*2.2. Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI)*

Two indicators were used to determine droughts: the Standardized Precipitation Index (SPI), which defines meteorological droughts, and the Standardized Runoff Index (SRI), which defines hydrological droughts. Data from 8 meteorological stations: 5 located within the catchment area of the Note´c River (Pako´s´c, Strzelno, Sompolno, Izbica Kujawska, Kołuda Wielka) and 3 in its close vicinity (Kłodawa, Gniezno and Janowiec Wielkopolski) were used to determine meteorological droughts.

The Standardized Precipitation Index (SPI) is one of the most frequently used indicators of a meteorological drought, and was developed on the basis of the normalization of precipitation probabilities [60]. This indicator defines a precipitation deficit and allows the monitoring of droughts in different time frames. The SPI is recommended by the World Meteorological Organisation (WMO) for determining the phenomenon of drought [61]. For more information on the formulation of SPI, its advantages and limitations, see papers [62–64].

The SPI was calculated on the basis of monthly precipitation totals for 5 meteorological stations in the upper catchment area of the Note´c River (Izbica Kujawska, Strzelno, Kołuda Wielka, Sompolno and Pako´s´c) and 3 stations in its close vicinity (Kłodawa, Gniezno and Janowiec Wielkopolski). The daily precipitation data were aggregated into monthly time scales, and fitted to a two-parameter gamma distribution function. The SPI was calculated for each month at different timescales. Therefore, 5 different time series were analysed, i.e., 1-, 3-, 6-, 9- and 12-months. SPI values define the deviation from the median expressed in units of standard deviation, which was calculated according to the formula:

$$SPI\_{\prime} \; SRI = \frac{f(\mathbf{x}) - \mathbf{u}}{\sigma} \tag{1}$$

*SPI*, *SRI*—Standardized Precipitation Index, Standardized Runoff Index

*f*(*x*)—transformed sum of precipitation, discharges

*μ*—mean value of the normalized index *x*

*σ*—standard deviation of index *x*

In order to calculate the SPI, the compliance of the distribution of the transformed variable *f*(*P*) with the normality distribution was tested using the *x*2—Pearson normality test [65].

The Standardized Runoff Index (SRI) is calculated according to the same procedure as the SPI, however it is based on the discharge data [66–69]. A 2-parameter logarithmic function was used as a normalizing function when calculating the SRI [70]. The detailed calculation method is presented in the study [9,47]. The probabilities were transformed into standard normal distribution. The application of SPI allows for the differentiation of the intensity of a drought using a set of SPI thresholds: −1, −1.5, −2 and 1.0, 1.5, 2 for moderate, severe and extreme droughts and precipitations, respectively [28].

The proposed approach is based on the assessment of water resources under different hydroclimatic conditions and the determination of different intensity classes. The adoption of standardized indicators allowed for the classification of drought intensity, which is presented in Table 3. Extreme events were identified for the indicator values above 1.0, when rainy periods occur, and for the indicator values below −1.0, when there are droughts.

**Table 3.** The classification scale for SPI and SRI values.


**1. Introduction**

The production of electric energy with renewable energy sources (RES) and the electrification of energy use are crucial for energy transition, which is the ongoing process to reduce the use of fossil fuels with the decarbonization, decentralization, and digitalization of the energy sectors. In this context, the European Union (EU) launched an ambitious plan to cut emissions in the atmosphere by harvesting the energy from wind and solar and fostering a profound transformation of heating and transportation sectors [1].

From the power system perspective, the non-programmability of energy production (i.e., mostly based on intermittent sources) is making it tough to comply with the required adequacy and security levels. Furthermore, since a significant amount of renewable energy currently comes from small to medium-size power plants connected to the distribution system (e.g., photovoltaic power plants), the energy transition is profoundly impacting the distribution system. The high share of distributed generation (DG) is already

causing temporary voltage regulation and power congestion problems in distribution networks that are destined to become more frequent with the progress of the energy transition. The active management (AM) of distribution networks allows fixing such operational issues with the use of flexibility offered by generation and consumption, that is referred to as no-network solutions in opposition to the traditional paradigm of network solutions. As a consequence, no-network options have to be included in distribution planning as a new development option [2–4]. The usage of flexibility can increase the hosting capacity of the network and, as a consequence, the deferment of infrastructural investments. To foster the use of flexibility, the EU Member States now have to allow the distribution system operators (DSOs) to procure flexibility products with transparent, non-discriminatory, and market-based procedures for the operation and development of the distribution system [5]. Because of the expected more extensive exploitation of flexibility for the operation of the distribution network, DSO and transmission system operators (TSO) will have to cooperate, by exchanging all necessary information to ensure the use of flexibility, guarantee an efficient operation of the whole system, and facilitate market development [6]. Indeed, the "local" flexibility, offered by the distributed energy resources (DERs) for the operation of the distribution system, might be useful also for "system" services, needed by the TSO for guaranteeing safe and secure operation of the whole power system. It is clear that DSOs cannot completely rely on the flexibility in their networks, and the level of uncertainty might be considerably high.

Battery energy storage systems (BESS) are a technical choice to increase flexibility and reduce the level of uncertainty. They can be locally employed for RES integration, load peak flattening, voltage regulation, efficiency improvement, solving power congestions, etc., BESS can offer frequency regulation services that are crucial with the progressive drop of the system inertia consequent to the diffusion of non-rotating generators or power electronics interfaced generators. The reduction of the system inertia may cause critical frequency deviations even during small active power imbalances [7].

