**5. Methods**

Three hypotheses were formulated:

The first hypothesis was conducted for the evaluation of tremor simulation; the other two hypotheses involved human subjects. We conducted the experiments in laboratory conditions, on healthy and Parkinson subjects. Physicians of Parkinson patients reported to us that the PD patients also manifest action tremor. The facilities that enabled us to conduct these experiments were involved in the development of the Rehapiano. We have complied with all legal and legislative conditions, including GDPR patient protection. Each patient was personally acquainted with the use of their data from the device and signed an agreement permitting processing the medical data.


*5.1. Experiment Description*

5.1.1. Dataset

A dataset of vibration dampers for overhead transmission lines is required for the proposed theoretical validation and experimental analysis. At present, although there is a lot of research work on vibration dampers, but there is no completely public vibration damper detection dataset. Moreover, most of the vibration damper data in the article were obtained by geometric transformation methods such as flipping, cutting, and scaling. An insufficient number of vibration dampers would make it difficult to verify the correctness of the proposed theory. Therefore, a dataset was made for vibration damper detection based on the real UAV cruise video of overhead transmission lines, and named DamperDetSet. In the process of making the DamperDetSet dataset, LabelMe was used as a data labeling tool to label the positions of all existing line vibration dampers in the original image. The callout box was kept as close as possible to the smallest enclosing rectangle of the target area.

DamperDetSet contains a total of 3000 images, each of which contains vibration dampers, and the types of vibration dampers are not unique, such as hippocampus antislip vibration dampers, hook wire vibration dampers, etc. We randomly divided all 3000 images into a training set and a test set. The training set contains 2500 images and the test set contains 500 images. The ratio of training set and test set is 5:1. In addition, as the dataset is obtained by shooting with UAVs, the presentation angle of the vibration damper in the image is not unique, which also puts forward higher requirements for the robustness of the model.

#### 5.1.2. Experiment Configuration

In terms of hyperparameter settings in the experiment, we trained DamperYOLO for a total of 200 epochs. The learning rate of the first 100 epochs remained unchanged, and the learning rate of the last 100 epochs gradually decreased to 0. In terms of experimental software settings, all our programs were written in Python language and integrated based on the Pytorch 1.4 platform. In the system environment of the experimental platform, Ubuntu18.04 was used as the operating system. In terms of the hardware environment of the experimental platform, an NVIDIA RTX 2080 GPU was used as the main equipment for training calculation, matched with an AMD R5-3600X CPU and 32 GB RAM.

#### *5.2. The Baselines*

In the following experiments, we chose one-stage, two-stage, and anchor-free methods as comparison methods.

YOLOv4 [37]: This method is the latest achievement of the YOLO series. After continuing the advantages of the previous work, it introduces the structure of FPN + PAN, which improves the transferability of features in the network; it is also the basis of our proposed model.

Cascade R-CNN [49]: This framework is the latest achievement of the R-CNN series. It creatively introduces a cascade structure. The detection accuracy is state-of-the-art, but its excellent performance consumes a lot of computational resources.

CenterNet [50]: This method is a heatmap-based detection method rather than anchorbased, which has the advantage of fast testing and low space occupancy.

SSD [47]: SSD is another classic one-stage object detection method. It initially utilizes multiple detectors.

RetinaNet [51]: RetinaNet is based on FPN [43], and its contribution is to propose focal loss to solve the problem of category imbalance.

#### *5.3. Qualitative Evaluation*

To visually compare the difference between the detection effect of DamperYOLO and other baselines, we conducted qualitative analysis and comparison experiments based on the DamperDetSet dataset. The experimental results are shown in Figure 5. As can be seen from Figure 5, under the same test image, the detection effect of CenterNet is not stable enough. This proves that calculation of the heat map will be greatly disturbed by the current anchor-free algorithm in the face of complex scenes such as transmission lines. The performance of the two-stage Cascade R-CNN is very superior. As the latest framework of the R-CNNs series, the results obtained by the second iteration based on the proposal are more accurate. There is also room for improvement in the performance of a single-level SSD. And using VGG16 as a backbone may be weaker than the ResNet-like feature extraction network in feature extraction. RetinaNet and YOLOv4 perform better. Both of them benefit from the latest research results in one-stage. They can obtain high performance with only one calculation, but the edge detection effect of vibration damper still needs to be improved. Finally, DamperYOLO outperforms other one-stages. The detection result image of DamperYOLO proves that our proposed improvement strategy is effective, and its performance is no less than that of Cascade R-CNN.

**Figure 5.** Test examples of each model on the DamperDetSet dataset. Experimental results show that the performance of DamperYOLO is similar to Cascade R-CNN, better than SSD, RetinaNet and YOLOv4 in one-stage class, and CenterNet.

#### *5.4. Quantitative Evaluation*

We compared other baselines and performed the quantitative analysis shown in Table 2 with AP in the COCO [52] dataset as the evaluation standard. The calculation of AP is based on the ground truth and the IOU of the prediction result. The calculation formula is shown in Equation (3). The AP calculation results were selected when the IOU was 0.5, 0.7, and 0.9 as the evaluation basis, so that the performance difference of the model under different pressure levels could be more comprehensively evaluated.

**Table 2.** APs of the different models.

As can be seen from Table 2, under the same test picture, thanks to the two-stage detection strategy, the performance of Cascade R-CNN was still stable, and its performance under various AP standards was at the forefront; however, its good score came at the cost of great computation time.

The one-stage RetinaNet and YOLOv4 performed similarly, and YOLOv4 slightly outperformed RetinaNet. Compared with SSD, both of them had a certain degree of lead in terms of indicators, and the latest training tricks available from analysis confers an advantage in accuracy. In addition, the calculation speed of these three methods is faster than Cascade R-CNN, without the intermediate step of proposal, which shortens the calculation time considerably.

The anchor-free based CenterNet had the lowest score; so, it can be concluded that the calculation of the heatmap is very susceptible to interference from similar objects in the background. However, the advantage of the anchor-free class method is that the calculation speed is much faster than other baselines, which is a huge advantage for scenarios with extremely high real-time requirements.

Our proposed DamperYOLO takes the lead on AP, but the score of YOLOv4 is lower than Cascade R-CNN; therefore, the edge extraction, attention mechanism, and feature fusion structure proposed in this paper are better than Cascade R-CNN. The calculation speed of DamperYOLO was similar to other one-stage class methods. Therefore, DamperYOLO is a model with a balance between speed and accuracy.

#### *5.5. Sensitivity Analysis*

In this section, multiple sets of sensitivity analysis are performed on each component of DamperYOLO, which includes the choice of backbone, edge extraction, the attention mechanism, the number of training iterations, and the minimum amount of training data.

### 5.5.1. Backbone

We conducted a sensitivity analysis on the backbone used by DamperYOLO while retaining other improvements. As shown in Table 3, the CSPDarknet53 used by YOLOv4 was improved based on ResNet50, so it performed better. In addition, the only objects need to be detected were dampers. Therefore, we believe that it may be more effective to expand the number of network layers and improve the feature abstraction ability of the backbone. The performance of ResNet101 also supports our idea, but if network layers such as using ResNet152 are added, the improvement is limited, so ResNet101 is used as the backbone.

#### **Table 3.** APs of different backbones.

#### 5.5.2. Edge Extraction

To verify the effectiveness of preprocessing, a sensitivity analysis was performed on the image denoising, and edge detection used while retaining other improvements constant. Table 4 shows that, compared with not using any preprocessing strategy, using image denoising and edge extraction alone leads to a certain improvement in detection effect. If both are used, the AP50 increase by about five percentage points, which shows that the image augmentation method in this paper is effective.

