**1.1. Different forces acting in membrane emulsification.**

418 Numerical Simulation – From Theory to Industry

causing coalescence on the membrane.

times the pore diameter gave nearly mono dispersed emulsions.

greater than 90o, the wall is called the non-wetting.

pressure is maximum at the starting of formation of the droplet at the pore and it decrease as the droplet grows. The capillary pressure becomes the minimum at the detachment time of the droplet. The effective or drag pressure part is responsible for the flow rate of dispersed phase. The effective pressure determines the throughput and thus the productivity of the membrane emulsification system. The transmembrane pressure controls the size of the droplet formed in membrane emulsification process. With the increase in transmembrane pressure, several researchers (Katoh et al, 1996; Peng and Williams, 1998; Schröder and Schubert, 1999) have observed the increase in droplet diameter while others (Abrahamse et al., 2002; Vladisavljevic and Schubert, 2003; Vladisavljevic et al., 2004) have observed the decrease in droplet diameter. The variation in droplet diameter with transmembrane pressure results the wide range of droplet size distribution (Abrahamse et al., 2002; Vladisavljevic et al., 2004). The wide range of droplet size also is affected by the steric hindrance between droplets or by the membrane being wetted by the dispersed phase,

The design and pore distribution of the membrane are important factors controlling the droplet dynamics in membrane emulsification. Due to presence of multi-pore and multidroplet formation, there is a change in hydrodynamic effects caused by neighboring droplet and interactions between the droplets. The separation distance between the pores controls those hydrodynamic effects. If the separation distance of pores in the flow direction is small, the continuous phase velocity decreases and the boundary layer thickness increases as the flow approaches consecutive rows after crossing the first row. These would lead to an increase in the size of the droplets. With the increase in droplet size, there would be a caution of stability loss and coalescence of the droplets. For high efficiency of the emulsification process, narrow droplet size distribution and higher dispersed phase velocity is required. However, with the increase in dispersed phase flow rate, the droplet formation phenomenon shifts towards jetting (Pathak, 2011) and this requires a greater distance between the pores in the direction of the cross-flowing continuous phase in order to prevent drops from colliding and coalescing. In several experimental studies (Sugiura et al., 2002; Kobayashi et al, 2003, 2006) the droplet size distribution has been observed narrow up to a specific velocity of the dispersed phase, above which the diameter of the droplet distribution has been increased. Timgren et al. (2009) have investigated the effects of pore size distribution on hydrodynamic effects of droplet size and distribution. They observed that for small pore separation distance and with a low dispersed phase velocity the drop formation process was uniform, resulting an emulsion with a narrow drop size distribution. For shortest pore separation distance, with the increase in dispersed phase velocity, they observed the formation of poly dispersed emulsion, whereas pore separations of 15 and 20

The wetting behavior of membrane surface also controls the droplet growth. The wetting behavior of a membrane is represented by the static contact angle between the two liquid phases and the solid boundary. The static contact angle between the two phases and walls controls the evolution of the dispersed phase inside the micro-pore and in the continuous phase flow channel. If the angle is less than 90o the wall is said to be wetting and if it is All the parameters discussed in above control the droplet dynamics in membrane emulsification process with different magnitudes and output of the process can be analyzed on the basis of these operating parameters. Besides the individual effects, many of these parameters exhibit coupling effects. Different types of hydrodynamic forces act in the emulsification process. The droplet growth and deformation in membrane emulsification can be explained from the action of these and the final droplet size is a result of the interaction of these forces.

**Figure 2.** Different forces acting on the emulsification system

The major forces that act in the process are: drag force imparted by the flowing continuous phase, the interfacial tension force, the inertial force of the dispersed phase and the buoyancy or gravitational force. Different forces acting in the droplet formation process are shown in Fig. 2. Among these forces, interfacial tension force is the attaching force and other are detaching force. The droplet is detached from the pore when the detaching forces overcome the attaching force.