BESS can foster the market of flexibility products in the early stage of implementation. In fact, pioneering projects implementing flexibility markets involving both TSOs and DSOs showed that the sourcing of flexibility is a critical element, mostly at the early phases of the market development, when aspects like flexibility product cost, provider participation, and availability of flexibility products represent a potential risk for the distribution network management. This uncertainty could be reduced by BESS that represent an additional resource when the number of available RES is too low to ensure effective competition in the area where suitable facilities for the provision of the services are located, avoiding the increase of the costs.

Moreover, since also TSOs are procuring the flexibility in the market, in the paper, it is proposed a decision-making process that allows the evaluation of the share of flexibility to be reserved to each player, enabling a stronger coordination between local and system objectives and needs. The BESS cost is obviously a crucial element of the decision-making process that has been considered in the paper. In fact, despite BESS having a huge cost-reduction potential, BESS cost is still a feature that impacts on the success of business models depending on the regulatory framework. In [8], the authors proposed a multi-objective (MO) approach for simultaneously optimizing the size, the position, and the operation profile of BESS called to offer services to the DSOs, without monetizing the benefits that are not naturally expressed with currency (e.g., improvement of voltage profile or benefits related to the environmental protection). That paper considered the point of view of the DSOs that, under specific conditions, can be allowed to own and manage BESS. This paper makes another step forward, combining the needs of BESS owners and DSOs. Taking into account the advocated cooperation between TSO and DSO, an instrument is proposed that allows the evaluation of the availability of the flexibility product for both the systems' operators, without disregarding the BESS owner point of view. The BESS owners aim at increasing their incomes from arbitrage practice and from the provision of ancillary services to both the system operators (i.e., DSO and TSO), while the DSO aims at reducing the residual risk of technical constraints violation by promoting the

use of flexibility products offered by the BESS owners. In particular, for considering both the interests, the capacity and the power of the BESS are shared between the percentage dedicated exclusively to support the DSO operation and the remaining quote available for arbitrage or other services. The specific service of frequency regulation support offered by BESS to the TSO and the relevant economic benefits for the BESS owners are explicitly considered in the proposed optimization.

The paper is organized as follows. In Section 2, the proposed methodology is described underlying the novelties and the improvements in respect to the one proposed in [8]. In Section 3, the approach is validated and discussed through a case study that applies the methodology to a real Italian distribution network. Finally, in Section 4, some concluding remarks are presented.

#### **2. Multi-Objective Optimization for Optimal Exploitation of BESS**

A large number of papers have been published on the use of BESS in power distribution systems, analyzing different models and methods to enhance the optimal network planning [9–11]. Studies that include BESS (as well as demand response actions, characterized by possible recover of the curtailed energy) are more complicated due to time intercorrelations, since the BESS energy scheduling in one hour is subject to the charging/discharging cycle implemented in the previous hours [12]. Consequently, the decision-making problem may become complicated to be solved because planning alternatives can excessively grow in number. Traditional numerical methods like non-linear programming, dynamic programming, and mixed-integer linear programming have shortcomings if applied on large and complex distribution systems. On the contrary, meta-heuristic algorithms, like particle swarm (PS), Tabu search (TS), evolutionary algorithm (EA), and genetic algorithm (GA), can provide near-optimal solutions for complex, large-scale planning problems, like the one faced in this paper [9]. In [13–15], the PS optimization is used for the optimal allocation of BESS in the distribution system. In [16], the use of an EA for determining the capacity of BESS in an islanded microgrid, considering both steady-state and dynamic constraints, is proposed. The problem is formulated as an MO optimization that involves the dynamic equations of the power system, to improve reliability, stabilizing transients and reducing load shedding. A significant number of papers used the GA [17] or a combination of GA with other optimization techniques like PS, linear programming in [18] or quadratic programming in [19].

It is worth noting that, despite the high interest related to the cooperation between DSO and TSO in the provision of the ancillary services, in literature, few publications have addressed the optimal size and location of BESS for the assessment of flexibility products to be shared between DSO and TSO. Moreover, most of them analyze the voltage support and peak shaving, while very few analyze the possible frequency service [20]. Regarding frequency support, such papers are related to high voltage (HV) networks, islanded networks (microgrid or island) or examine the aggregation of DERs (usually represented as virtual power plant, VPP) [21–23]. For instance, in [23] clusters of electric vehicles (EVs) are grouped together as a VPP to provide fast frequency reserve service to the transmission system through the DSO whilst considering network unbalance. Most of the papers on optimal location and size analyze a specific voltage level or the two systems independently without considering the possible services for the other level and a wide range of contingencies (or the worst-case scenario) are considered [24].

Compared to the literature, the proposed paper presents an advancement for several reasons. First of all, moving in the direction of the cooperation between DSO and TSO, different grid services (i.e., arbitrage, frequency containment reserve (FCR), and automatic frequency restoration reserve (aFRR)), that medium voltage (MV) BESS can sell through the services market are considered. Moreover, the planning strategies are not developed taking into account only critical days but typical daily profiles, indicative of the seasonal behavior of loads and generators during a year, are used. Such choice is in agreement with the recommendations of the main international scientific organizations (CIGRE, CIRED, IEEE, etc.), that

recognize the unsuitability of the traditional deterministic distribution planning approaches, based on the aim of fulfilling the extremely rare operating conditions, which could lead to an unsustainable amount of network investments. In addition, the methodology proposed is able to deal with all the uncertainties related to RES and to consider distinctly the risk associated to any planning decision.