**Table 4.** APs of different preprocessing methods.

#### 5.5.3. Attention Mechanism

The attention mechanism is an important mechanism pioneered in the field of NLP, and was developed in object detection in recent years. In order to verify the effect of adding an attention mechanism in different layers of ResNet101, we conducted a sensitivity analysis for the number of times an attention mechanism is introduced while retaining the other conditions. As shown in Table 5, when the attention mechanism was added to the first three layers of ResNet101, the detection effect improved to a certain extent. However, continuing to introduce attention-blocks containing edge information to the 4th and 5th layers caused a drop in detection accuracy. This is because there is more abstract information in the feature maps extracted by the fourth and fifth layers in ResNet101, and the edge information is the basic feature information. This is counterproductive and reduces the detection performance.

**Table 5.** APs of different introduction times of the attention mechanism.

#### 5.5.4. Number of Epochs

The number of epochs for experimental training affects the performance of the model. Because the number of training epochs is not enough, the model is under-fitted, and the model has not yet fully learned to identify all the objects to be detected. Excessive training epochs reduce the robustness of the model, the parameters are limited by the existing training data, and the realization of unfamiliar data in the test set is reduced. Therefore, we conducted an evaluation test of the number of training times for the performance of the model, and the test results are shown in Table 6. It can be seen from the table that when the training epoch is 200, the model is the most balanced.

**Table 6.** APs of different epoch numbers.

#### 5.5.5. Minimum Training Data Experiment

Changes in the amount of training data also affect the final performance of the model. At the same time, by comparing the detection accuracy of the model with different amounts of data, the feature extraction ability of the model can be judged. As shown in Table 7, we conducted experiments with the minimum amount of data. From the results, it can be seen that when the amount of data decreases, the performance of the model has weak performance, which indicates that our data is sufficient. The model performance did not drop significantly until the test set dropped to 1750. Moreover, DamperYOLO had strong robustness and could still learn key feature information on small-scale datasets, which overcame the shortcomings of the previous model's poor generalization ability to a certain extent.

**Table 7.** Results of minimum training data experimental.

#### *5.6. Ablation Analysis*

To analyze the functions of the different components of DamperYOLO, an ablative analysis was performed on DamperDetSet. As shown in Table 8, Model B had better indicators than Model A, which indicates that using ResNet101 as the backbone can better extract image features. Model C uses image augmentation for preprocessing, which improves the quality of the input image and provides the model with better training data. Compared with other stages, the performance of Model D has the highest improvement in detection effect. This indicates that the attention mechanism plays a sufficient role, because the attention mechanism allows the model to focus on the edge information of the damper when converging, with the help of the image enhancement model. In addition, it can be seen from other comparative experiments that the additional overhead brought by it is very low, so it is necessary for our task to add an attention mechanism to the backbone.

**Table 8.** The results of the ablation analysis.

#### *5.7. Computational Complexity*

The network parameters and training time were recorded to evaluate the space and time complexity of the networks. As shown in Table 9, compared with Cascade R-CNN, DamperYOLO has a similar detection effect, but its parameters and training time are greatly reduced. Compared with YOLOv4, the space complexity and the training time are basically unchanged, because we only changed the backbone and added the attention mechanism on its basis, but a higher detection effect was achieved. In addition, CenterNet still consumes the least resources. The computational complexity of SSD is slightly higher than that of RetinaNet, but the detection effect is slightly worse.

#### **6. Conclusions**

We propose a power line vibration damper detection model named DamperYOLO based on a deep neural network that can detect the position of the vibration damper in drone inspection aerial images. DamperYOLO first uses the Canny algorithm to obtain the edge information of the original image, then uses the attention mechanism to introduce edge information into ResNet101 to guide feature extraction. Finally, it outputs a feature map that is more conducive to small target detection with the FPN structure. The following conclusions can be drawn through qualitative and quantitative experiments on the power line vibration damper detection dataset built in this paper. Compared with the current baselines in the object detection field, the DamperYOLO proposed in this paper can output state-of-the-art detection accuracy. The results of sensitivity analysis experiments show that edge detection, attention mechanism, and feature pyramid network all significantly improve the detection accuracy. The ablation analysis shows that the attention mechanism and the feature pyramid network improve the accuracy of the output detection results. In addition, DamperYOLO consumes space similar to the computational resources and baselines of other one-stage classes, but the detection accuracy can reach the level of Cascade R-CNN, which shows the superiority of our model. In the future, we will continue to introduce appropriate training tricks for the detection accuracy of DamperYOLO and explore the application of the model to other power line components.

**Author Contributions:** Conceptualization, W.C. and Y.L.; methodology, W.C., Y.L. and Z.Z.; validation, W.C.; formal analysis, W.C. and Y.L.; investigation, W.C., Y.L. and Z.Z.; resources, W.C., Y.L., and Z.Z.; writing—original draft preparation, W.C.; writing—review and editing, W.C., Y.L. and Z.Z.; visualization, W.C.; supervision, Y.L.; project administration, Y.L.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Natural Science Foundation of China, grant number 61962031, the National Natural Science Foundation of China, grant number 51667011, and the Applied Basic Research Project of Yunnan province, grant number 2018FB095.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data in this paper are undisclosed due to the confidentiality requirements of the data supplier.

**Acknowledgments:** We thank the Yunnan Electric Power Research Institute for collecting the transmission line inspection data, which provided a solid foundation for the verification of the model proposed in this paper. The authors thank the reviewers and editors for their constructive comments to improve the quality of this article.

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

#### **References**


**8. Applications of bacterial EVs**

#### **8.1 Use of EVs as a vaccine platform**

As reviewed above, EVs interact with host cells leading to cytotoxicity, immunomodulation, tissue disruption, and other effects that mimic those caused by living bacteria during infection. These characteristics make EVs interesting vectors for delivering antigens and other components, some of which may have adjuvant properties. These features make EVs good candidates for vaccine development. Several studies have shown that EVs can induce adaptive immunity and confer protection against infections caused by both Gram-negative and Grampositive pathogenic bacteria [83–85]. For instance, mice immunized with 1 μg of *E. coli* derived OMVs resulted in 100% protection against a lethal dose challenge, while the survival rate was only 20% in the untreated group [86]. In another study, intraperitoneal administration of *Streptococcus pneumoniae* BAA-255 EVs protected mice against the EV-producing cells and the pathogenic KCCM-41569 strain, demonstrating EVs' ability in eliciting a cross-protection against different strains [14].

#### **8.2 Use of EVs against** *S. aureus* **infections**

Regarding *S. aureus*, several studies have already reported the use of its derived EVs for immunization, revealing its potential in vaccine design. In 2015, Choi *et al*. demonstrated that exposition of bone marrow-derived dendritic cells to ATCC 14458 EVs during 24 h enhanced the expression of co-stimulatory molecules CD80 and CD86 and of proinflammatory mediators such as TNF-α, IL-6, and IL-12, suggesting the induction of adaptive immunity [42]. As expected, intramuscular administration with three doses of >5 μg of ATCC 14458 EVs resulted in 100% protection against challenge with a lethal dose of bacteria in a mouse pneumonia model, with a reduction of bacterial colonization, pneumonia, and production of cytokines [42]. They revealed that immunization is mediated mainly by CD4+ T cell response, and transfection of these cells from EVs-immunized mice to naïve mice results in 70% protection after a lethal-dose challenge of *S. aureus*. Finally, they demonstrated that ATCC 14458 EV immunization provides long-term protective immunity and that it is a safe method since the administration of EV doses 10-fold higher were not cytotoxic to mice [42].

