons are represented through the paired-comparison matrix (Figure 3) that shows the dominant and dominated values. It is a square matrix *n x n*, in which *aij,* numerically expresses the preference of an element in the *i* row when compared with an element of the *j* column, for *i*= 1, 2, 3, ... *n* and *j*= 1, 2, 3, ... *n;* therefore, when *i* = *j*, the value of *aij* = 1, since the element is being compared to itself.

$$\mathbf{A} = \begin{pmatrix} 1 & a\_{12} & \cdots & a\_{1n} \\ a\_{21} & 1 & \cdots & a\_{2n} \\ \vdots & \vdots & \vdots & \vdots \\ a\_{n1} & a\_{n2} & \vdots & 1 \end{pmatrix}$$

**Figure 3.** Paired-comparison matrix.

This matrix is based on four axioms [65]: reciprocity: *aij* = 1/*aji*; homogeneity, since all compared elements must belong to the same hierarchical level; dependence, which means that there must be hierarchical dependence between elements from two consecutive levels; and consistency, meaning that, when the paired-comparison matrix is perfectly consistent, the following is fulfilled: *aij* = *aik*/*ajk* for *i, j* and *k* = 1, 2, 3 ... *n.*

Hereafter, we used an approximation method to obtain priorities from judgments given in the comparison matrix *n* × *n*. The first step was to procure the normalized matrix: we summed the values on every column and divided every box of the column by its summation:

$$C\_j = \sum\_{i=1}^{n} a\_{ij} \, j = 1, 2, 3 \dots n. \tag{1}$$

The normalized paired-comparison matrix is

$$\mathbf{N} = \left\| n\_{\mathbf{i}\mathbf{j}} = \mathbb{a}\_{\mathbf{i}}/\mathbb{c}\_{\mathbf{i}} \right\| \mathbf{i}\mathbf{j} = 1, 2, 3 \dots \text{n.} \tag{2}$$

Once we had the normalized matrix, we calculated the relative priority of each of the compared elements. We obtained an average value for every row in the normalized matrix, these values being

$$p\_i = \frac{1}{n} \sum\_{j=-1}^{n} n\_{ij}.\tag{3}$$

Since the hierarchy (Figure 2) is made of criteria and subcriteria, the three criteria's priorities were calculated according to the objective. Then, comparison matrices were made for each subcriterion, resulting in the relative priorities for each subcriterion on the second level. Those were multiplied by the corresponding criterion's priority to determine how it affects the objective. The process for the third-level subcriteria was the same. Afterward, to determine each alternative's priority, 20 relative comparisons matrices were made (corresponding to the 20 not-itemized subcriteria). Subsequently, aspects taken into account and data sources used for the pertinent survey are indicated (Table 1), and all of them properly georeferenced:

**Table 1.** Database used to evaluate each subcriterion.

> Source: Own elaboration.

QGIS software was used for treating georeferenced information. It was necessary to apply a spatial-disaggregation technique for the following layers of information: population retention, young population, aging population, social burden, and generated employment. Those layers have a 250 × 250 m square polygon vector format, so when assigning data to the territory subject of study, some polygons were divided. To do so, the areal-interpolation technique was used: information about the distribution values of a variable from an origin layer for a certain territory (in this analysis, demographic

spatial data in statistical enmeshes) was transferred to another layer of destiny information (territory subject of study) through their intersection. Then, the superficial proportion that each polygon on the origin layer had on the destiny layer was calculated to obtain the distribution of each variable in the new spatial units.

Afterward, we obtained each alternative's relative priority regarding the corresponding criterion or subcriterion; then, each alternative's general priority regarding the corresponding criterion or subcriterion was calculated by multiplying the relative priority by the compared criterion or subcriterion's general priority. Then, all priorities for each alternative were summed to obtain its priority regarding the objective [73]. Finally, the AHP allowed measuring the inconsistence of judgments through the consistency ratio, and they had to be revised and corrected. For 3 by 3 matrices, the value of the consistency ratio had to not be higher than 5%; in the case of 4 by 4 matrices, it would not exceed 9%; for all the other matrixes, it would be 10% or less [73]. The software used to carry out the analytic hierarchical process was Total Decision.

The result of the process is summarized in a layer of information that shows zoning of the studied area with a valuation assigned to every part of the territory depending on its capacity to accept the evaluated uses.

#### *2.3. Implementation on Urban Area*

The territory subject of study was Cordoba (Spain), a city whose four UNESCO World Heritage Sites have had increased mass tourism in the last few years, besides an unregulated increase in tourist accommodation. Out of the 10 total territorial districts that conform to the city of Cordoba, we chose the central district since it hosts the highest concentration of tourist housing, with 1456 tourist housing over a total of 24,457 built houses, that is, 5.95% [51]. Here (Figure 4), the distribution of tourist housing for each neighborhood in the central district is shown:

**Figure 4.** Tourist housing per neighborhood in the central-district map. Source: Own elaboration.

There are eight neighborhoods over the tourist-housing average (6.02%), such as the neighborhoods of La Catedral, San Francisco-Ribera, El Salvador y la Compañia, and San Pedro, which exceed 10% of tourist housing. There are also ten neighborhoods under the average, such as Cerro de la Golondrina, Ollerías, and El Carmen, which do not reach 1%.

According to a recent study carried out by the Council of Cordoba [51] on the effects that tourist housing has on the city of Córdoba, the city has the following threats and weaknesses: Regarding threats, there is a gradual loss of population and the substitution of residential use for other uses, weakening of traditional commerce, saturation of public spaces, and coexistence deterioration, detraction of housing from the rental market, and price increase, and deterioration of cultural tourism. With respect to weaknesses, there is a lack of knowledge about existing tourist homes and clandestinity in the activity of some caused due to the autonomous regulatory framework, the absence of municipal regulation of housing for tourism purposes, the existence of empty buildings, and dizzying growth in the supply of housing for tourism purposes.


*3.4. Evaluation*

To quantify the degree to which a test domain is OOD, different metrics were applied, namely Euclidean distance, Cosine similarity, Wasserstein distance, MMD, and DC. Each metric was applied to the representations of each model before the classification stage. Regarding the Wasserstein distance [51], the Wasserstein-1 version was used and is given by:

$$\mathcal{W}\_1(\mathbf{X}, \mathbf{Y}) = \inf\_{\pi \in \Gamma(\mathbf{X}, \mathbf{Y})} \int\_{\mathbb{R} \times \mathbb{R}} |\mathbf{x} - \mathbf{y}| \mathrm{d}\pi(\mathbf{x}, \mathbf{y}), \tag{1}$$

where Γ(*X*,*Y*) is the set of distributions whose marginals are *X* and *Y* on the first and second factors, respectively. *x* and *y* are samples from each distribution *π*(*x*, *y*) from the set. Intuitively, the distance is given by the optimal cost of moving a distribution until it overlaps with the other. In our experiments, *x* and *y* are the feature representations of subsets of the train and test data, thus *W*1 represents the cost of mapping the distribution of *x* into the distribution of *y* (or vice versa).