Generally, the optimal BESS exploitation requires to simultaneously take account of multiple goals. Thus, another critical aspect for the optimal siting and sizing of BESS in a distribution network is related to the definition of a unique financial objective function, because some benefits are not directly monetizable without adopting subjective assumptions that can produce biased results. In this context, MO programming permits a more transparent and impartial decision process and can be used for financial purposes by decision-making teams of companies or for socio-economic studies by regulators for defining fair rules [16, 17,19]. Evolutionary algorithms are well suited to solve optimization problems that are characterized by many contrasting objectives. Differently by other more conventional optimization methods, like the weighted linear combination or the ε-constraint, that need to perform several separate runs, the evolutionary algorithms can simultaneously deal with many candidates that form sets of solutions (named population) and find the optimal ones belonging to the Pareto set by performing the algorithm one time only. Furthermore, the Pareto front shape and its continuity have a small impact on the evolutionary algorithms results [25].

For these reasons, this paper implements a full MO optimization procedure based on a real coded non-dominated sorting genetic algorithm-II (NSGA-II) algorithm. This methodology has been chosen among meta-heuristic algorithms because it is recognized to be an efficient and robust technique, capable of generating good trade-off solutions for a wide range of optimization problems [26]. The real codification has been implemented by the authors in [8] to better deal with the continuous nature of some variables in optimization problems that include BESS exploitation. The main novelties of this paper, compared to [8], are the definition of new objective functions (especially the risk of technical constraint violations in a given electric distribution network) and the relative adaptations of the solution coding. Indeed, in [8], only the DSO point of view was considered, and the MO approach was used to avoid the a priori monetization of the benefits and perform the cost-benefit analyses of BESS allocation plans proposed by the DSO. Instead, in this paper, two stakeholders have been considered, the DSO and the BESS private owners, in order to find trade-off solutions between their contrasting goals.


**1. Introduction**

Due to the low efficiency and toxicity of current medications for Parkinson's disease treatment, interest is growing in phytochemicals as a potential treatment option. Phytochemicals with neuroprotective activity target various pathways due to their antioxidant, anti-inflammatory, and antiapoptotic properties [1]. *Mucuna pruriens* Linn. is a leguminous plant growing spontaneously in tropical and subtropical areas worldwide. The seeds have traditionally been used in India as a nerve tonic, and a male virility enhancement [2]. In

**Citation:** Kamkaen, N.; Chittasupho, C.; Vorarat, S.; Tadtong, S.; Phrompittayarat, W.; Okonogi, S.; Kwankhao, P. *Mucuna pruriens* Seed Aqueous Extract Improved Neuroprotective and Acetylcholinesterase Inhibitory Effects Compared with Synthetic L-Dopa. *Molecules* **2022**, *27*, 3131. https://doi.org/10.3390/ molecules27103131

Academic Editors: Nour Eddine Es-Safi and Celestino Santos-Buelga

Received: 15 April 2022 Accepted: 11 May 2022 Published: 13 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

addition, the pods have anthelmintic activity, and the seeds have anti-inflammatory activity. Powdered seeds possess anti-parkinsonism properties, possibly due to the existence of L-dopa, which is a precursor of the neurotransmitter dopamine. The dopamine content in brain tissue is reduced when the conversion of tyrosine to L-dopa is blocked. L-Dopa can cross the blood–brain barrier and undergoes conversion to dopamine, restoring neurotransmission [3]. Particularly, the hydro-alcoholic extract of *M. pruriens* seeds gave high yields of L-dopa, using ascorbic acid as a protector [4]. Surprisingly, n-propanol extract of *M. pruriens* seeds that contained a small amount of L-dopa, yielded the highest reaction for the growth and survival of dopaminergic culture neurons [2]. The significant neuroprotective activity of n-propanol extracts suggested that a whole extract could be potential for the treatment of Parkinson's disease [5].

Parkinson's disease is characterized by signs of major oxidative stress and mitochondrial damage in the pars compacta of the substantia nigra [6]. The previous study suggested that reactive oxygen species (ROS) played an important role in age-related neurodegenerative changes including Parkinson's disease [7]. Interestingly, the ethyl acetate and methanolic extract of the whole *M. pruriens* plant exhibited high antioxidant and free radical scavenging activities [2]. These in vitro assays indicated that the whole plant extract contained large amounts of phenolic compounds, which may be useful in preventing various oxidative stresses. Furthermore, it has been reported that methanolic extracts of *M. pruriens var.utilis* leaves have numerous biochemical and physiological activities, and contain pharmaceutically valuable compounds [8]. However, the in vitro neuroprotective activity of the *M. pruriens* seed aqueous extract obtained from the traditional extraction method has never been reported.

Although the first-line therapy for Parkinson's disease is L-dopa, long-term L-dopa use results in the development of significant clinical complications. L-dopa-induced dyskinesia (LID) and abnormal involuntary movements (AIMs) normally occur in a vast majority of Parkinson's disease patients with repeated administration of L-dopa [9]. AIMs occurring at the head, trunk, and extremities affect the function of daily living, and patients can become debilitated. These side effects are common with 30% incidence after 2 years of L-dopa administration. The incidence increased up to 40% and 90% for 5- and 10-year-treatment, respectively [10]. Parkinson's patients who early started high doses of levodopa are known to have risk factors for LID [11]. At present, few therapeutic options are available for the treatment of LIDs. Several strategies have been proposed to reduce these side effects including postponing the initiation of L-dopa dosing, adjusting the dose of L-dopa, and combining it with other drugs [12,13]. However, these strategies resulted in a decrease in the efficacy of L-dopa in the treatment of Parkinson's disease.