In another study, Askarian et al. demonstrated that intraperitoneal vaccination with USA300-derived EVs promoted a high production of antibodies, in addition to the protection of mice against subcutaneous and systemic *S. aureus* infections [69]. Another example of *S. aureus* EVs' application as a vaccine was shown by Wang et al. EVs were purified from the JE2 Δ*agr*Δ*spa* strain containing a plasmid coding for non-toxic Hla and LukE toxins under control of the *spa* promoter, whose activity is enhanced in the absence of the *arg* quorum sensing system [40]. They demonstrated that recombinant non-toxic Hla and LukE are immunogenic, and engineered EVs carrying these detoxified cytolysins protected mice against lethal sepsis infection [40]. Remarkably, reports on OMVs used as vaccine platforms against *S. aureus* infections were also explored. Irene et al. used OMVs derived from *E. coli* to incorporate five *S. aureus* antigens, Hla, SpA, FhuD2, Csa1, and LukE. They were successfully integrated into *E. coli* OMVs, corresponding from 5–20% of the total protein content [87]. The engineered OMVs conferred significant protection against sepsis, kidney, and skin *S. aureus* experimental infections in mice [87].

#### **8.3 Use** *S. aureus***-EVs against other infections**

Interestingly, Yuan et al. used EVs derived from the *S. aureus* RN4220-Δ*agr* strain to produce particles with a reduced content of virulence factors and a decreased toxicity to generate a safe platform against viral infections [41]. Major components of *S. aureus* EVs were fused to tag sequences able to incorporate viral antigens, generating PdhB-FLAG and Eno-FLAG proteins associated with envelope E domain III, the primary protective domain for prevention of dengue virus (DENV) [41]. These heterologous viral antigens were successfully integrated into EVs, which induced antibodies against four DENV serotypes and protected mice against lethal challenge with DENV-2 [41].


**4.4 Inflammatory mediators involved**

A variety of factors are involved in the cascade of the inflammatory process in a diabetic retina. It has been seen that intravitreal levels of cytokines, chemokines and growth factors change under inflammation leading to increased secretion from endothelial cell and development of neovascularization. The major factors are highlighted below:


**Author details**

Mongkol Techakumphu1\*, Kakanang Buranaamnuay2 , Wichai Tantasuparuk1 and Nutthee Am-In1

1 Department of Obstetrics Gynaecology and Reproduction, Faculty of Veterinary Science, Chulalongkorn University, Thailand

2 Institute of Molecular Biosciences (MB), Mahidol University, Thailand


*2.5. Statistical Analysis*

Data were analyzed with SPSS 17.0 (SPSS Inc., Chicago, IL, USA, 2008). The Mann–Whitney U test was used to compare the mean di fferences between men and women, while the Pearson chi-squared test was used to compare the percentage di fferences between men and women. The one-sample Kolmogorov–Smirnov test was used to examine departures from a normal distribution of continuous variables, and a base-10 logarithmic transformation was applied to the variables that showed rightward skewing. A multivariable regression model analysis was used to evaluate the association of the eGFR with independent variables including the excretion of Cd and Pb. For each regression model, the coe fficient of determination ( *R*2) value was obtained together with standardized β. A generalized linear model analysis was used to estimate the mean eGFR with adjustment for age, covariates and interactions. Logistic regression analysis was used to estimate the prevalence odds ratio (POR) for the reduced eGFR across the quartiles of excretion of Cd and Pb. The *p*-values ≤0.05 for two-sided tests were assumed to indicate statistical significance.


*2.3. Expectation-Maximization (EM) Algorithm*

In the original MCLUST method, the EM algorithm is used to estimate the unknown parameters and compute the membership probabilities. In this subsection, we will first review the EM algorithm under the general MCLUST framework, and then highlight the differences in implementation between the MCLUST method and the MCLUST-ME method.

**Complete data log likelihood** Given observations (*y*1, ..., *yn*), suppose that each *yi* is associated with one of *G* states. Then, there exists unobserved indicator vectors {*zi* = (*zi*1, ..., *ziG*)} where *zi* iid ∼ Mult*G*(1, *τ*) with *τ* = (*τ*1, ..., *τG*). The complete data then consists of *xi* = (*yi*, *zi*). Assuming that the conditional probability density of *yi* given *zi* is ∏*G k*=1 *fk*(*yi*;*μk*,Σ*k*,Λ*i*)*zik* , the complete data log likelihood can be derived as follows,

$$\begin{split} l\_{\mathbb{C}} &= \log \prod\_{i=1}^{n} f(\mathbf{y}\_{i}, \mathbf{z}\_{i}) \\ &= \log \prod\_{i=1}^{n} f(\mathbf{y}\_{i} | \mathbf{z}\_{i}) f(\mathbf{z}\_{i}) \\ &= \log \prod\_{i=1}^{n} \left[ \prod\_{k=1}^{G} f\_{k}(\mathbf{y}\_{i}; \boldsymbol{\mu}\_{k}, \mathbf{E}\_{k}, \mathbf{A}\_{i})^{z\_{ik}} \right] \left[ \prod\_{k=1}^{G} \mathbf{r}\_{k}^{z\_{ik}} \right] \\ &= \log \prod\_{i=1}^{n} \prod\_{k=1}^{G} \left[ \tau\_{k} f\_{k}(\mathbf{y}\_{i}; \boldsymbol{\mu}\_{k}, \mathbf{E}\_{k}, \mathbf{A}\_{i}) \right]^{z\_{ik}} \\ &= \sum\_{i=1}^{n} \sum\_{k=1}^{G} z\_{ik} \log \left[ \tau\_{k} f\_{k}(\mathbf{y}\_{i}; \boldsymbol{\mu}\_{k}, \mathbf{E}\_{k}, \mathbf{A}\_{i}) \right]. \end{split} \tag{11}$$

**EM iterations** The EM algorithm consists of iterations of an *M step* and an *E step*, as described below.

$$\hat{z}\_{ik} = \frac{\mathbf{f}\_k f\_k(\mathbf{y}\_i; \hat{\mathbf{p}}\_{k'} \hat{\mathbf{E}}\_{k'} \mathbf{A}\_i)}{\sum\_{j=1}^G \mathbf{f}\_j f\_j(\mathbf{y}\_i; \hat{\mathbf{p}}\_j, \hat{\mathbf{E}}\_j, \mathbf{A}\_i)}. \tag{12}$$

The two steps alternate until the increment in *lO* is small enough. Upon convergence, a membership probability matrix is produced and each observation is assigned to the most probable cluster, that is,

$$\text{membership of } \mathfrak{Y}\_i = \text{argmax}\_k \{ \hat{z}\_{ik} \}, \tag{13}$$

and the classification uncertainty for *yi* is defined as

$$1 - \max\_{k} \{ \mathcal{E}\_{ik} \}. \tag{14}$$

In two-group clustering, the classification uncertainty cannot exceed 0.5 (otherwise the point is incorrectly assigned).

For MCLUST, the component density *fk* is defined in (2), and for MCLUST-ME, *fk* is substituted by *gk* in (8).