Regarding the MMD , this is a kernel-based statistical procedure that aims at determining whether two given datasets come from the same distribution [52]. Given a fixed kernel function *k* : *X* × *X* → R and two datasets *X*,*Y* with sizes |*X*| = *n*, |*Y*| = *m*, the MMD can be estimated as:

$$\text{MMD}(X, Y) = \frac{1}{n(n-1)} \sum\_{i \neq j} k(\mathbf{x}\_i, \mathbf{x}\_j) + \frac{1}{m(m-1)} \sum\_{i \neq j} k(y\_i, y\_j) - \frac{2}{nm} \sum\_{i,j} k(\mathbf{x}\_i, y\_j) \tag{2}$$

Intuitively, the MMD measures the distance between *X* and *Y* by computing the average similarity in *X* and *Y* separately, and then subtracting the average cross-similarity between the two datasets, where the similarity between two instances is quantified by means of the selected kernel *k*. In this work, a simple linear kernel was selected. Furthermore, as for the Wasserstein distance, *x* and *y* represent the feature representations of subsets of the train and test data. Thus, MMD quantifies the average kernel similarity among instances in *x* and *y*, discounted by the cross-similarity between the two datasets.

The DC, by contrast, is a multivariate hypothesis testing procedure for the hypothesis that two samples of data come from the same distribution: having fixed a representative data sample, the obtained *p*-value, then, can be considered as a measure of how much any other data sample is OOD with respect to the representative one. In particular, scores close to 0 can be interpreted as being most likely OOD (since, assuming the null hypothesis of the two data samples coming from the same distribution, observing a *p*-value close to 0 has low probability). While the DC cannot be defined and computed by means of a closed-form

procedure, in [25] a permutation-resampling algorithm (see Algorithm 1) was defined to compute the corresponding *p*-value, based on the selection of a base distance metric.

**Algorithm 1** The algorithm procedure to compute the similarity between the two dataset *T* and *V*, using the Degree of Correspondence (DC).

**procedure** DC(*T*, *V*: datasets, *d*: distance, *∂* distance metrics) *dT* = {*d*(*t*, *t*) : *t*, *t* ∈ *T*} For each *v* ∈ *V*, find *tv* ∈ *T*, nearest neighbor of *v* in *T T*|*V* = {*t* ∈ *T* : *v* ∈ *Vs*.*t*.*t* = *tv*} ∪ *V dT*|*V* = {*d*(*t*, *t*) : *t*, *t* ∈ *T*|*V*} *δ* = *∂*(*dT*, *dT*|*V* ) Compute DC = *Pr*(*δ* ≥ *δ*) using a permutation procedure return DC **end procedure**

The selection of the distance metrics *∂* in Algorithm 1 is important to obtain sensible results for the DC. Intuitively, *∂* should represent the appropriate notion of distance in the instance space of interest. In [53], lacking any appropriate definition of distance in the instance space, the authors suggest the use of a general baseline, e.g., the Euclidean or cosine distance, or robust non-parametric deviation metrics, e.g., MMD or Kolmogorov– Smirnov statistics.

In previous work, model performance has been evaluated using metrics such as accuracy, sensitivity, specificity, precision, recall, and f1-score [1]. As class imbalance is common in most publicly available HAR datasets (see Table 2), f1-score is used as the main performance metric since it is more robust than accuracy in these settings [30]. To be able to compare deep learning models and classic models with HC features, the f1-scores are compared in tasks across different OOD scenarios and including five public HAR datasets.

#### **4. Experiments and Results**

The main purpose of this paper is to compare the performance of HC features and deep representations in different OOD settings for HAR. A scheme of the full pipeline used for the experiments is presented in Figure 4.

HAR is a classification task that usually involves multiple domains, easily turning into a domain generalization task if the domains are considered when splitting the data. We devise four domain generalization settings, starting with a baseline ID setting where 30% of each dataset is randomly sampled for testing, and three OOD settings: (a) splitting by user within the same dataset, where approximately 30% of the users were assigned to the test set—OOD by user (OOD-U); (b) leaving a dataset out for testing, while including all the others for training—OOD with multiple source datasets (OOD-MD); (c) training on a dataset and leaving another for testing, running all the possible combinations—OOD with a single source dataset (OOD-SD). To obtain a direct comparison, the test set of OOD-U is used as a test set for all the OOD settings. Of the three OOD settings, OOD-U is the one that is expected to be closest to the training distribution since it is drawn from the same dataset, where devices and acquisition conditions are usually similar. It is followed by OOD-MD, since joining all the datasets (except one) for training averages their distributions onto a more general space. Subsequently, as it includes only a single dataset for training, OOD-SD should capture the largest distances between train and test distributions.

**Figure 4.** Scheme of the experimental pipeline.

In order to validate our hypothesis about the ordering of the distances between the train and test splits on our four settings, different metrics were applied to the feature representations. This experiment has the following objectives: (1) to validate that our three OOD settings are in fact OOD; and (2) to obtain the best metric for our main experiments, which should output values that agree with our ordering hypothesis for both HC features and deep representations. For models based on HC features, metrics were computed directly from the features. In contrast, for deep models, metrics were calculated from the hidden representations of the last layer before classification.

We note that different distance metrics have different scales, therefore, making their interpretation and comparison more difficult. For this reason, we computed distance ratios instead of raw distances, so as to make the values of the different metrics more consistent across tasks. The distance ratios were computed for each task, i.e., setting/dataset combination, using the following equation:

$$\text{Distance\\_ratio} = \frac{\partial (tr\_{1\prime} tr\_1)}{\partial (tr\_{2\prime} tr\_3)}\,'\,\tag{3}$$

where *∂* is a distance metric and *tri* and *tsi* are subsets randomly sampled (with replacement) from the train and test sets, respectively. The sample size is half the minimum of the train and test set lengths. By contrast, for the DC, the raw value without any ratio-based normalization was used, since it is already normalized in the [0, 1] range and is able to deal with any data representation directly.

A comparison of the considered metrics based on the TSFEL features is presented in Table 3. It is easy to observe that all the metrics agree with the OOD ordering hypothesis stated above. Indeed, the value of all metrics was higher for the OOD-U, OOD-MD, and OOD-SD (respectively, in this order) than for the ID setting. In particular, it can be seen that DC with Euclidean-based metrics saturates to values close to zero for all three OOD settings, indicating that, by the comments above on the interpretation of this score, the test sets are likely to be OOD.

Table 4 shows a comparison of the considered metrics based on the CNN-base representations. In contrast to the case of TSFEL features, the metrics showed a much lower degree of agreement with the OOD ordering hypothesis. First, it can be noted that only Wasserstein and MMD have values that clearly increase with the expected degree of OOD, being in agreement with the results of the TSFEL representations and, consequently, with our OOD ordering hypothesis. Nonetheless, it can be verified that both metrics had a large degree

of variation, with the confidence intervals for the ID, OOD-U, and OOD-MD partially overlapping. In the case of DC Cosine, the score for the OOD datasets was higher than that for the ID one. This seemingly paradoxical behavior may have an intuitive geometric explanation, as it may be a consequence of the transformations that take place during training, which influence the shape of the instance space and possibly make the representations of instances that would otherwise be OOD closer to the training data manifold. In support of this hypothesis, it can be easily observed that most metrics reported a significantly different value for the OOD-SD setting than for the other OOD settings, showing that the training of the deep learning model had an important influence on the natural representation of the data manifold. In this sense, both the Wasserstein and MMD metrics seemed to be more apt at naturally adapting to this change of representation.