The seed of *M. pruriens* has been widely investigated for its pharmacological properties including Parkinson's disease because it contains significant amounts of L-dopa. The advantages of natural L-dopa in *M. pruriens* seed extract over the synthetic forms have been reported. The natural L-dopa is less toxic [14]. It has a shorter onset of action, but it has a longer therapeutic effect, which could delay the need for combination therapy [14–16]. In this study, we quantified the amount of L-dopa, total phenolic content, and total flavonoid contents in freeze-dried *M. pruriens* seed extract obtained from an aqueous extraction method. The cytotoxicity and neuroprotective activities of the freeze-dried extract were investigated in neuronal cells using two models. The effects of L-dopa and *M. pruriens* on acetylcholinesterase enzyme activity were studied.


*1.2.1 Classification of WAAM*

The WAAM is a hot developing topic yet and is not classified so far clearly. But in this text, an attempt has been given for the classification of WAAM processes based on the familiar arc welding processes alone employed for stainless steels and proved to be fit for WAAM. Therefore, WAAM is here classified into,

Selection of the welding process is based on the operator's requirement. For example, if the priority is given to a high deposition rate, GMAW process may be selected compared to GTAW even if the quality and process stability are comparatively less. If the operator is looking for an electric arc with very high energy density then PAW may be preferred that actually enables increased travel speeds and good quality weld deposits with minimum weld distortion [8].


**1. Introduction**

In recent years, deep neural networks (DNNs) have been used very effectively in a wide range of applications. Since these models have achieved remarkable results, redefining state-of-the-art solutions for various problems, they have become the "go-to solution" for many challenging real-world problems, e.g., object recognition [1,2], object segmentation [3], autonomous driving [4], automatic text translation [5], cybersecurity [6–8], credit default prediction [9], etc.

Training a state-of-the-art deep neural network requires designing the network architecture, collecting and preprocessing data, and accessing hardware resources, in particular graphics processing units (GPUs) capable of training such models. Additionally, training such networks requires a substantial amount of trial and error. For these reasons, such trained models are highly valuable, but at the same time, they could be the target of attacks by adversaries (e.g., a competitor) who might try to duplicate the model and the entire sensitive intellectual property involved without going through the tedious and expensive process of developing the models by themselves. By doing so, all the trouble of data

**Citation:** Mosafi, I.; David, E.; Altshuler, Y.; Netanyahu, N.S. DNN Intellectual Property Extraction Using Composite Data. *Entropy* **2022**, *24*, 349. https://doi.org/10.3390/ e24030349

Academic Editor: Stanisław Drozd˙ z˙

Received: 29 December 2021 Accepted: 21 February 2022 Published: 28 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

collection, acquiring computing resources, and the valuable time required for training the models are spared by the attacker. As state-of-the-art DNNs are used more extensively in real-world products, the prevalence of such attacks is expected to increase over the next few years.

An attacker has two main options for acquiring a trained model: (1) acquiring the raw model from the owner's private network, which would be a risky criminal offense that requires a complicated cyber attack on the owner's network; and (2) training a student model that mimics the original mentor model. That is, the attacker could query the original mentor using a dataset of samples and train the student model to mimic the output of the mentor model for each of the samples. The second option assumes that the mentor is a black box, i.e., there is no knowledge of its architecture, no access to the training data used for training it, and no information regarding the trained model's weights. We only have access to the model's predictions (inference) for a given input. Thus, such a mentor would effectively teach a student how to mimic it by providing its output for different inputs.

In order for mimicking to succeed, a key element is to utilize the certainty level of a model on a given input, i.e., its softmax distribution values [10,11]. This knowledge is highly important for the training of the student network. For example, in case of a binary classification, classifying an image as category *i* with 99% confidence and as category *j* with 1% confidence is much more informative than classifying it to category *i* with, say, 51% confidence and to category *j* with 49% confidence. Such data are valuable and often much more informative than the predicted category alone, which in both cases is *i*. This confidence value (obtained through the softmax output layer) also reveals how the model perceives this specific image and to what extent the predictions for categories *i* and *j* are similar. In order to protect against such a mimicking attack, a trained model may hide this confidence information by simply returning only the index with the maximal confidence, without providing the actual confidence levels (i.e., the softmax values are concealed, while the output contains merely the predicted class). Although such a model would substantially limit the success of a student model using a standard mimicking attack, we provide in this paper a novel method by querying the mentor with *composite* images, such that the student effectively elicits the mentor's knowledge, even if the mentor provides the predicted class only.

Contributions: This research possesses various contributions to the domain of DNN intellectual property extraction.

The rest of the paper is organized as follows. Section 2 reviews previous methods used for network distilling and mimicking. Section 3 describes our new approach for a successful mimicking attack on a mentor, which does not provide softmax outputs. Section 4 presents our experimental results. Finally, Section 5 makes concluding remarks. This paper is based on a preliminary version published in [12].


*4.3.2 Results*

 Different exposure durations displayed significantly different levels of HSP 70 mRNA compared to the normal. This increase in mRNA was 14% at day 10 and became 39% at day 15 (**Figure 12**). The amount of HSP70 protein has increased with longer exposure time although it did not prove strong correlation with the

**Figure 12.**  *mRNA for HSP70 for liver tissue at day 10 and day 15 in the control and experimental groups.*

**Figure 13.**

*ELISA graphs showing increase in the cytosolic HSP70 in the control and exposed groups in liver at day 10 and day 15.*

expressed mRNA quantities (**Figure 13**). The total cellular protein represented by the cytoplasmic protein concentration was increased in treated samples than in untreated samples demonstrating cellular mechanism in attempting to express housekeeping genes and HSP70 for survival. Organs have shown differences. Our results indicate that HSP 70 mRNA is over expressed in an attempt to synthesize more of the HSP70 in order to resist electromagnetic waves irradiation.