**M-step implementation details** For likelihood maximization in the *M step*, a closed-form solution always exists for *τ*ˆ*k*, *k* = 1, . . . , *G* (see [12] for more details):

$$\mathfrak{H}\_{\mathbf{k}} = \frac{1}{n} \sum\_{i=1}^{n} z\_{i\mathbf{k}} \tag{15}$$

We can derive the estimation equations for *μk* and Σ*k* (*k* = 1, ... , *G*) by taking the partial derivatives of *lC* with respect to *μk* and Σ*k* and setting the derivatives to 0. For MCLUST-ME (see [13] for a summary of useful matrix calculus formulas, in particular (11.7) and (11.8)):

$$\frac{\partial l\_{\mathbb{C}}}{\partial \boldsymbol{\mu}\_{k}} = \sum\_{i=1}^{n} z\_{ik} (\mathbf{E}\_{k} + \mathbf{A}\_{i})^{-1} (\mathbf{y}\_{i} - \boldsymbol{\mu}\_{k}) = \mathbf{0} \tag{16}$$

and

$$\frac{\partial l\_{\mathbb{C}}}{\partial \mathbf{E}\_{k}} = \frac{1}{2} \sum\_{i=1}^{n} z\_{ik} (\mathbf{E}\_{k} + \mathbf{A}\_{i})^{-1} (\mathbf{y}\_{i} - \boldsymbol{\mu}\_{k}) (\mathbf{y}\_{i} - \boldsymbol{\mu}\_{k})^{T} (\mathbf{E}\_{k} + \mathbf{A}\_{i})^{-1} - \frac{1}{2} \sum\_{i=1}^{n} z\_{ik} (\mathbf{E}\_{k} + \mathbf{A}\_{i})^{-1} = \mathbf{0}. \tag{17}$$

For estimation equations under the MCLUST model, one set all the Λ*i*'s to 0 in the above two equations. Note that under MCLUST, if there is no constraint on Σ*k*, there are closed-form solutions for *μk* and Σ*k*:

$$
\hat{\mathbf{p}}\_k = \frac{\sum\_{i=1}^n z\_{ik} \mathbf{y}\_i}{\sum\_{i=1}^n z\_{ik}} \tag{18}
$$

and

$$\boldsymbol{\Sigma}\_{k} = \frac{\sum\_{i=1}^{n} z\_{ik} (\mathbf{y}\_{i} - \hat{\boldsymbol{\mu}}\_{k}) (\mathbf{y}\_{i} - \hat{\boldsymbol{\mu}}\_{k})^{T}}{\sum\_{i=1}^{n} z\_{ik}}.\tag{19}$$

For MCLUST-ME, each *yi* corresponds to a different Λ*i*. One can see that, in general, there is no closed-form solution for (*μk*,Σ*k*). In our implementation of the MCLUST-ME M step, we solve *μk* from (16),

$$\hat{\mathfrak{a}}\_{k} = \left[ \sum\_{i=1}^{n} z\_{ik} (\mathbf{E}\_{k} + \mathbf{A}\_{i})^{-1} \right]^{-1} \sum\_{i=1}^{n} z\_{ik} (\mathbf{E}\_{k} + \mathbf{A}\_{i})^{-1} \mathbf{y}\_{i\prime} \tag{20}$$

and plug it into (17), and then use the limited-memory BFGS, a quasi-Newton method in R (function optim [14]), to obtain an optimal solution for Σ*k* numerically. We obtain *μ*ˆ *k* by substituting the resulting Σˆ *k* into (20).

The complexity of the EM algorithm for the MCLUST-ME increase with the number of clusters, the number of parameters (which is determined by the dimension of the data), and the number of observations. It is much slower than the original MCLUST algorithm due to the fact we have to use a numerical optimization routine to find the maximum likelihood estimate (MLE) of *μk*'s and Σ*k*'s in the M step. (See Conclusion and Discussion for a brief summary of running time of MCLUST-ME on the real data example.)


**Author details**

Guna Muthuvairavan1 , Saranya Manikandan1 , Elavarasan Elangovan2 and Sendhil Kumar Natarajan1 \*

1 Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, Union Territory of Puducherry, India

2 Department of Aerospace Engineering, SJC Institute of Technology, Chikkaballapur, Karnataka, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**About the Editors**

**Valentina Cauda** is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino, head of the TrojaNanoHorse Lab (in brief TNHLab), and co-founder of the Interdepartmental laboratory PolitoBIOMed Lab.

Thanks to her ERC Starting Grant project (TrojaNanoHorse, GA 678151), which started in March 2016, she now leads a multidisciplinary research group of 18 people, including chemists, biologists, physicists, engineers, and nanotechnologists. Her main research topic concerns theranostic nanomaterials: the research team develops metal oxide nanomaterials from wet synthesis, chemical functionalization, and physical–chemical characterization up to their coating by lipidic bilayer from both artificial and natural origins, aimed for drug delivery, tumor cell targeting, bio-imaging. Metal oxide nanomaterials, like zinc oxide, mesoporous silica, titania, and metal (gold, silver) nanostructures, as well as liposomes and cell-derived extracellular vesicles, are investigated.

Valentina Cauda graduated in Chemical Engineering in 2004 at Politecnico di Torino and then received her Ph.D. in Material Science and Technology in 2008. After a short period at the University of Madrid, she worked as a Post Doc at the University of Munich, Germany, on nanoparticles for drug delivery and tumor cell targeting. From 2010 to 2015, she worked as a Senior Post Doc at the Istituto Italiano di Tecnologia in Torino, and then she moved, as Associate Professor, to Politecnico di Torino. In 2010, she received the prize for young researchers at the Chemistry Department of the University of Munich, in 2013, she received the Gioved`ı Scienza award, in 2015, the Zonta Prize for Chemistry, and, in 2017, the USERN Prize for Biological Sciences. She has 113 scientific publications and a Hirsch Factor of 35 (updated on November 2020). She holds 4 international patents concerning the use of metal oxide nanoparticles in nanomedicine. Prof. Cauda is the principal investigator of several industrial, national, and international projects raising more than € 5 million funds in all. The most relevant are the recently granted ERC Proof-of-Concept *XtraUS* N. 957563, the FET Open RIA *MIMIC-KEY*, the Marie-Slodowska Curie Action *MINT* N. 842964 (where she acts as supervisor of an incoming Post-Doc from abroad), and the ERC Starting Grant *Trojananohorse*.

More details available at https://areeweb.polito.it/TNHlab/.

**Giancarlo Canavese** is an Assistant Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino and a member of the Interdepartmental laboratory PolitoBIOMed Lab and the Materials and Processes for Micro & Nano Technologies (Chilab Laboratory) at DISAT.

His main research topics concern sonodynamic technique enhanced by engineered nanostructures for theranostic applications, interactions of oxide nanoparticles under ultrasound activation with biomaterial structure, such as cells and extracellular vesicles, and acoustophoretic labs-on-chips for drug delivery, tumor biomarkers detection, and bio-imaging.

Giancarlo Canavese received his ME degree in Mechanical Engineering in 2004 and his Ph.D. degree in Biomedical Engineering in 2008 from Politecnico di Torino, Italy. From 2010 to 2015, he worked as a Senior Post-Doc at the Istituto Italiano di Tecnologia, Italy, where he studied nanostructured piezoelectric materials for biosensors and energy devices. From 2013–2014, he spent six months as a visiting scientist at the Houston Methodist Research Institute, Texas US, where he worked on a microfluidic lab-on-chip device to study drug delivery in microgravity conditions on the International Space Station.

He has 90 scientific publications and a Hirsch Factor of 27 (updated on November 2020). He holds 8 international patents about micro and nanotechnologies for biomedical applications.


*3.1.1 In a diesel-biodiesel blend*

The level of biodiesel in a diesel-biodiesel blend is determined in a laboratory by observing the mid-infrared absorption of the C〓O bond present in the fatty acid methyl esters (FAMEs) that constitute the biodiesel. Although this test gives satisfactory results, it cannot be used in the field, which is a major drawback, as fuel distributors need to test the fuel when they are adding biodiesel to diesel on a commercial scale. Furthermore, the accuracy of the results will be compromised if the biodiesel itself is adulterated with vegetable oil or animal fat, whose constituent triglycerides also have C〓O bonds.