**Table 3.** Comparison of metrics over all four domain generalization settings based on the TSFEL feature representations. For each setting, values were averaged over every test set. All metrics are ratios except the ones with (\*).

**Table 4.** Comparison of metrics over all four domain generalization settings based on the CNN-base representations. For each setting, values were averaged over all the datasets. All metrics are ratios except the ones with (\*).

Thus, as a consequence of these results, we chose the Wasserstein distance ratio as our main metric to quantifiy the degree of OOD due to the fact that it agrees with our hypothesis when using both TSFEL features and deep representations as input. This metric has also been applied by Soleimani et al. [5] to compute distances between source and target distributions.

Our experiments were run on an NVIDIA (Santa Clara, CA, USA) A16-8C GPU and an AMD (Santa Clara, CA, USA) Epyc 7302 processor with python version 3.8.12 and Visual Studio Code (Microsoft, Redmond, WA, USA) as the development environment. All the learning models were implemented using the PyTorch library [54]. Adam [55] was adopted as the optimizer used for the training process. To reduce bias [16], results were averaged over nine combinations of three different batch sizes (64, 128, and 256) and three learning rates (0.0008, 0.001, and 0.003). To account for class imbalance, the percentage of instances per class in the training set was given to the cross-entropy loss function as class weights.

To make the experiments as agnostic to the training method as possible, the same procedure was used for training the classifiers based on HC features and the deep learning models. Figure 5 shows the training and validation loss over the course of training for a single task. The chosen task was the OOD-U setting on the SAD dataset, an example of a task in which there was a verified occurrence of instability in training. One of the ways to handle this instability is by ending the training process earlier—early stopping [56].

**Figure 5.** Evolution of loss by epoch on SAD dataset in the OOD-U setting. The red dots indicate the minimum loss of each curve.

Over all the tasks, most models reached plateaus on validation performance after 30 to 50 epochs, so the training process was limited to 140 epochs to leave a margin for models to converge, but not so much as to fully overfit the data. For validation, we randomly sampled a 10% subset of the training data without replacement. While training, a checkpoint model was saved every time the validation loss achieved its best value since the start of training. Our early stopping method consisted of stopping training if the validation loss did not improve for 30 epochs in a row, which proved helpful in cases where training was not very stable. In these cases, the validation error oscillates, increasing for a certain number of epochs before decreasing again and, on many occasions, achieving a slightly lower error rate than in any of the previous epochs, which can be seen in the loss curves for the CNN models in Figure 5. This resembles the effects of double descent [57]. In our case, one of the causes of such unstable training may be the fact that these datasets are noisy, due to the diversity in users, devices, and positions, among other factors. It may also be a consequence of overparameterization, as the phenomenon was much more pronounced when training CNNs, which have significantly more parameters than our MLP and LR models. Both these potential causes were documented by Nakkiran et al. [57].

The evolution of the f1-score over the Wasserstein distance ratio for the best performing model of each family (CNN-base and TSFEL+LR) is documented in Figure 6. For each combination of model, dataset, and setting, the average and standard deviation of the f1-score were computed over nine different runs with varying learning rates and batch sizes. The CNN-base embeddings were chosen to compute distance ratios for this figure since they contain less outliers when compared to the distance ratios computed from TSFEL representations (see Figure A2). It can be verified that, initially, the CNN model outperforms the model using HC features. However, as the distance between train and test domains increases, the situation is reverted, with the classic approach outperforming the CNN. This suggests that HC features are more robust to the shifts that occur in OOD data. The regression curves reinforce the idea of OOD stability. As expected, there is a negative correlation between f1-score and distance ratio, meaning that performance decreases as the test data becomes more distant from the distribution seen during training. In general, the distance ratios given by the Wasserstein distance appear to agree with the previously stated OOD ordering hypothesis, with OOD-SD being the most OOD of the three settings, followed by OOD-MD and OOD-U, respectively. Still, a few outliers can be seen in the figure. The higher values of standard deviation for the CNN indicate that these models are more susceptible to the choice of hyperparameters, which is reasonable due to the much larger number of trainable parameters. However, it is not always ideal to have

such variability, as it indicates that the validation loss has become less correlated with the test loss. In practice, an apparently good model may perform surprisingly well in some settings while failing in situations that would otherwise be trivial to a simple model.

**Figure 6.** F1-score vs. log(distance ratio). Each marker represents a different task. Distance ratios are based on the CNN-base embeddings. Error bars represent one standard deviation away from the mean. The natural logarithm was applied to the distance ratios to make the regression curves linear.

More detailed results are presented in Table 5. For each combination of model and setting, the average and standard deviation of the f1-score were computed over all five datasets. The last column represents the average of the three OOD settings, which gives an idea of the overall generalization performance. The significant overturn from the ID to the OOD settings can be noticed in the table. TSFEL + LR, which had the worst ID f1-score (90.54%), turned out to be the best overall in the OOD regime, with an f1-score of 70% for the average of all three OOD settings. Using an MLP instead of LR slightly decreased the overall OOD performance to 69.55%, while increasing the ID performance to 92.87%, becoming closer to the deep learning results. This phenomenon may be related to an increase in the number of trainable parameters. Including HC features as an auxiliary input to deep models improved both ID and OOD results, with the hybrid version of CNN-base being the deep learning model with the strongest generalization performance (average OOD f1-score of 66.95%). However, this improvement is still insufficient to reach the OOD robustness of models solely based on HC features.

**Table 5.** Average f1-score in percentage over all the tasks in a given setting. Values in bold indicate the best performance for each setting.

Despite being simpler than the ResNet, the CNN-base model achieves a slightly higher generalization performance. On the other hand, CNN-simple, the simplest deep learning

model, did not perform well in OOD tasks. There appears to be an optimal number of parameters, possibly dependent on the architecture, so more studies should be conducted to understand this trade-off.


*2.3. Data Analysis*

After collecting samples of fauna, further research was carried out in the laboratory of the Department Agricultural Zoology of the Faculty of Agriculture in Zagreb. The collected soil fauna from each sample was separated from the contaminants and transferred into containers with 96% alcohol. This was followed by sample identification. The determination was carried out with the help of light microscope and standard keys [26–31]. All organisms found were classified into the appropriate classes (Insecta, Arachnida, Malacostraca, Diplopoda, Chilopoda and Gastropoda). Members of the class Insecta are identified by family, genus or species.

After determining the samples, a list of soybean soil fauna was compiled. After determining the number of soil fauna, their coenological characteristics, dominance and frequency were also determined.