*3.3. 24-Epibrasinolide and Titanium Oxide Nanoparticles Increase Nonenzymatic Antioxidant Activities (Flavonol, Tocopherol, and Total Phenolics) in Bamboo Species under Cu and Cd Toxicity*

The effects of TiO2 NPs and EBL concentrations on nonenzymatic activity (flavonol, tocopherol, and total phenolics) in the bamboo species revealed a significant difference between the co-application of 24-epibrassinolide and titanium oxide nanoparticles with Cu and Cd (*p* < 0.001). According to the results, the combination of TiO2-HMs and EBL-HMs significantly increased nonenzymatic antioxidant activities in our bamboo species. However, the greatest increase in nonenzymatic activity under heavy metal stress was related to the combination of TiO2 –EBL with Cu and TiO2–EBL with Cd, with a 1.55-fold and 1.51-fold enhancement in flavonols, 1.53-fold and 1.51-fold enhancement in tocopherols, and 1.68-fold and 1.58-fold increase in total phenolics, respectively, in comparison with the control treatment (Figure 3). Conversely, the concentrations of 100 μM Cu and 100 μM Cd clearly reduced nonantioxidant activity, as demonstrated by a 21% and 23% reduction in flavonols, 12% and 24% reduction in tocopherols, and 34% and 28% reduction in total phenolics, respectively, in comparison with the control treatment. We suggest that the combination of TiO2 and EBL has a positive impact on the reduction in heavy metal toxicity by stimulating nonenzymatic antioxidant activities (flavonols, tocopherols, and total phenolics).

**Figure 2.** The impact of the co-application of 24-epibrassinolide and titanium oxide nanoparticles individually and combined on malondialdehyde content (MDA) (**a**), hydrogen peroxide (H2O2) (**b**), superoxide radical (O2 •−) (**c**), soluble proteins (SP) (**d**), and electrolyte leakage (EL) (**e**) in bamboo species (*Pleioblastus pygmaeus*) with 100 μM Cu and 100 μM Cd. In this study, 1-year-old branches of

*P. pygmaeus* were used as plant treatments together with 100 μM TiO2 NPs and 10−8 M 24-epibrassinolide, individually and in combination with 100 μM Cu and 100 μM Cd using four replications. Planting of the treated bamboo was performed in an Air Tech inoculation hood with fluorescent white lamps and ultraviolet light (wavelengths of 10–400 nm) at 15 ◦C and 30 ◦C. The bamboo plants were constantly exposed to excess heavy metals for three weeks. Sampling for the measurement of MDA, H2O2, O2 •−, SP, and EL (**a**–**e**) was conducted after three weeks of plant exposure to the coapplication of 24-epibrassinolide and titanium oxide nanoparticles under 100 μM Cu and 100 μM Cd. The capital letters (A–C) indicate significant differences between treatments of control (C), titanium (Ti), 24-epibrassinolide (EBL), and 24-epibrassinolide involving individual or combined application of titanium oxide nanoparticles (EBL–TiO2 NPs) under 100 μM Cu and 100 μM Cd (the bars with similar colors), while the lowercase letters (a–c) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs, individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey s test (*p* < 0.05).

**Figure 3.** The impact of the co-application of 24-epibrassinolide and titanium oxide nanoparticles individually and combined on nonenzymatic antioxidant activities ((**a**) flavonols, (**b**) tocopherols, (**c**) total phenolics) in bamboo species (*Pleioblastus pygmaeus*) with 100 μM Cu and 100 μM Cd. In this study, 1-year-old branches of P. *pygmaeus* were used as plant treatments together with 100 μM TiO2 NPs and 10−8 M 24-epibrassinolide, individually and in combination with 100 μM Cu and 100 μM Cd,

using four replications. Planting of the treated bamboo was performed in an Air Tech inoculation hood with fluorescent white lamps and ultraviolet light (wavelengths of 10–400 nm) at 15 ◦C and 30 ◦C. The bamboo plants were constantly exposed to excess heavy metals for three weeks. Sampling for the measurement of flavonols, tocopherols, and total phenolics (**a**–**c**) was conducted after three weeks of plant exposure to the co-application of 24-epibrassinolide and titanium oxide nanoparticles under 100 μM Cu and 100 μM Cd. The capital letters (A–C) indicate significant differences between treatments of control (C), titanium (Ti), 24-epibrassinolide (EBL), and 24-epibrassinolide involving individual or combined application of titanium oxide nanoparticles (EBL–TiO2 NPs) under 100 μM Cu and 100 μM Cd (the bars with similar colors), while the lowercase letters (a,b) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs, individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey s test (*p* < 0.05).

#### *3.4. 24-Epibrassinolide and Titanium Oxide Nanoparticles Improve Photosynthetic Pigments and Fluorescence Parameters in Bamboo Species under Cu and Cd Toxicity*