The conversion of esters into hydroxamates by their reaction with hydroxylamine [27–29], forming complexes of a red to violet color caused by the reaction with Fe3+ , has been used to confirm esters in organic analysis procedures. **Figure 1** shows the reaction by which this complex is formed.

### **Figure 1.**

*Chemical reactions involved in the test for esters known as the hydroxamic acid test, used to test for the presence of biodiesel in fossil diesel. Adapted from Silva et al. [29].*

**91**

**Figure 2.**

*Santos [30].*

*Fuel Quality Monitoring by Color Detection DOI: http://dx.doi.org/10.5772/intechopen.86531*

Silva et al. used this test to develop a new spectrophotometric method to determine biodiesel levels in diesel-biodiesel blends ranging from B0 (pure fossil diesel) to B5 (fossil diesel containing 5% biodiesel), in which the esters from the biodiesel present in the mixtures react with hydroxylamine chlorhydrate in an alkaline solution, forming alkaline salts from the hydroxamic acid, followed by acidification, to form hydroxamic acid, followed by a reaction with Fe3+ ions. The complex formed, the intensity of whose color is proportional to the biodiesel content, is extracted with *n*-hexane/*n*heptane, forming an upper phase with a yellow, orange, or red color, depending on the biodiesel concentration, as shown in **Figure 2**. Absorbance can be measured using spectrophotometry at 420–440 nm. The linearity, limit of detection, limit of quantification, accuracy, selectivity, and specificity results indicate that the proposed method is adequate for analyses of biodiesel in diesel-biodiesel blends. A simplified method

Using the same reaction, Santos developed a simple, practical, rapid method that could be used in the field for semiquantitative identification by the naked eye of biodiesel in diesel-biodiesel blends ranging from 0% to 6% v/v biodiesel [30]. A similar method was developed by Leite and Fernandes for the 0–7% range [32]. What sets these methods apart from their predecessors is the separation of the biodiesel from the blend before the hydroxamic acid test. To do this, solid-phase extraction is conducted using a chromatographic column adapted from a 3mL disposable plastic syringe into which around 0.4 g silica gel is inserted, supported by a cotton plug (see **Figure 3a**). The diesel-biodiesel blend is introduced as if in frontal chromatography, pushed by a plunger, generating a less polar diesel fraction (F1) followed by a more polar fraction containing biodiesel (F2), displaced by the addition of ethanol (see **Figure 3b**).

*Color scale of samples of fossil diesel containing biodiesel at 0–6% v/v, showing intensities proportional to the concentrations of biodiesel in the diesel, for semiquantitative analysis observable by the naked eye. Source:*

By using seven reference samples of the diesel-biodiesel blend containing 0, 1, 2, 3, 4, 5, and 6% (v/v) biodiesel, the concentrations of which were confirmed by the laboratory reference method [33], a standard table of colors was prepared that could be used for the semiquantitative analysis of the percentage of biodiesel, as in **Figure 2**, since the intensity of the color formed by the ferric hydroxamate complex is proportional to the level of biodiesel present in the sample. By comparing the observed colors with the standard color table, it was possible to determine the

was proposed for qualitative analyses for use at filling stations [29, 31].


**4. Conclusions**

The old joke has it that there is no such pain that your doctor cannot take it. For severe pain sufferers, faced with inattentive medical professionals, this joke loses its humor. The widespread fear of dentists and surgeons is not caused by the nature of their work, but by the pain and discomfort their work connotes. It is vital that doctors of all specialties where chronic suffering is a possibility pay special attention to how they communicate with patients. Correct identification of the cause of pain and its mutability is key to successful treatment. It is often assumed that in certain cases pain cannot be effectively removed and that it can be a desired symptom. However, this should not extend to the assumption that it is natural and can be ignored. Pain is a key diagnostic. It offers feedback on accuracy of the chosen procedures. Take, for example, dental root canal: the pain caused by touching the exposed nerve indicates that the treatment proceeds correctly. It is a specific reaction to a specific stimulus, and it allows a specific diagnosis; diagnoses based on vague understanding of unexamined and unexplained pain have no place in proper medical procedures.


**4. Methods – surgical technique: esophageal mucosectomy**

Surgical technique following standardization proposed by Aquino [65]:

a.Mucosal resection - Abdominal stage: The surgery starts with a midline laparotomy from the xiphoid process to 5 cm below the umbilicus followed by dissection of the abdominal esophagus and division of vagi nerves. Longitudinal myotomy in the anterior esophagus from the cardia to the hiatus and circumferential dissection of the mucosa/submucosa layer in 5 to 7 cm (**Figure 1**).

Cervical stage: Left lateral cervicotomy following the anterior border of the sternocleidomastoid muscle from the sternum to 10 cm upwards. Dissection of the esophagus free of the posterior and prevertebral fascia and trachea. Longitudinal myotomy in the anterior esophagus 5 cm from the pharynx to the sternum and circunferencial dissection of the mucosa/submucosa layer. (**Figure 2**).

Combined stage: After a cylindrical segment of mucosa is dissected free of the muscular in the abdomen and neck, a small mucosectomy is made in the abdomen and neck to allow the passage of a rectal tube upwards. Cervical esophageal mucosa is circumferentially transected and tied to the rectal tube by a long and resistant surgical thread to allow pulling the replacement viscera to the neck. The mucosa is slowly striped downwards and inverted in the abdomen. (**Figures 3** and **4**). The esophagus is completely sectioned at the level of the esophagogastric junction and the neck.

b.Digestive Tract Reconstruction: Digestive tract was reconstructed in all patients with the stomach after division of the left side gastric, right gastroeplicoic and

**Figure 1.** *Abdominal stage-circunferencial dissection of the mucosa/submucosa layer.*

**Figure 2.** *Cervical stage-circunferencial dissection of the mucosa/submucosa layer.*

short vessels. A route for stomach transposition on accessibility to the neck in all patients was into the muscular tunnel (**Figure 5**). Esophagogastrostomy anastomosis was performed at the cervical level too in all patients with a circular stapler for end-to-side anastomosis. A feeding jejunostomy tube was always added to the procedure. Drains were left in the abdomen and neck.


**4.1 Relative efficiency of HEIs**

The efficiency measurement of HEIs was carried out through the output orientation DEA model. The teaching, research, and overall efficiency of 40 HEIs under observation are given in **Table 1**.

As we observed from the result of the efficiency scores of HEIs in **Table 1**, the mean and minimum score of the overall(join) efficiency of HEIs is greater than the mean and minimum score of teaching efficiency of HEIs. And also, the mean and minimum score of the overall(join) efficiency of HEIs is greater than the mean and

*Perspective Chapter: Complementarities of Teaching and Research on Higher Education DOI: http://dx.doi.org/10.5772/intechopen.109456*

#### **Table 1.**

*Relative efficiency scores of HEIs.*

minimum score of the research efficiency of HEIs. Moreover, there are some HEIs that are efficient in either teaching or research is also efficient in their overall efficiency. This implies that each activity has a positive impact on the overall efficiency, and institutions that execute both activities simultaneously have better efficiency scores rather than adopting any one activity. Therefore, the institutions adopting both activities simultaneously are more beneficial rather than adopting a single activity. This indicates that the joint adoption of both activities follows economies of scale.