Dominance is used to express the percentage of an order/family/genus/species in the total number of insects in a particular biotope. The Balogh formula was used to calculate dominance (cited in Balarin [32]):

$$\mathbf{D} = (\mathbf{n} \mathbf{A} / \mathbf{N}) \times 100\tag{1}$$

D—dominance index; nA—the number of individuals caught of the same species/genus/ order; N—the total number of individuals caught.

Based on the calculated dominance, the orders are classified into the following groups according to Tischler and Heydeman (cit. Balarin [29]) as eudominant (>10%); dominant (5–10%); subdominant (1.00–4.99%); recedent (0.5–0.99%); subrecedent (0.01–0.49%).

Frequency shows the exact number in which an order/family/genus/species appears on a surface within a biotope. The Balogh formula was used to calculate the frequency [32]:

$$\mathbf{C} \mathbf{a}\_{i} = \mathbf{U} \mathbf{a}\_{i} / \Sigma \mathbf{U}\_{i} \times \mathbf{10} \tag{2}$$

Cai—frequency index; Uai—number of samples with order found; Σ Ui—total number. According to Tischler (Balarin 1974), the obtained frequency results are divided into the following groups: euconstant (75–100%); constant (50–75%); accessory (25–50%); accidental (0.1–25%).

The data on the number of individuals belonging to different orders/classes captured in each field were analysed by analysis of variance (ANOVA) using the AOV factorial method with three factors using ARM 9 software software (Gylling Data Management, Brookings, South Dakota) [33]. The first factor was pre-sowing intervention, which was considered as a fixed factor. The second factor was sampling period and the third factor was statistical class. A Tukey post hoc test was used to determine which mean values of the variants were significantly different after a significant test result (*p* < 0.05).

In order to compare species richness among different treatments, the Shannon index (H) [34] was calculated based on the total collected individuals of different classes for each pre-sowing activity. The Shannon entropy quantifies the uncertainty (entropy or degree of surprise) associated with this prediction. It was calculated as follows:

$$\text{Shannon Index } (H) = \sum\_{i=1}^{s} pi \ln p i \tag{3}$$

In the Shannon index, *p* is the proportion (n/N) of individuals of one particular species found (n) divided by the total number of individuals found (N), ln is the natural log, Σ is the sum of the calculations, and *s* is the number of species.

Shannon's equitability (E H) has been calculated by dividing H by Hmax (here Hmax = lnS) [35]. Equitability assumes a value between 0 and 1 with 1 being complete evenness.

The data on the number of individuals belonging to family Carabidae captured in each field were analysed by analysis of variance (ANOVA) using the AOV factorial method with two factors using ARM 9 software [33]. The first factor was pre-sowing intervention, which was considered as a fixed factor. The second factor was sampling period. To normalise the data, square root transformation of X + 0.5 has been applied. A Tukey post hoc test was used to determine which mean values of the variants were significantly different after a significant test result (*p* < 0.05).

#### **3. Results and Discussion**

#### *3.1. Soybean Fauna Diversity*

Table 2 shows the number and composition of soil fauna of soybean collected in April, June and September 2019. A total of 7836 individuals were collected. The Insecta class

was the most numerous with 4373 individuals, within which eight orders were identified. The most numerous order was Coleoptera with 2698 members, which accounted for 34% of the total soybean soil fauna collected. The study identified 807 individuals from the order Hymenoptera, which accounted for 10% of the total soybean soil fauna collected, especially members of the family Formicidae (712 individuals, 9.1%). In addition, there were 466 members of the order Diptera (6%), 241 members of the order Collembola (3%), 72 members of the order Orthoptera (1%), 2 members of the order Mecoptera (0.03%), and 1 member of the order Lepidoptera (0.01%). In addition, 86 individuals from the order Hemiptera were identified, representing only 1% of the total fauna collected. In the study of soybean fauna by Bažok et al. [36], the most numerous order was Hemiptera with 818 individuals, which accounted for 60.3% of the total fauna collected. However, their results show the composition of the fauna on the plant canopy. In addition to the class Insecta, this study also identified 3111 individuals from the class Arachnida, which accounted for 40% of the total soil fauna. Other classes were much less represented in the total catches.

**Table 2.** The number and composition of the soybean soil-dwelling fauna.

**Table 2.** *Cont.*

**Table 2.** *Cont.*

The composition of soil fauna, according to the influence of organisms on agriculture, consists of 6601 members of beneficial fauna, which is 84% of the total fauna collected, 603 members were pests, which is 8% of the total fauna collected, and 632 members were indifferent fauna, which is 8% of the total fauna collected. In our study, out of the total beneficial fauna which comprised 6601 individuals, 3111 individuals were spiders, which is 47% of the total beneficial fauna collected. This confirms the findings of Costello and Daane [37], Pearce et al. [38] and Pajaˇc Živkovi´c et al. [39], in which they stated that spiders are among the most abundant predators in the soil layer and in large numbers can play an important role in reducing the pest population.

#### *3.2. Dominance and Frequency of Collected Fauna*

After determination, the parameters of dominance and frequency of classes and orders per total number of collected soil fauna were calculated. According to the dominance index, the classes Arachnida and Insecta (and individually the orders Hymenoptera and Coleoptera) were classified as eudominant. The results show that the class Insecta accounted for 56% and the class Arachnida for 40% of the total number of fauna collected. Within the class Insecta, the order Coleoptera accounts for 34% of the total number of fauna collected and the order Hymenoptera accounts for only 10% of the total number of fauna collected. The order Diptera is classified as the dominant order and accounts for 6% of the total number of fauna collected, and the order Collembola as the sub-dominant order accounts for only 3% of the total number of fauna collected. The frequency index of these orders over all the samples shows that the orders Coleoptera (100%), Hymenoptera (89%), Diptera (92%) and the class Arachnida (100%) belong to the category of euconstants, with their frequency index occurring in more than 75% of the samples. The order Collembola (25%) belongs to the category of accessory orders, occurring in 25% of the samples.

When the same parameters were analysed by collection period, the dominance index of the members of the order Coleoptera was 34%, 58% and 21%, respectively; it was classified as the eudominant order in all three collection periods. However, the results showed that its dominance increased in June and decreased in September. Members of the class Arachnida were also classified as a eudominant order throughout the collection period, with a dominance index of 42% (April), 20% (June) and 32% (September). The results show that their index decreased in June and increased in September, which was the opposite to the members of the order Coleoptera. The order Diptera is classified as a eudominant order in April with a dominance index of 11%, while in June and September it is classified as a dominant order with a dominance index of 8% and 10%, respectively. Members of the order Hymenoptera increased in each sampling period and were classified as a subdominant order with a dominance index of 5% in April, dominant order with a dominance index of 6% in June and eudominant order with a dominance index of 23% in September. Members of the order Hemiptera also increased in their dominance index over

the study period. In April, it was classified as a recurrent order with a dominance index of 1%, and in June and September it was classified as a subdominant order with dominance indices of 1% and 4%, respectively. The order Isopoda was subdominant in April with a dominance index of 3%, then fell to a recedent order in June with a dominance index of 1%, and rose to a dominant order in September with a dominance index of 5%.