Photosynthetic pigments and fluorescence parameters are important indices in the evaluation of photosynthetic efficiency in different species of plants under stress conditions. The indicators of plant photosynthesis performance, including photosynthetic pigments (Chl a, Chl b, and total Chl, as well carotenoid contents), and fluorescence indices, including the maximum photochemical efficiency of PSll (Fv/Fm), photochemical quenching coefficient (qP), effective photochemical efficiency of PSll (Fv /Fm ), actual photochemical efficiency of PSll (ϕPSll), and nonphotochemical quenching (NPQ), were measured. We found a significant difference between the co-application of 24-epibrassinolide and titanium oxide nanoparticles with Cu and Cd (*p* < 0.001). Based on the results, the levels of TiO2NPs and EBL alone and in combination with heavy metals (Cu and Cd) could increase photosynthetic pigments in bamboo under Cu and Cd. However, the greatest increase in photosynthetic pigments under Cu and Cd was attributed to the co-application of TiO2–EBL with Cu and the co-application of TiO2–EBL with Cd, which resulted in 21% and 17% increases in Chl a, 85% and 83% increases in Chl b, 42% and 38% increases in total Chl, and 46% and 39% increases in carotenoid, respectively, in comparison with their control treatments (Table 2). Conversely, the measurement of the fluorescence parameters demonstrated a significant difference between the combination of TiO2–EBL and Cu and Cd (*p* < 0.001). The data analysis revealed similar results, e.g., in the Chl and carotenoid contents and in the measurement of fluorescence parameters. Therefore, the greatest increase in fluorescence parameters was related to the combination of TiO2 and EBL, which resulted in a 50% increase in the maximum photochemical efficiency of PSll (Fv/Fm), 41% increase in the photochemical quenching coefficient (qP), 54% increase in the effective photochemical efficiency of PSll (Fv /Fm ), 56% increase in the actual photochemical efficiency of PSll (ϕPSll), and 58% increase in nonphotochemical quenching (NPQ) in comparison with their control treatments. We suggest that the combination of TiO2 NPs and EBL has a strong ability to increase photosynthesis parameters in plants exposed to heavy metal stress (Cu and Cd) (Figure 4).

**Table 2.** The effect of the co-application of 24-epibrassinolide and titanium oxide nanoparticles individually and combined on photosynthetic pigments (Chl a, Chl b, and total Chl, as well as carotenoid contents) in bamboo species (*Pleioblastus pygmaeus*) with 100 μM Cu and 100 μM Cd.

In this study, 1-year-old branches of *P. pygmaeus* were used as plant treatments together with 100 μM TiO2 NPs and 10−8 M 24-epibrassinolide individually and in combination with 100 μM Cu and 100 μM Cd using four replications. Planting of the treated bamboo was performed in an Air Tech inoculation hood with fluorescent white lamps and ultraviolet light (wavelengths of 10–400 nm) at 15 ◦C and 30 ◦C. The bamboo plants were constantly exposed to excess heavy metals for three weeks. Sampling for the measurement of photosynthesis pigments was conducted after three weeks of plant exposure to the co-application of 24-epibrassinolide and titanium oxide nanoparticles under 100 μM Cu and 100 μM Cd. The capital letters (A,B) indicate significant differences between treatments of control (C), titanium (Ti), 24-epibrassinolide (EBL), and 24-epibrassinolide with titanium oxide nanoparticles (EBL–TiO2 NPs) individually or in combination with 100 μM Cu as well as 100 μM Cd (the bars with similar colors), while the lowercase letters (a,b) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey s test (*p* < 0.05).

#### *3.5. 24-Epibrasinolide and Titanium Oxide Nanoparticles Reduce Heavy Metal Accumulation in Bamboo Leaves, Stems, and Roots*

The decrease in metal accumulation in different types of plants is one of the main mechanisms responsible for the reduction of metal toxicity and increase in plant resistance when exposed to oxidative stress. Our results showed that TiO2 and EBL had a positive impact on the reduction in heavy metal concentrations in bamboo species; therefore, TiO2 and EBL alone or in combination significantly reduced heavy metal accumulation in leaves, stems, and roots (Table 3). This phenomenon is related to the role of TiO2 as a physical barrier that leads to a reduction in metal translocation from roots to aerial parts. The roots in plants are typically the first contact points where exposure to heavy metals occurs. Therefore, root physical traits are extremely decisive in limiting metal entry into plants. The root-based cellular layers comprised of epiblema, endodermis, and exodermis form apoplastic barriers in the roots, which can restrict heavy metal uptake by plants. We hypothesized that TiO2 NPs, through strengthening the apoplastic barriers in the roots and enhancing their impermeability, can significantly diminish the uptake of heavy metals. On the other hand, TiO2 NPs with high adsorption capacity act as an efficient binder of metal ions. Hence, TiO2 NPs can restrain the movement of heavy metals within the extracellular or intercellular parts of roots, thereby restricting heavy metal translocation from the root to shoot. TiO2 NPs may also have the ability to influence the expansion of the epidermal layer in plants, preventing heavy metal accumulation in nonphotosynthetic tissues by providing additional physical resistance. Conversely, we indicated that EBL, a hormone that is involved in plant growth regulation alone and in combination with TiO2, plays a positive role in the stimulation of antioxidant activity, which can scavenge ROS components in plant organs and inhibit plant oxidative stress caused by heavy metal toxicity. As shown in Table 3, the combination of TiO2–EBL with heavy metals showed the greatest reduction in heavy metal accumulation in the leaves, stems, and roots of bamboo species (Table 3). We suggest that TiO2 and EBL can reduce heavy metal contents in plant leaves, stems, and

roots, thus demonstrating that the combination of TiO2 and EBL has the greatest impact on the decrease in metal toxicity.