The number of faculty members is a proxy for the size of the HEI. Overall efficiency results indicate that smaller HEIs or universities with a smaller number of faculty members were performing better in transforming input resources into higher outcomes. Bigger HEIs might be restructured to new demands with lesser difficulty as they have established flexible structures. Our findings are in line with the theory of economies of scope. That is, HEIs seem to benefit from the reduction of cost per unit of output by adopting the other activity when one is in practice. This also strengthens the theory of complementarity, where one activity gives more power to output when the other activity is already in practice. Our results are in line with the finding concluded by Long [56] that economies of scope exist for the adoption of teaching and research in combination. However, these results should be generalized with caution of heterogeneity. HEIs would further increase efficiency by giving the same emphasis on teaching and research because the implementation of these activities simultaneously allows them to build their own core skills and gain more benefits. Therefore, institutes that have jointly adopted both activities are substantially more likely to achieve higher performance in those activities.


4.2.1. Approaching Flow Measurements

The main restrictions for typical flow measurements at large velocities are related to instrument intrusion in the flow. For supercritical flows, instruments can only be deployed from a fixed point (a bridge or cable way), and intrusive measurements generally become impossible above a flow speed of 5 m/s (for a current meter on a torpedo). Acoustic Doppler Current Profilers (ADCP), which are currently used in the field to measure flow fields, are limited to a relative velocity of a few meters per second between the apparatus and the river, making their use generally impossible for supercritical flows. Indeed, these instruments are highly sensitive to air entrainment around the apparatus. From our own experience, the result quality using ADCP starts to decrease above a flow speed of 3.5 m/s. It should also be noted that such high flows are often associated with high suspended load concentrations and woody debris [69], which make such a measurement very dangerous.

The non-intrusive counterpart instruments used to measure large flow velocities during flood events are radar velocimeters ([70]; Figure 7a) and video analysis systems, such as Large Scale Particle Image Velocimetry (LSPIV; [71]). The main limitation of these systems is that they can only provide a surface velocity, so complex 3D flows cannot be described. Moreover, radar velocimeters are not able to provide a flow direction. LSPIV has been applied successfully to measure the velocity field in rivers with high discharges [72,73]. However, a difficulty that appears close to bridge foundations in supercritical flows is that the water surface may become 3D due to standing waves or antidunes. Since surface velocity estimations are made on a plane, this type of surface could lead to significant errors. On the other hand, stereovision-based techniques could be of interest to describe the water surface of such rapid flows.

**Figure 7.** (**a**) Radar measurement from a bridge (Photo: P. Belleudy), and (**b**) a system for pier erosion monitoring used at NARLabs (National Applied Research Laboratories), Taiwan.


**3. Methods**

The study is focused on studies conducted between April and August 2019. The study was mostly based on 41 detailed qualitative interviews of those working on the interactive job channels (i.e., Uber and Taxify). We selected interviewees from among those who replied to the broader survey. A non-random survey of 355 Uber and Bolt drivers was performed, and quantitative data from this more general population was also obtained. Following the claims made by [2], we started our research with a web-based survey designed to hit drivers on leading shared riding platforms: Uber, Uber Eats, Bolts, and others. These businesses consider their network staff as independent contractors and not employees.

We recruited drivers to take part in our mobile online survey in two ways. First of all, we aimed ads to Facebook network drivers. The benefits and drawbacks of using Facebook ads for low-wage jobs can not be overemphasized [2]. The prevalent use of Facebook in South Africa and other developing countries makes it a valuable sampling environment. It should be judiciously compared to the telephone-based means of interviewing [3]. We tailored Facebook advertising for our survey to individuals between 18 and 55 who spoke English and reported working on one of the two channels. The commercial was shown to 47,981 users, some of whom were shown advertising several times, and 4869 users clicked on the survey link, just above 10% of those to whom the commercial was shown. Of those who clicked on a survey link shared in the commercial, 476 described themselves as platform staff started the survey, and 355 completed the study.

Respondents to the study were not indicative of the entire employees of the site. Those who marked themselves as platform employees on their Facebook profile, and those who belong to communities connected to the interactive job platform on Facebook – the two networks from which respondents have been drawn – may be more attached to this job than other platform employees and may also vary from the people of other platform workers in different unexpected ways. Given the lack of data on this digital workforce forum's makeup and experience and the difficulties of accessing this sample demographic through other means, the analysis presented here provides a significant, although the non-representative, reflection of this developing market.

One of the questions posed in our online survey was whether the respondent would participate in an hour-long telephone or in-person interview with R120 as an incentive for participation. We called for contact details from the interviewees who showed a desire to schedule the consultation. Of the 355 people who completed the online survey, most (245 or 69%) showed interest in engaging in the follow-up interview. To determine who contacted the 245, we tried to optimize diversity in terms of age, size, ethnicity, geography, political preference, and family household

*Evaluation of Algorithmic Management of Digital Work Platform in Developing Countries DOI: http://dx.doi.org/10.5772/intechopen.94524*

income. We conducted interviews with 41 platform staff or 11 percent of those who showed interest, and interviews lasted at least one hour or less. We use pseudonyms all over to protect the anonymity of the respondents.

Although our survey is not generalizable to the digital network workers' population, interviews have helped us classify processes using reasoning rather than statistical inference and to achieve fullness [4]. Also, we found the same similarities through interviews, which enabled us to trust the findings: that is, identical similarities in the interviewee's account across various platforms. However, we equate different employees' perspectives on other interactive job platforms or the same worker's experience working on multiple platforms. Remarkably, respondents consistently emphasized Uber's higher level of time and task management relative to other networks.

### **3.1 Passengers interviews**

The responses were triangulated by questioning 19 passengers who used or are currently using Uber and Bolt at 6 locations in two cities (Pretoria and Johannesburg). On average, the passengers used the service 4–7 times over three months. Ten of the passengers questioned used both Uber and Bolt, 4 used only Bolt, whereas five used only Uber. The interview aimed to either affirm or disprove the impressions shared by drivers of how passengers use the platform, mainly how drivers are classified and their actions and attitudes towards price increases.

### **3.2 Analyzing archived data: drivers' online forum**

We analyzed the drivers' details on the online forums where all drivers are registered to do so. This is because most drivers said in our interviews that the forums were the primary source of information and places for them to socialize. Two online forums have been observed: one that is not moderated by Uber and Bolt, like different Facebook communities, and the official Facebook pages moderated by Uber and Bolt.

One author who registered as a Bolt driver was granted access to Bolt's (Taxify) and Uber Driver's and Clients 'new driver' Facebook forum, which provides information that is specifically relevant to the business. We also entered other unmoderated private driver groups on Facebook by applying to participate as researchers. This was done to prevent being mistakenly posing as a driver. We have retained a single observation status during this review. To ensure that the method used in [5] was followed, we sampled 142 posts and responses on the online site, noting the algorithmic features chosen from the thousands made over three months.


**Table 2.**

*Plant protection products approved by the European Union (EU) for use in organic farming [24].*

**143**

**Figure 1.**

*Jebusaea hammerschmidti.*

*Insect Pest Management in Organic Farming System DOI: http://dx.doi.org/10.5772/intechopen.84483*

**6. A case study of organic date palms**

enemies of pests.