The class Arachnida (100%) and, within the class Insecta, the order Coleoptera (100%) are classified as euconstant throughout the sampling period and occur in all samples collected. The order Diptera is also classified as euconstant throughout April and September and was found in every sample collected, while in June it occurs in 75% of the samples. The order Hymenoptera is found in all the collected samples in April and September and is classified as a euconstant order, while in June it is found in 67% and is classified as a constant order. Members of the order Hemiptera were present in all collected samples in April and classified as a euconstant order, in June they were classified as an accessory order and were present in 33% of collected samples, while in September they were again classified as a euconstant order and were present in 83% of collected samples. The order Isopoda was found in 67% of the collected samples in April, in 50% of the collected samples in September and was classified as a constant order, and in June it was found as an accessory order in only 17% of the collected samples.

#### *3.3. Influence of Pre-Sowing Tillage on Soil Fauna Abundance*

The total number of catches from the same pre-seeding measures was tested mutually and the *p* values ranged from 0.158978 to 0.687678, which means that the same pre-seeding measure had no influence on the abundance of fauna on the tested sites in the Šašinovec area. Therefore, in the further results, we present summarised data on collected soybean fauna per measure before sowing.

Table 3 shows the results of ANOVA between the total number of catches of all soildwelling fauna on the studied variants throughout the survey period. The results show that catches were extremely high in April and that they decreased during the summer and autumn months. In April, significantly more members of the fauna were found in fields with glyphosate and in fields where mulching was carried out. The lowest abundance of fauna was found in fields with cover crops prior to seeding. In June, up to 10 times lower catches of fauna were found, and significantly the highest catches were found in fields where ploughing was carried out before sowing, and the lowest catches were found in fields with mulching, glyphosate and standard tillage. In September, catches were even lower and no differences were found between the studied variants.

**Table 3.** Total catches of soil fauna ( ±standard error: SE) on all variants throughout the research period.

\* values marked with the same letter (a–c) do not differ significantly (*p* > 0.05; HSD test); ns—non significant; \*\* HSD was determined by comparing the total abundance of fauna between different methods of pre-sowing tillage in all periods of research. Equality of variances was tested with Levene's test and reaches with equal variances (*p* > 0.05); 't = Mean descriptions are reported in transformed data units and are not de-transformed (data were log (x + 1) transformed and arcsin trans-formed √x).

In April, a total of 6838 individuals of soil fauna were identified, representing 87% of the total catch. In June, 508 individuals of fauna were identified (6%), and in September, 490 individuals (6%) (Table 4).

Soybean soil-dwelling fauna is many times more numerous in the spring months (the onset of soybean vegetation), while it drastically decreases later. Nait-Kaci et al. [40] claim that a large difference in research results during the year is due to the sensitivity of terrestrial fauna to climatic conditions, especially heat and humidity, and the influence of vegetation cover. There are numerous studies that also find the highest abundance of fauna in spring months [41], which is probably due to more favourable climatic conditions (more humidity, lower temperatures). Gkisakis et al. [42] conducted a study on soil fauna in common and hilly olive groves and the results showed the highest number of individuals in spring, while in summer and autumn the number decreases. Goncalves et al. [43] in their study on soil fauna in olive groves found that most of the soil fauna was collected in spring. House and All [44] in their study also found the highest numbers of members of the order Coleoptera in pitfall traps in mid-spring in soybean cultivation.

In April, most fauna was collected in the field where mulching was performed and in the field where glyphosate was applied. The percentage of fauna in both fields was 22%. Fields with standard tillage had 19% of the fauna, while fields that were ploughed had 15% of the total fauna. Fields with undermining had 12% of the total fauna. Fields with cover crops had the least amount of fauna, only 10%. In June, the most fauna was collected in the field with undermining and the field with ploughing, with percentages of total fauna of 25% and 23%, respectively. In the fields with cover crops, the proportion of fauna was 17%. In the fields with mulching, the proportion was 14%, and in the fields with glyphosate application, the proportion was 12% of the total catches. The lowest proportion of fauna was found in the fields with standard tillage, only 10% of the total catch. In September, the most fauna was collected in fields with standard tillage and glyphosate application, 22% and 21%, respectively. In fields with cover crops, the percentage of fauna was 19%. Fields with mulching and undermining had similar percentages, 15 and 14%, respectively. The lowest percentage of fauna was found in the field with ploughing, only 9% of the total catch. As reported by several authors, the associated conserved management systems contribute to the optimal development of soil fauna, besides the high relationship with soil fertility due to increased biological activity [45–47]. In contrast, the no-till measures showed a lower occurrence and diversity of soil organisms. Therefore, the conserved soil management should not be recommended when the objective is to benefit and to preserve soil biodiversity, regardless of the type of soil tillage and management.

The total catches of various members of the soybean soil fauna classified into orders/classes were analysed during the study period to determine differences among fields within each taxonomic category with respect to the sampling period. The detailed analysis of the number of individuals from different statistical categories collected in April, June and September on different tillage systems is presented in Tables 5–7.

There were no significant differences in the number of individuals captured in April between different tillage systems in the orders Orthoptera, Hemiptera, and the classes Arachnida and Diplopoda. The differences were found in the orders Colembolla, Coleoptera,

Hymenoptera, Diptera and the class Malacostraca. Among the fields with different tillage methods, significantly higher catch of individuals of order Coleoptera was recorded in the fields with mulching. In fields with ploughing, a significantly higher number of members of order Hymenoptera was recorded. In the fields with glyphosate, significantly higher catches of individuals of the orders Colembolla and the class Malacostraca were recorded. However, the catches of individuals of the classes Malacostraca were very low. Members of the class Arachnida were caught in high numbers in all fields, but due to high variability in catches, differences between fields with different tillage practices were not detected.

**Table 5.** Total soil fauna analysed by order/class (±SE) collected in April on different tillage systems before sowing.

¥values marked with the same letter (a–c) do not differ significantly (*p* > 0.05; HSD test); ns—non significant value; \* significant value; \*\* HSD was determined by comparing the numbers of each group of insects between different methods of pre-sowing tillage; Equality of variances was tested with Levene's test and reaches with equal variances (*p* > 0.05); 't = Mean descriptions are reported in transformed data units and are not de-transformed (data were log (x + 1) transformed and arcsin transformed √x).

> The results in Table 6 show that there were no significant differences in catches between the variants in the orders Collembola, Orthoptera, Hemiptera, Hymenoptera, Diptera and the class Diplopoda in June. The only differences in catches were found in the order Coleoptera and the classes Arachnida and Malacostraca. Members of the order Coleoptera were significantly more abundant in fields with ploughing and undermining. Catches in other fields were low and did not differ significantly. Members of the class Arachnida were caught in fields with cover crops, ploughing and undermining. There were no significant catches in other fields. The Malacostraca class had the significantly highest catch in fields with glyphosate, but it should be noted that these catches were very small.