**Figure 4.** The effect of the co-application of 24-epibrassinolide and titanium oxide nanoparticles individually and combined on fluorescence parameters, including the maximum photochemical efficiency of PSll (Fv/Fm) (**a**), effective photochemical efficiency of PSll (Fv /Fm ) (**b**), photochemical quenching coefficient (qP) (**c**), actual photochemical efficiency of PSll (ϕPSll) (**d**), and nonphotochemical quenching

(NPQ) (**e**) in bamboo species (*Pleioblastus pygmaeus*) with 100 μM Cu and 100 μM Cd. In this study, 1-year-old branches of *P. pygmaeus* were used as plant treatments together with 100 μM TiO2 NPs and 10−8 M 24-epibrassinolide individually and in combination with 100 μM Cu and 100 μM Cd using four replications. Planting of the treated bamboo was performed in an Air Tech inoculation hood with fluorescent white lamps and ultraviolet light (wavelengths of 10–400 nm) at 15 ◦C and 30 ◦C. The bamboo plants were constantly exposed to excess heavy metals for three weeks. Sampling for the measurement of fluorescence parameters (**a**–**e**) was conducted after three weeks of plant exposure to the co-application of 24-epibrassinolide and titanium oxide nanoparticles under 100 μM Cu and 100 μM Cd. The capital letters (A–C) indicate significant differences between treatments of control (C), titanium (Ti), 24-epibrassinolide (EBL), and 24-epibrassinolide involving individual or combined application of titanium oxide nanoparticles (EBL–TiO2 NPs) under 100 μM Cu and 100 μM Cd (the bars with similar colors), while the lowercase letters (a–c) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey s test (*p* < 0.05).

**Table 3.** The accumulation concentrations of titanium oxide nanoparticles and corresponding heavy metals (Cu and Cd) in bamboo shoots, stems, and roots.

In this study, 1-year-old branches of *P. pygmaeus* were used as plant treatments together with 100 μM TiO2 NPs and 10−8 M 24-epibrassinolide individually and combined with 100 μM Cu and 100 μM Cd using four replications. The capital letters (A–D) indicate significant differences between treatments of control (C), titanium (Ti), 24 epibrassinolide (EBL), and 24-epibrassinolide with titanium oxide nanoparticles (EBL–TiO2 NPs) individually or in combination with 100 μM Cu as well as 100 μM Cd (the bars with similar colors), while the lowercase letters ( a–c) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey's test (*p* < 0.05). 2–6 24-Epibrasinolide and titanium oxide nanoparticles increase plant biomass indices (root and shoot dry weight) and plant growth (length of shoot) in bamboo species under Cu and Cd toxicity.

To evaluate the plant growth rate under Cu and Cd toxicity, the plant biomass, including root and shoot dry weight, as well as the length of shoots, were measured. A significant difference was found for the co-application of TiO2–EBL and heavy metals (*p* < 0.001) (Figure 5). Therefore, TiO2 and EBL individually and in combination significantly increased plant growth and biomass under stressful conditions. Based on this result, the greatest increase in plant biomass and growth under heavy metal exposure was related to the combination of TiO2 and EBL, which resulted in a 21% increase in the dry weight of shoots, a 23% increase in the dry weight of roots, and a 19% increase in the length of shoots in comparison with their control treatments (Table 4). Conversely, the measurements showed that the lowest plant growth was recorded under 100 μM Cu and 100 μM Cd, which resulted in 0.5 g and 0.46 g dry weights of shoots, 0.59 g and 0.53 g dry weights of roots, and 10.04 cm and 9.21 cm shoot lengths, respectively (Table 4).

**Figure 5.** The impact of the co-application of 24-epibrassinolide and titanium oxide nanoparticles individually and combined on the dry weight of shoots (**a**), dry weight of roots (**b**), and shoot length (**c**) of bamboo species (*Pleioblastus pygmaeus*) with 100 μM Cu and 100 μM Cd. In this study, 1-year-old branches of *P. pygmaeus* were used as plant treatments together with 100 μM TiO2 NPs and 10−8 M 24 epibrassinolide individually and combined with 100 μM Cu and 100 μM Cd through four replications. Planting of the treated bamboo was performed in an Air Tech inoculation hood with fluorescent white lamps and ultraviolet light (wavelengths of 10–400 nm) at 15 ◦C and 30 ◦C. The bamboo plants were constantly exposed to excess heavy metals for three weeks. Sampling for the measurement of biomass indexes and plant growth (**a**–**c**) was conducted after three weeks of plant exposure to the co-application of 24-epibrassinolide and titanium oxide nanoparticles under 100 μM Cu and 100 μM Cd. The capital letters (A–C) indicate significant differences between treatments of control (C), titanium (Ti), 24-epibrassinolide (EBL), and 24-epibrassinolide involving individual or combined application of titanium oxide nanoparticles (EBL–TiO2 NPs) under 100 μM Cu and 100 μM Cd (the bars with similar colors), while the lowercase letters (a–c) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey s test (*p* < 0.05).

**Table 4.** The changes in bamboo biomass in root and shoot dry weight as well as shoot length with 24-epibrassinolide and titanium oxide nanoparticles individually or combined with 100 μM Cu and 100 μM Cd in comparison with the control treatment.

3–7 24-Epibrasinolide and titanium oxide nanoparticles reduce the translocation factor (TF) and bioaccumulation factor (BAF) and improve the tolerance index (TI) in roots and shoots of bamboo species. ↑ indicates increases and ↓ indicates decreases.