**6.1 Selection of planning materials**

There are about 100 million date palms in the world mostly distributed in Asia and North Africa, producing 7.78 million ton of dates annually [25]. The international famous date palm cultivars include Medjool, Deglet Noor, Barhee, Halawy, Khalas, and Khadrawy. Organic dates are now produced in many countries around the world including Tunisia, Israel, Saudi Arabia, Egypt, Sudan, Iran, Algeria, and the USA. Date palm, whether grown conventionally or organically, has numerous pests and diseases including 132 species of arthropod (insects and mites), 52 vertebrate pests (birds, rodents, bats), and 28 non-arthropod pests (slugs and snails, parasitic nematode) [26, 27]. Additionally, more than 16 important fungal, phytoplasma, and unidentified diseases attack the date palm. The major ones include Bayoud, black scorch, *Diplodia*, Khamedj inflorescence rot, Belaat, graphiola leaf spot, Al-Wijam disease, brittle leaf disease, and Faroun disease [28]. These pests and diseases may cause substantial losses in date palm groves if left unmanaged. Therefore, a well-planned and supervised pest management program is important to maintain a sustainable date palm production in organic farming system. Some examples of injuries inflicted by pests on date palm and dates are shown in **Figures 1** and **2**.

Date palm pests of economic important in organic farming could be prevented through an IPM program comprising the following components: selection of planning materials, pest monitoring, cultural management, and conservation of natural

To a healthy vigorous palm that yield good quality date fruits, one should start with good planting materials whether tissue culture seedlings, offshoots, or mature palms. Planting materials should be adapted to the area where to be grown, in addition of being healthy and free from pests and diseases. Such planting materials should be obtained from nurseries certified for organic date palm production, where strict quarantine measures and protocols are applied. Many serious pests and diseases of date palm including the invasive red palm weevil spread rapidly through movement of infested planting materials [29]. Dubas bug, scale insects, longhorn beetle, and rhinoceros beetle also invade new areas through transportation of

*Symptoms of damage on the fruit bunch stalk (left) due to Oryctes elegans and on the trunk (right) due to*

*Multifunctionality and Impacts of Organic and Conventional Agriculture*

**Name of product Purpose and specifications of use**

Pheromones Used only in traps and dispensers

Quassia from the plant *Quassia amara* Only insecticide and repellent

Ethylene Insecticidal fumigant against fruit flies Paraffin oil Used as insecticide against small-bodied insects Fatty acids (soft soaps) Insecticide against mite, thrips, and aphids

Azadirachtin from the neem tree (*Azadirachta*

Laminarin (from *Laminaria digitata*) or kelp or

Pyrethrins from the leaves of *Chrysanthemum*

Microorganisms, e.g., *Bacillus thuringiensis*, *Beauveria bassiana*, and *Metarhizium anisopliae*

Spinosad from the soil bacterium *Saccharopolyspora*

Lime sulfur (mixture of calcium hydroxide and

Kieselgur (diatomaceous earth) from the hardshelled diatom protist (chrysophytes)

Sodium hypochlorite (bleach or as javel water). It is a disinfectant with numerous uses, and its effect is

Copper compounds such as: copper hydroxide, copper oxychloride, copper oxide, tribasic copper sulfate, and Bordeaux mixture (copper sulfate and

Sheep fat (obtained from fatty sheep tissues by heat extraction and mixed with water to obtain an

Calcium hydroxide Used as fungicide

Pyrethroids (only deltamethrin or

*indica*)

brown algae seaweed

*cinerariaefolium*

*spinosa*

sulfur)

due to the chlorine

calcium hydroxide)

oily water emulsion)

lambdacyhalothrin)

self-defense mechanism. Sunflower oil, whey, and lecithins are used as fungicides, while vinegar is used as fungicide and bactericide, and *Urtica* sp. is used as insecticide, fungicide, and acaricide [21]. In organic farming, only active substances listed in the Commission Regulation (EC) No. 889/2008 (**Table 2**) can be used. New update is frequently being made by the EC to add or remove PPPs from the list.

Beeswax Used as protectant for treatment of cuts and wounds after

Plant oils Used for control of small-bodied insects such as thrips,

pruning or in grafting

aphids, and whiteflies

Used as insecticide

Used as insecticide

Used as fungicide

Naturally occurring aluminum silicate (kaolin) As insect repellent against a wide range of insects at a rate of 50 kg/ha

Sulfur Used as broad-spectrum inorganic contact fungicide and acaricide

Quartz sand Used as repellent against vertebrate pests

*Plant protection products approved by the European Union (EU) for use in organic farming [24].*

per ha annually

Used as mechanical insecticide

Used in seed treatment as viricide and bactericide

Used as fungicide and bactericide maximum of 6 kg copper

A triglyceride consisting predominantly of glycerine esters of palmitic acid, stearic acid, and oleic acid.  It should not be applied to the edible parts of the crop

Origin should not be GMOs

A polysaccharide from the group of the glucans, used to protect plants against fungi and bacteria. Kelp should be

Used only in traps with attractants or pheromones

grown according to the organic standards

**142**

**Table 2.**


**TOXICOLOGY STUDIES - CELLS, DRUGS AND ENVIRONMENT**

Edited by **Ana Cristina Andreazza and Gustavo Scola**

#### **Toxicology Studies - Cells, Drugs and Environment**

http://dx.doi.org/10.5772/58714 Edited by Ana Cristina Andreazza and Gustavo Scola

#### **Contributors**

Dorina Coricovac, Cristina Dehelean, Mariana Ribeiro, Armanda Santos, José Custódio, Rossella Snenghi, Donata Favretto, Daniel Dorta, Omid Mehrpour, Eduardo Rocha, Maria João Rocha, Jalal Pourahmad, Bruno Nunes

#### **© The Editor(s) and the Author(s) 2015**

The moral rights of the and the author(s) have been asserted.

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Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2015 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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*2.2. PpaPrx19 Is a 36 kDa Basic Peroxidase*

We extracted total protein from *P. patens* gametophores grown in control conditions and we then followed a three-step protocol for purification, including ammonium sulfate precipitation, hydrophobic chromatography on phenyl Sepharose and cationic chromatography on SP Sepharose. A fractionated precipitation with ammonium sulfate did not allow the partial purification of the peroxidases of interest, which led us to consider just one fraction, precipitating the proteins with 95% (NH4)2SO4. The fraction was pooled into phenyl Sepharose chromatography, obtaining two major fractions of peroxidases (Figure 2A). The first eluted fraction (F1) contained only acidic peroxidases, while F2 contained both neutral and basic peroxidases and therefore was selected to continue the purification process (Figure 2). After this step, the specific activity for F2 reached 266 nkat mg−1 protein (Table 1). The F2 was then loaded into a cationic exchange chromatography, and the peroxidase bound to SP Sepharose matrix was eluted with a linear gradient of 9.5–11.5 pH. Neutral peroxidases were not retained in the matrix and only one peak of peroxidase activity was eluted, at a pH of 10.9 (Figure 2A). The fraction arising from cationic chromatography migrated as

two different bands of 36 and 46 kDa in SDS-PAGE electrophoresis, but an IEF showed only one peroxidase with the pI value determined to be 10.04 (Figure 2B). The peptide mass fingerprinting of the two resultant proteins enabled us to detect that the protein of 46 kDa corresponded to a lipase (accession number XP\_001755452). The 36 kDa protein was identified as a predicted protein (access number XP\_001781554) which corresponded to PpaPrx19 (according to RedoxiBase nomenclature).

**Figure 2.** Purified PpaPrx19 is a strongly basic peroxidase. (**A**) Purification process of PpaPrx19, including adsorption chromatography on phenyl Sepharose (upper panel, peak F2) and cationic exchange chromatography on SP Sepharose (lower panel). Profiles of peroxidase activity and protein are denoted either by a continuous or by a dotted line, respectively. (**B**) Protein fingerprint in SDS-PAGE (left) and peroxidase isoenzyme pattern in IEF (right) of the crude extract (1) and the purified peroxidase (2) SDS-PAGE and IEF were revealed using the silver staining method and 4-MN in the presence of H2O2, respectively. (**C**) Dependence on pH of the purified PpaPrx19 activity. Data presented are average values ± SD of *n* = 3 experiments.