¥

**Table 6.** Total soil fauna analysed by order/class (±SE) collected in June on different tillage systems before sowing.

values marked with the same letter (a,b) do not differ significantly (*p* > 0.05; HSD test); ns—non significant value; \* significant value; \*\* HSD was determined by comparing the numbers of each group of insects between different methods of pre-sowing tillage; Equality of variances was tested with Levene's test and reaches with equal variances (*p* > 0.05); 't = Mean descriptions are reported in transformed data units and are not de-transformed (data were log (x + 1) transformed and arcsin transformed √x).

> The results in Table 7 show that in September the number of members of the orders Collembola, Orthoptera, Hemiptera, Coleoptera, Hymenoptera, Diptera and the classes Malacostraca and Chilopoda did not differ significantly among the variants of the study. Only in the class Arachnida were differences in catches found between the variants studied. The number of members of the class Arachnida was significantly higher in fields with glyphosate and slightly lower in fields with cover crops, mulching and standard tillage. Statistically, the lowest catches were found in fields where ploughing and undermining were used.

**Table 7.** Total soil fauna analysed by order/class (±SE) collected in September on different tillage systems before sowing.

¥ values marked with the same letter (a,b) do not differ significantly (*p* > 0.05; HSD test); ns-non significant value; \* significant value; \*\* HSD was determined by comparing the numbers of each group of insects between different methods of pre-sowing tillage; Equality of variances was tested with Levene's test and reaches with equal variances (*p* > 0.05); 't = Mean descriptions are reported in transformed data units and are not de-transformed (data were log (x + 1) transformed and arcsin transformed √x).

> As shown in our results, beetles (Coleoptera: 2709) and spiders (Arachnida: 3072) are the most important members of the soil-dwelling fauna and, contrary to the statements of Wardle [48] that they are greatly reduced by tillage, we found that these two groups are

much more influenced by weather conditions (high abundance in spring, low in autumn) than by tillage. The results of the factorial analysis (Table 8) provide additional support for our conclusions. The number of individuals collected was significantly (*p* > 0.05%) influenced by the pre-sowing treatment (HSD = 1.28) and by the period of sampling (HSD = 0.74), as well as by the order/class of individuals recorded (HSD = 1.71), proving that the pre-sowing treatment (i.e., the type of tillage) is responsible for the number of different orders/classes of soil-dwelling fauna in soybean. The sampling period also influences the captures as well as the interaction between pre-sowing intervention and sampling date. The significant interaction (*p* > 0.05%) was present between all the three factors (pre-sowing intervention, sampling period and order/class of individuals recorded) for the number of individuals recorded.

**Table 8.** Factorial analysis of the total capture of different orders/classes of soil fauna.

df—degrees of freedom; *p*—probability value; HSD—honestly significant difference.

#### *3.4. Influence of Pre-Sowing Tillage on Soil Fauna Species Richness*

The diversity in six different pre-sowing treatments, calculated according to the Shannon diversity index (H) and according to Shannon's equitability (EH), is shown in Table 9. We can see from our results that the diversity and evenness in the fields from the standard pre-sowing treatment are much lower than in the fields from the treatments that disturb the soil less, such as cover crops, ploughing, undermining and mulching. At the same time, the difference in diversity and evenness between the standard treatment and the treatment with glyphosate is somewhat smaller. In the fields where the activities are less intensive, not only is there a greater number of species, but the individuals in the community are more evenly distributed among these species. In the fields with standard pre-sowing activities, there are 50 species, but the class Arachnidae accounts for 46% of the community and Staphylinidae, *Carabus tibiscianus* and Formicidae account for the other 22% of the community.

**Table 9.** Shannon diversity index (H) and Shannon's equitability (EH) of collected fauna in different pre-sowing treatments.

Our results confirm an earlier study by Baretta et al. [49], in which it was shown that the members of Collembola, Araneae, Hymenoptera, Orthoptera, Grylloblattodea, Lepidoptera and the total abundance of soil fauna were related not only to specific tillage systems but also to weather conditions at the time of sampling. The same authors noted that no-till has a higher amount of organic matter in the surface layers and a higher moisture

status of the soil, which promotes the formation of a suitable environment for a greater abundance and diversity of edaphic groups, especially Coleoptera and Isopoda [49]. This was partially confirmed in our study, especially in the spring sampling, where the greatest faunal diversity was found in variants with mulching and glyphosate treatments, which are without intervention in the soil layers.

#### *3.5. Influence of Pre-Sowing Treatments on the Carabid Population*

Of all the fauna recorded, the family Carabidae has received special attention because of its importance as predators of numerous pest species [50–52] and as indicators of anthropogenic impacts and agroecosystem quality [22,52–58]. Members of the family Carabidae accounted for 34% of the total fauna collected. A total of 21 species of carabids were identified in this study, with *Carabus cancellatus tibiscinus* (441 individuals), *Poecilus cupreus* (383 individuals), *Brachinus psophia* (181 individuals) and *Harpalus distinguindes* (105 individuals) standing out in numbers. Lemic et al. [57] reported a similar carabid community (26 species with 15 genera) in an intensively managed agricultural production. Carabids are considered as one of the most important natural enemies in soil and subsoil layers [59]. They are also used in numerous studies as bioindicators of climate change and the effects of agrochemicals on their habitats, and their abundance can indicate the level of pollution in an area [12,60–68].

Depending on the pre-sowing interventions (Figure 1), the abundance of carabids in April was the highest in fields where mulching was carried out (363 individuals: 22%), followed by fields with glyphosate application (317 individuals: 20%); fields with standard tillage (241 individuals: 15%); fields with undermining (183 individuals: 11%); ploughing (161 individuals: 10%); and the lowest abundance was observed in fields with cover crops (121 individuals: 8%). In June, the total number of catches was much lower and the highest number of carabids was observed in the fields with ploughing (only 56 individuals, 4%), while the number was even lower in the other variants. In September, the abundance of carabids was very low, with a maximum catch of 10 individuals identified in the field where glyphosate was applied.

**Figure 1.** Number of members of the family Carabidae captured on fields with different pre-sowing interventions in the fields with different pre-sowing activities (values marked with the same letter do not differ significantly (*p* > 0.05; HSD = 2.01)).

Overall, the abundance of carabids was lowest in fields where ploughing and undermining occurred before soybeans were sown. This result is confirmed by the studies of Kromp (1999) [50] and Holland and Reynolds [60], who found that ploughing negatively affected the abundance of Carabidae. Numerous previous studies observed higher carabid catch rates in fields with reduced or no tillage compared to conventionally tilled fields [58,69–74].

Catches of collected individuals of the Carabidae family were significantly (*p* > 0.05%) affected by pre-sowing treatment (HSD = 1.87) and sampling period (HSD = 0.47) (Table 10). Pre-sowing treatment (i.e., tillage type) is responsible for the catch of Carabidae family members in soybean. At the same time, the highest catches were recorded in April, so the sampling period also has an effect on catch, as does the interaction between pre-sowing treatment and sampling date (HSD = 2.01). The highest abundance of ground beetles in Poland is in early spring (May) [64], which is consistent with our results of highest catches in April due to the shift in climatic conditions. Drmi´c et al. [75] found that endogenous ground beetle species are active throughout the growing season, which is probably due to the more stable conditions in the lower soil layers.