The translocation factor (TF) is one of the main mechanisms used to evaluate the remediation efficiency of heavy metals in plant organs and the reduction of toxicity in plants under heavy metal stress. Therefore, it is calculated according to differences in the accumulation of Cu and Cd in shoots and roots, and it serves as an important factor in increasing plant tolerance to toxicity. In the present study, the addition of 24-epibrassinolide and titanium oxide nanoparticles significantly reduced Cu and Cd translocation from roots to shoots, which led to a reduction in toxicity by limiting metal accumulation in the plant aerial organs. Therefore, according to the results, the co-application of 24-epibrassinolide and titanium oxide nanoparticles in combination with heavy metals (Cu and Cd) resulted in a low level of translocation factor, which could reduce metal toxicity in the aerial parts of bamboo plants (Table 5). Additionally, the result showed that the co-application of EBL and TiO2 NPs significantly reduced Cu and Cd concentration in the leaves (*p* < 0.001), implicating the positive role of EBL–TiO2NPs in the reduction of heavy metal toxicity in the aerial parts of the bamboo plant (Table 5). Conversely, the calculation of the tolerance indices in shoots and roots revealed a significant difference between the co-application of TiO2 NPs and EBL alone under Cu and Cd (*p* < 0.001). Therefore, the levels of TiO2 NPs and EBL indicated an increase in shoot and root tolerance under heavy metal stress, which was obtained by the amelioration mechanism of the co-application of TiO2 and EBL against heavy metal toxicity, such as the stimulation of antioxidant activity and the increase in plant biomass. We suggest that TiO2 NP and EBL concentrations alone increase plant tolerance under metal stress; however, the most positive effect was more pronounced with the co-application of TiO2 NPs and EBL under Cu and Cd toxicity (Table 5).

**Table 5.** Changes in the translocation factor and tolerance index of shoots and roots in response to 24-epibrassinolide and titanium oxide nanoparticles individually or in combination with 100 μM Cu and 100 μM Cd compared with the control treatment. Each data point is the mean ± SE of four replicates. The capital letters (A–C) indicate significant differences between treatments of control (C), titanium (Ti), 24-epibrassinolide (EBL), and 24-epibrassinolide involving individual or combined application of titanium oxide nanoparticles (EBL–TiO2 NPs) under 100 μM Cu and 100 μM Cd (the bars with similar colors), while the lowercase letters (a–c) denote statistically significant differences at each concentration of the co-application of EBL and TiO2 NPs individually or in combination with 100 μM Cu and 100 μM Cd (the bars with various colors) based on Tukey s test (*p* < 0.05).


*High-Throughput Single Extracellular Vesicle Profiling DOI: http://dx.doi.org/10.5772/intechopen.97544*

and particle size distribution are analyzed in AFM [2]. AFM tip indents the EVs and records the force-distance curve to provide insight into the mechanical properties, including mechanical stiffness, Young's modulus and bending modulus [4]. Tumorexosomes of bladder cancer cell lines were proven to show differential mechanical properties dependent on the malignant state [5]. Although constrained in the throughput of EV analysis, the precise analysis of structural and biophysical properties via AFM provides unique measures in EV engineering for targeted delivery and therapeutic (**Figure 2**) [4].


**Acknowledgements**

The study was funded by Nord university. I wish to thank Berit Berg (NTNU) and Monica Martinussen (UiT The Arctic University of Norway), for supervising the project. I also want to thank my colleagues Kristin Berre Ørjasæter and Jonathan Tritter at Nord University for their insightful comments.


**12. Conclusion**

*Bioinformatics Tools for Detection and Clinical Interpretation of Genomic Variations*

genomic selection methods dropping the penalties of each approaches like in elastic net, enabling the fitting of a certain statistical model. Therefore, an outstanding methodology to analyze genome is elastic net domain used in several study, like

Recently, the tuberculosis strain's differences were evaluated using the elastic net domain [34]. In that examination, 10 genome sequences of *Mycobacterium tuberculosis* with a window size of 10,000 bp were assessed combining the NDWT and elastic net domain. This study encompasses 10 strains: 2 from drug resistant, 6 from drug susceptible, 1 from multidrug resistant, and finally 1 from extensively drug resistant. The clustering detected on that analysis indicated to be real

Hurst exponent is applied as a degree of long-standing memory of time series. It associates to the autocorrelations of time series and the degree at which these decline as the lag between pairs of values intensifications. This coefficient has started to be established in hydrology, used to understand the variation level of dam size at Nile River over a long cycle of time. Harold Edwin Hurst was a British engineer that worked with hydrology; for this reason the coefficient has his surname. Later, this exponent was used in several areas, including fractal geometry, storage process, trends in financial market analyzing economic time series, mechanics, physics, mathematics, computation, and finally to the long-ranging dependency in DNA. **Figure 2** displays the values of Hurst exponent and their interpretation in a

Using the genetic data, the Hurst exponent approach is able to build genetic cluster based on genome sequence. There are a lot of estimation methods of Hurst exponent: the original and best-known is the alleged rescaled range (R/S) analysis promoted by [37, 38] and based on previous hydrological findings [39]. Alternatives include DFA, periodogram regression [40] aggregated variances [41], local Whittle's estimator [42], and wavelet analysis [43, 44] both in the time domain and frequency

In our case, we performed a Hurst exponent in the bacterial strains used in article [34]. We did many methods of Hurst exponent. Interestingly, the R/S methodology was the most similar to the cluster obtained on elastic net domain approach. This data is not shown due to being in a review process to an International journal currently. Our data agree with the majority of scientific papers published approaching the Hurst exponent, which so far applying the R/S

**10**

**Figure 2.**

*Hurst exponent pattern interpretation of the index value.*

[33–36].

adequate.

**11. Hurst exponent**

long-standing.

domain.

method.

We strongly believe that exploring the genetic variability of any organism using wavelet coupled with elastic net domain and/or Hurst exponent will be a valuable and interesting tool. It is not difficult and the free R software could solve easily the approach. In this way, it gives reliability and robustness in your results. Therefore, these bioinformatics apparatuses provide more possibility to scrutinize the genetic divergence of living organisms.