These four forces can be approximated as follows:

Drag force:

$$F\_D = \frac{1}{2} \mathcal{C}\_d \rho\_{cp} (v^\* - v\_{dp})^2 (\pi D\_p^2 / 4) \tag{4}$$

Surface tension force:

$$F\_t = \pi \sigma D\_p \tag{5}$$

Numerical Simulation of Droplet Dynamics in Membrane Emulsification Systems 421

(12)

For low value of *We* number, the value of inertial force ( *i F* ) is very low. In that situation the

\* 2 ( )( ) *p d cp dp*

Droplet formation and deformation have been studied for a long time due to complexity with the problem and practical utilities of the phenomenon. Droplet formation in a twophase flow system possesses a rich dynamics with the involvement of several parameters such as average velocity of the liquids, their viscosities, densities, surface tension, surface chemistry and the flow geometry. Droplets formation results the creation of new surfaces which enhance the heat and mass transfer between the phases. Due to enhanced heat and mass transfer, the process has been used for wide ranges of phase-contact applications. Particularly the droplet formation in micro or nano size has received significant attention during last several years. Due to miniature size, the fabrication of experimental facility is expensive and reliable experimentation of microfluidic is very intricate. Hence the viable alternate is the numerical tools for investigating the problems. With the development of high speed computer and advanced algorithm, numerical modeling and simulation have become an essential part in the design and development of numerous engineering systems. Numerical simulations of droplet dynamics i.e. the investigations of two-phase flow in micro scale have been extensively undertaken during last several years. Various types of numerical techniques have been developed to solve the governing equations of the two-

In numerical simulation of droplet dynamics or as a whole in the simulation of two-phase flow, there are several challenges which need to be carefully tackled to obtain reliable results. The main challenge is capturing the moving interface of the two phases accurately, which is not known priory. The accurate tracking of the interface and investigation of twophase flow topology should be the essentiality of a good numerical method. There are several numerical methods based on interface kinematics to track the interface in free surface flows. Among them are: volume of fluid methods, front tracking methods, level set methods, phase field formulations, continuum advection schemes, boundary integral

Volume of fluid method (VOF), earlier known as the volume tracking method, were originally developed by Nichols and Hirt (1975), Noh and Woodward (1976) and further extended by Hirt and Nichols (1981). Since then, the method has been extensively used and significantly improved over the years (Rudman,1997; Rider and Kothe,1998). The VOF

Thus analytically, droplet diameter increases with the increase in surface tension value.

*C vv*

*D*

diameter of the drop can be approximated as

**1.2. Numerical simulation of droplet dynamics**

phase flow.

**1.3. Different numerical methods**

methods, particle-based methods, and moving mesh methods.

Inertial force of dispersed phase:

$$F\_i = \frac{\pi \rho\_{dp} D\_0^2 v\_{dp}^2}{4} \tag{6}$$

Buoyancy force:

$$F\_{\mathcal{B}} = \rho\_c \mathcal{g} V\_{dr} - \mathcal{p}\_c A\_n \tag{7}$$

In above *dp v* is the velocity of dispersed phase ( *dp o v v* ), \* *v* is the local continuous phase velocity at the centre of the drop, *Dp* is the diameter of the drop, *D*0 is the pore diameter and Vdr is the droplet volume. The local continuous phase velocity is given by:

$$\boldsymbol{\upsilon}^\* = 2\boldsymbol{\mu}\_{cp} \left[ \mathbf{1} - \left( \frac{\boldsymbol{D}\_h - \boldsymbol{D}\_p}{\boldsymbol{D}\_h} \right)^2 \right] \tag{8}$$

where *cp u* is the average velocity of the continuous phase in the channel. *Dh* is the hydraulic diameter given by:

$$D\_h = \mathcal{Z}bh \;/\; (b+h) \tag{9}$$

where *b* and *h* are the width and height of the continuous phase channel respectively. The drag coefficient *Cd* depends upon the Reynolds number of the droplet (*Rep*) and the viscosity ratio λ (/) *dp cp* . The drop Reynolds number is defined as

$$Re\_p = \frac{\rho\_{dp} \left| \boldsymbol{v}^\* - \boldsymbol{v}\_{dp} \right| D\_p}{\mu\_{dp}} \tag{10}$$