**Table 10.** Factorial analysis of the total capture of members of the family Carabidae.

Overall, the recorded members of the family Carabidae belong to 18 species and three genera, although individuals of the genera *Amara* sp., *Bembidion* sp. and *Harpalus* sp. were not identified to species level. The species richness of Carabidae was studied in Croatia in fields with maize [76,77], barley [78], sugar beet [79], rapeseed [80] and winter wheat [70], as well as in intensively cultivated fields [57]. The number of established species in their studies varied from eight [79] to 72 [80]. The reported studies focused on the dependence of faunal composition on different regions [76,77], crops [78,79], different cropping methods [78–80] and/or tillage practices [57]. In our study, the most numerous species were *Carabus cancellatus tibiscinus*, *Poecilus cupreus* and *Brachinus psophia*. *Poecilus cupreus* and *Brachinus psophia* are also mentioned as important and numerous species by other authors in their studies.

With this study, we obtained the results on the effects of pre-sowing interventions on the soil fauna of soybean, where climatic conditions and sampling time had an influence on the number and composition of the fauna studied. Multi-year studies are needed to obtain clearer data on the effects of treatment and other treatments (pesticides) on soil fauna abundance and composition. It appears that a reduction in mechanical activities in the shallow seed layer of the soil has a positive effect on species richness or biodiversity. Particularly noteworthy is the large proportion of natural enemies that currently colonise the study area, characterising the soil richness and stable natural equilibrium.


**6. Conclusions**

The potential for reusing natural by-products and wastes from different sources was reviewed in this chapter, describing their most attractive properties and

characteristics. The most recent innovations and developments in this area were listed and presented, showing a novel possible way for the development of technical and functional textiles. The main potential applications for the valorization of whey protein by the production of textile fibres have been described, as well as by its application as a textile finish. The different applications already tested and the main products already available on the market for sustainable alternatives to produce genuine leather were also listed. Although these types of applications at an industrial level have some limitations, as cost-effectiveness ratio, permanence of the intrinsic properties of the substrates and durability to the laundering, for example, the reuse of these natural by-products and wastes as a bio-resource in the demanding textile sector presents itself as an attractive alternative.


*Edited by Dean F. Martin and Barbara B. Martin*

This book is characterized by three important features. The authors represent an impressive collection of international workers from Brazil, China, Egypt, Poland, Turkey, and the United States. The majority of the chapters reflect the importance of collaborative efforts in contemporary research. Finally, some chapters are especially useful because of the experimental details that are provided. And it is to be hoped that readers will find that the chapters are both informative and inspirational.

Column Chromatography

Column Chromatography

*Edited by Dean F. Martin and Barbara B. Martin*

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Turnover of the different chimeras was studied in the presence of the substrates erythromycin and testosterone using NADPH as electron source, and the results are reported in **Table 2**. Quantification of both the amount of product formed in the N-demethylation of erythromycin and coupling efficiency, determined from the ratio product formed/NADPH oxidized, were performed for the three chimeras. Erythromycin Ndemethylation rates were calculated at increasing concentration of the substrate in the presence of all the 3A4-BMR constructs, and the experimental data were fitted to the Michaelis-Menten model in order to calculate kinetic parameters (**Figure 5A**). The KM-values of 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR were 20.8 ± 3.6, 27.4 ± 4.0, and 21.3 ± 4.4µM respectively. In this case no statistically relevant differences were found. These results show that the glycine linker length does not influence 3A4-BMR affinity for erythromycin substrate, indicating that the binding pockets of the heme domain of the chimeras are not perturbed.

Further analysis in terms of turnover rate led to Vmax-values of 0.0515 ±.0004, 0.1177 ± 0.0006, and 0.1387 ± 0.0009 nmol min−1 for 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR (**Figure 4B**), respectively. In this case, a statistically significant increase in Vmax for 3A4-3GLY-BMR and 3A4-5GLY-BMR with respect to the 3A4-BMR construct was observed. This shows that the glycine linker increases the enzyme turnover rate in the presence of erythromycin substrate. Moreover, a significant increase in Vmax-value was found for 3A4-5GLY-BMR compared to 3A4-3GLY-BMR.

Also the testosterone 6β-hydroxylation rates were calculated at increasing concentrations of the substrate in the presence of all the 3A4-BMR constructs, and the results were fitted to the Michaelis-Menten model in order to calculate kinetic parameters (**Figure 5D**). The KM-values obtained from fitting the Michaelis-Menten curves of 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR were 19.6 ± 2.6, 21.3 ± 2.7, and 19.8 ± 2.2µM respectively, indicating again no statistically relevant differences in the binding pocket. The Vmax-values were 0.0066 ± 0.0002, 0.0089 ± 0.0002, and 0.0113 ± 0.0003 nmol min−1 respectively for 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR enzymes (**Figure 5E**). As observed in the presence of erythromycin, when comparing Vmax results, a statistically significant increase was found for 3A4-3GLY-BMR and 3A4-5GLY-BMR with respect to the 3A4- BMR construct. Also in this case the glycine linker increases the enzyme turnover rates, with a significant higher Vmax-value for 3A4-5GLY-BMR compared to 3A4-3GLY-BMR.

The coupling efficiency percentage during erythromycin Ndemethylation, calculated as ratio of HCHO formed and NADPH consumed, were found to be 9.4 ± 0.5, 10.0 ± 0.8, and 12.5 ± 0.9% for 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR, respectively (**Figure 5C**). When performing statistical analysis of these results, the 3A4-5GLY-BMR fusion protein was found more coupled than the other two constructs, whereas no significant difference was found when comparing 3A4-BMR and 3A4-3GLY-BMR. A similar comparison was also performed for testosterone catalysis by calculating coupling efficiency as the ratio of the amount of 6β-hydroxytestosterone formed and NADPH consumed. The calculated coupling efficiency percentage ratios were found to be 3.4 ± 0.1, 4.6 ± 0.1, and 5.8 ± 0.2% for 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR, respectively (**Figure 5F**).

TABLE 2 | Catalysis and uncoupling parameters for erythromycin N-demethylation and testosterone 6β-hydroxylation for 3A4-BMR, 3A4-3GLY-BMR, and 3A4-5GLY-BMR.

Coupling parameters are determined at saturating erythromycin (120 µM) and testosterone(150µM) concentrations. Values are means ± standard deviation of four experiments. (a)The coupling efficiency is calculated as: HCHO × 100/NADPH consumed.

(b)The coupling efficiency is calculated as 6β-hydroxytestosterone × 100/NADPH consumed.

#### NADPH–Cytochrome c Reduction by 3A4-BMR Chimeras

The results from enzymatic activity, coupling and reduction potentials indicate that the different loops affect the interaction between the BMR and 3A4 domains of the chimeras, probably due to the different degrees of length and flexibility associated with them. Cytochrome c was therefore used as external probe to check its ability to intercept the electrons of NADPH delivered by the BMR when this is exposed and hence available to interact with proteins other than the 3A4 domain. The ability of cytochrome c to be reduced by NADPH via free cytochrome P450 reductase has already been described in the literature (Guengerich et al., 2009).