**Table 1.** Purification of basic peroxidase PpaPrx19 from *P. patens*. Peroxidase activity was measured using TMB as substrate.

We confirmed by RT-qPCR that *PpaPrx19* was strongly induced after a treatment with NaCl and that gene expression was modulated in response to other stresses such as hydrogen peroxide and mannitol (Figure S1).

We also evaluated the dependence on pH of PpaPrx19 enzymatic activity, using a different pH (4.0 to 9.0) in the reaction mixture. The purified peroxidase showed the highest activity at pH 5.0, but it rapidly decreased at pH > 6 and showed no activity at pH above 7.0 (Figure 2C). These results do not differ from other peroxidases purified from different sources, with the optimum pH between 4.5 and 6.5 [18–20]. pH is critical for peroxidase activity because pH values outside the optimum prevent the heme from binding to the active site of the enzyme [21].

#### *2.3. PpaPrx19 Is Homologous to Peroxidases with a Role in Lignification*

PpaPrx19 is 332 amino acids long, including a 26 aa N-terminal signal peptide, and it is targeted to the secretory pathway according to analysis with SIGNALP [22] and TAR-GETP [23] programs. The exon–intron pattern of PpaPrx19 is the second most abundant for *P. patens* and classic for class III peroxidases, consisting in three exons and two introns [13]. In a BLAST search, PpaPrx19 showed the highest identity at the protein level with two other *P. patens* peroxidases (PpaPrx18 and PpaPrx09) and the moss *Tortula ruralis* (Table 2). The rest of the listed peroxidases belong to gymnosperms and angiosperms, and show identity values below 50%, emphasizing the evolutionary distance among them and pointing out the unique characteristics of this peroxidase, at the amino acid level.

**Table 2.** Comparison of PpaPrx19 mature protein sequence with other class III peroxidases. The most similar peroxidase sequences based on BLASTP searches against the RedoxiBase database were used.

Given these low identity values, we blasted PpaPrx19 against *Arabidopsis* peroxidases, in order to infer a putative function. Most of the peroxidases with the highest identity level have a reported role in lignification (Table 3). This was a surprising result, given that *P. patens* has an internal water-conducting system constituted by hydroids and living cells with thick walls [24] containing pre-lignin and lignin-like polyphenols but no true lignin (defined as the polymerized compounds found in vascular plants) has been described [5,6].

**Table 3.** Reported function of *Arabidopsis* peroxidases which show highest homology to mature PpaPrx19 protein sequence after a BLAST search with Redoxibase.


**1. Introduction**

Phosphorus is a non-metallic element in the 5th group of the periodic table with an atomic weight of 30.97, a density of 1.82 g/cm3 , and an atomic number of 15. It was discovered in 1669 by a German chemist named Hennig Brand. The melting point and boiling point of this element are 44.1 and 280°C, respectively, and its atomic radius is 1.28 angstroms. Phosphorus is never found in nature in its free state; instead, it's found combined with other elements in rocks, phosphate minerals, and all living cells. It appears as a waxy white solid with an unpleasant smell. Phosphorus is highly reactive and emits a small amount of light when it reacts with oxygen. It burns rapidly in air,

transforming into phosphorus pentoxide. This non-metal does not dissolve in water, but it is soluble in carbon disulfide [1]. Phosphorus is an essential component of biological molecules such as ATP, ADP, and DNA, and it's also found in bones and teeth. It is present in trace amounts in all the foods we eat. There are various isotopes of phosphorus; only one, 31P, is stable. The other isotopes of phosphorus are radioactive and have short half-lives ranging from a few nanoseconds to a few seconds, except for two isotopes, 32P and 33P, which have longer half-lives of 14 and 25 days, respectively. These longer half-lives make these isotopes useful for analyzing and labeling DNA.


**Author details**

S.B. Vishnu\* and Biju T. Kuzhiveli Centre for Advanced Studies in Cryogenics, National Institute of Technology Calicut, Calicut, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Effect of Roughness Elements on the Evolution of Thermal Stratification in a Cryogenic… DOI: http://dx.doi.org/10.5772/intechopen.98404*


*4.2. ABCA7 in Brain Functions*

*Abca7* is mainly expressed by neurons and microglia in human and mouse brains [23,80–82]. Both isoforms described in Section 2.2 are observed by western blots in brain samples of healthy donors and AD patients [25]. Consequences of *Abca7* depletion

on brain cholesterol homeostasis were first investigated in mice. Lipidomic analysis of forebrain samples from five male *Abca7*−/− mice showed alterations in lipid content; of the 275 studied lipids, only 24 were significantly affected by the absence of expression of *Abca7* [83]. Among them, 12 subspecies in ethanolamine, three in phosphoglycerol, one in lysophosphatidylcholine, and two in shingomyelin were lower in brains of *Abca7*−/− mice. Three subspecies in phosphatidylcholine, one in ceramide, three in sulfatide, and one in cerebroside were increased, thus suggesting that *Abca7* deficiency may significantly affect cerebral lipid metabolism.

The same study reported deficiencies in spatial memory in *Abca7*−/− 20- to 22-monthold male and female mice [83] but these deficiencies were only observed in 19- to 20-weekold females in another study performed by Logge et al. [84]. In the same study, males and females displayed an altered novel object recognition but this was more pronounced in females [84]. No difference between genders or with wild type mice was measured when a battery of tests for behavior was realized. Indeed, *Abca7*−/− mice showed the same sensory abilities, neurological reflexes, motor functions, anxiety, spatial learning and shortterm memory as the wild-type mice [84]. No significant role in neurogenesis or neuron proliferation has been observed in another study in which only 8.5-month-old males were studied [85]. All these data suggest that ABCA7 might play a minor role in behavioral domains, but again further studies considering mouse strains, ages, sex or methodology are necessary to elucidate the role of ABCA7 in brain development and behaviors.

In humans, it was demonstrated more recently in brain samples that levels of ABCA7 modestly but significantly decrease with normal aging [23]. A significant association between ABCA7 SNP (rs3764650) and cognitive decline was only observed in females in a longitudinal study including 3267 females and 3026 males [70].


**3. Methodology**

This study aims to determine a human factor index for ergonomic workplace assessment in automotive industries, as shown in Figure 1.

**Figure 1.** Proposed research scheme for ergonomic assessment.

A total of 379 documents were obtained from the core collection database on 30 March 2020. Of the 379 articles, 365 were journal articles, and 14 were review articles. A literature review of workplace ergonomic assessments and expert opinions obtained from the automotive industry enabled us to identify the list of factors to be considered in this study. The factors, criteria, and variables to be included in the ergonomic measurement model were carefully selected. ISM was used to identify and describe the relationships among the factors. The comprehensive model constructed from ISM was verified using partial least squares (PLS)-based SEM. SEM-based statistical analyses do not allow for a comprehensive inference of the interconnectivity relationship. Therefore, to determine the direction of the relationships among factors, ISM was used. In this study, SEM was applied to a first-order four-factor structure for fifteen performance variables. After the theoretical model was verified using SEM, an ergonomic quantification tool was developed using the multigrade fuzzy approach. A demonstration of the proposed model in a workplace ergonomic measurement study was carried out in the Indian automotive industry. The computation of the human factor index facilitates evaluation of the "ergonomic conducive level" of the industry.