The capability of 3A4-BMR chimeras to use NADPH electrons to reduce cytochrome c is strictly related to cytochrome c ability to interact directly with the BMR domain when this is exposed. In order to explore the effect of viscosity on the interaction of cytochrome c with the chimera BMR domain, we performed the experiments both in the absence and in the presence of 20% glycerol. The reduction rates were calculated as nmoles of reduced cytochrome c per min and the results are summarized in the **Figure 5**.

The 3A4-5GLY-BMR chimera shows the highest cytochrome c reduction rate compared to the other two proteins. Moreover, the presence of 20% glycerol significantly impaired cytochrome c reduction ability of all the three constructs, with the 3A4-5GLY-BMR being the most affected one.

#### DISCUSSION

Cytochromes P450 have been extensively used in our laboratories to investigate their potential for applications in biocatalysis as electron transfer domains in chimeric constructs (Cunha et al., 1999; Sadeghi et al., 2011; Sadeghi and Gilardi, 2013). This work investigates how the stability and activity of selfsufficient chimeric human enzymes can be optimized not only by creating fusion proteins but also with the correct choice of loop connecting the reductase and the catalytic domains.

The stability of the P450 catalytic domain is significantly improved in the presence of the BMR domain since all the three fusion proteins showed a 1.5–2 fold lower rate of P450 loss compared to P450 3A4. In this case, the presence of an extra domain rather than the length of the loop is essential for stability as no significant differences are observed among the three proteins. However, the length of the loop has an impact on other parameters, such as NADPH oxidation rates and reduction potential. In particular, the chimeric proteins show a higer NADPH consumption rate compared to the CYP3A4 hCPR system at a 1:1 molar ratio. Moreover, the 3A4-3GLY-BMR protein shows a 2-folds faster rate in NADPH consumption with respect to the other two constructs. This effect suggests that the 3GLY construct is more prone to accept electrons from the redox partner with respect to no GLY and 5GLY leading to a faster reduction of the heme-Fe3+ to heme-Fe2+ (Sligar et al., 1979). These data show that there is a critical length of the linker between the domains that can modulate the inter-domain interactions that determine the rate of the electron transfer.

The fastest oxidation rate in the 3GLY protein is explained by its more positive reduction potential compared to the other two fusion proteins. It is known that in redox proteins, the interaction with the redox partner can influence the conformation, the reduction potential and in general, the dielectric environment of the redox cofactor (Bendall, 1996). Here, the three fusion proteins show a different, although negative, reduction potential that can reflect a different dielectric heme environment triggered by a different domain arrangement and interaction.

When investigating the catalytic domain ability to use reducing equivalents for erythromycin N-demethylation and testosterone 6β-hydroxylation during the catalytic event, the 5GLY construct resulted the best for both turnover rate and coupling efficiency. Thus, the increase in turnover number and coupling efficiency is neither directly related to an improvement in electron transfer rate nor to a more positive heme iron reduction potential. Differences in substrate binding can also be excluded as the KM-values measured for both erythromycin and testosterone substrates are similar for the three proteins and within the range of previously published data (Riley and Howbrook, 1998; Yuan et al., 2002). Moreover, these data are consistent with previously published results in which it was demonstrated that the use of artificial redox chains allows the regulation of the electron flow to the P450 catalytic domain through the mediation of the non-physiological reductase domain by lowering the electron transfer rate and improving the correct electrochemical inter-domain tuning thus enhancing coupling efficiency and catalytic activity (Dodhia et al., 2008; Degregorio et al., 2011; Castrignanò et al., 2014).

In order to investigate if there is a correlation between the observed improved turnover/coupling efficiency and an increased inter-domain flexibility in the 5GLY protein, cytochrome c was used as external probe to check its ability to

intercept the electrons of NADPH delivered by the BMR when this is exposed and hence available to interact with proteins other than the 3A4 domain. The 5GLY resulted in the fastest in cytochrome c reduction and this result can be explained by the highest flexibility between the reductase and catalytic domain of the chimera allowing a better interaction with cytochrome c and an easier electron delivery from BMR reductase domain (**Figure 6**). This interpretation is further confirmed by the fact that the impairment of inter-domain flexibility upon glycerol addition that increases the medium viscosity, causes a significant decrease in the cytochrome c reduction rate in all the three constructs, with a more marked effect on the more flexible for 5GLY loop.

Taken together, the erythromycin N-demethylation and testosterone 6β-hydroxylation activity and the coupling results show that the 3A4-5GLY-BMR chimera is the more catalytically efficient and the more coupled. This result suggests that the higher linker flexibility allows a more proficient arrangement of catalytic and reductase domains and improves the tuning of the electron transfer to the active site leading to a more accurate electrons delivery from the NADPH electron source during the catalytic event (**Figure 7**).

glycerol; #P < 0.001 vs. 3A4 BMR and 3GLY(two way ANOVA followed by Student-Newman-Keuls post hoc test).

# CONCLUSIONS

The creation of chimeric proteins was shown before to improve the solubility of P450 3A4 and to generate catalytically selfsufficient enzymes (Degregorio et al., 2011). Here, the P450 3A4-BMR catalytic performance is improved in terms of coupling efficiency and enzyme turnover by engineering the loop connecting the P450 3A4 and the BMR modules. The glycine linker acts as a flexible hinge that can control electron flow rate, catalytic activity and coupling efficiency of the 3A4-BMR chimeras. Data in this work highlight that the modulation of the length of the linker connecting the modules of a multidomain protein has an impact on electron transfer, catalytic ability and coupling efficiency of the system that can be ascribed to the modulation of inter-domain flexibility and interactivity rather than to a different tuning of the electron transfer process. These parameters can be considered as the major requirement for an optimization of the electron delivery to the heme iron center showing that the design of the linker hinge between the interacting domains plays a crucial role in the optimization of reductase domain improvement of P450 catalytic efficiency. Our results suggest that for the 3A4-BMR fusion proteins studied here, the electron transfer may be achieved through the direct flow between the reductase and the heme domain rather than through "inter subunits" interaction between

protomers in a dimeric system as suggested for the P450 BM3 system.

This molecular design approach can be successfully developed for the obtainment of performing enzymatic paradigms suitable for biotechnological applications. The next step is the introduction of such catalysts in bacterial cells for the large-scale production of drug metabolites using whole cells as a less expensive system.

#### REFERENCES

#### AUTHOR CONTRIBUTIONS

DD performed the catalysis experiments on Erythromycin. SD performed the catalysis experiments on Testosterone. SC performed the uncoupling experiments, redox potential determination and cyt c interaction. GDN and GC performed the modeling of the chimeras. SS and GG interpreted the data and wrote the manuscript.

and ibuprofen. Arch. Biochem. Biophys. S0003–S9861, 30116–30118. doi: 10.1016/j.abb.2015.12.005

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Degregorio, D'Avino, Castrignanò, Di Nardo, Sadeghi, Catucci and Gilardi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.