Out of these forces, the only attaching force is the surface tension force and remaining forces, drag and inertial and buoyancy force are detaching forces. Neglecting the buoyancy or gravity force, the balance of forces at the moment of droplet detachment can be written as

$$\frac{1}{2}\mathcal{C}\_{d}\rho\_{cp}(\boldsymbol{v}^\*-\boldsymbol{v}\_{dp})^2(\pi\mathcal{D}\_p^2/4) + \frac{\pi\rho\_{dp}\mathcal{D}\_0^2\boldsymbol{v}\_{dp}^2}{4} = \pi\sigma\mathcal{D}\_p\tag{11}$$

For low value of *We* number, the value of inertial force ( *i F* ) is very low. In that situation the diameter of the drop can be approximated as

$$D\_p \propto \frac{\sigma}{\left(\mathcal{C}\_d \rho\_{cp}\right) \left(v^\* - v\_{dp}\right)^2} \tag{12}$$

Thus analytically, droplet diameter increases with the increase in surface tension value.

### **1.2. Numerical simulation of droplet dynamics**

420 Numerical Simulation – From Theory to Industry

Drag force:

Surface tension force:

Buoyancy force:

diameter given by:

viscosity ratio λ (/) *dp cp*

 Inertial force of dispersed phase:

These four forces can be approximated as follows:

1 \* 22 ( ) ( / 4) 2 *D d cp dp P F C vv D*

*t p F D*

*B c dr c n F gV p A*

In above *dp v* is the velocity of dispersed phase ( *dp o v v* ), \* *v* is the local continuous phase velocity at the centre of the drop, *Dp* is the diameter of the drop, *D*0 is the pore diameter

> \* 2 1 *h p cp*

where *cp u* is the average velocity of the continuous phase in the channel. *Dh* is the hydraulic

where *b* and *h* are the width and height of the continuous phase channel respectively. The drag coefficient *Cd* depends upon the Reynolds number of the droplet (*Rep*) and the

. The drop Reynolds number is defined as

*p*

1 \* 22 ( ) ( / 4) 2 *C vv D d cp dp P*

\* *dp dp p*

Out of these forces, the only attaching force is the surface tension force and remaining forces, drag and inertial and buoyancy force are detaching forces. Neglecting the buoyancy or gravity force, the balance of forces at the moment of droplet detachment can be written as

 *v vD*

*dp*

2 2 0 4 *dp dp*

2

*h*

*D D*

*D*

*D v*

(4)

(5)

(6)

(7)

2 /( ) *D bh b h h* (9)

*Re* (10)

*Dp*

(11)

*dp dp D v* (8)

*i*

*F*

and Vdr is the droplet volume. The local continuous phase velocity is given by:

*v u*

Droplet formation and deformation have been studied for a long time due to complexity with the problem and practical utilities of the phenomenon. Droplet formation in a twophase flow system possesses a rich dynamics with the involvement of several parameters such as average velocity of the liquids, their viscosities, densities, surface tension, surface chemistry and the flow geometry. Droplets formation results the creation of new surfaces which enhance the heat and mass transfer between the phases. Due to enhanced heat and mass transfer, the process has been used for wide ranges of phase-contact applications. Particularly the droplet formation in micro or nano size has received significant attention during last several years. Due to miniature size, the fabrication of experimental facility is expensive and reliable experimentation of microfluidic is very intricate. Hence the viable alternate is the numerical tools for investigating the problems. With the development of high speed computer and advanced algorithm, numerical modeling and simulation have become an essential part in the design and development of numerous engineering systems. Numerical simulations of droplet dynamics i.e. the investigations of two-phase flow in micro scale have been extensively undertaken during last several years. Various types of numerical techniques have been developed to solve the governing equations of the twophase flow.


**Replacement of Chromium by Non-Toxic Metals in Lewis-Acid MOFs: Assessment of Stability as Glucose Conversion Catalysts**

**Ralentri Pertiwi 1,2,3,**†**, Ryan Oozeerally 1,**†**, David L. Burnett 2, Thomas W. Chamberlain 1,2, Nikolay Cherkasov 1, Marc Walker 4, Reza J. Kashtiban 4, Yuni K. Krisnandi 3, Volkan Degirmenci 1,\* and Richard I. Walton 2,\***

Received: 16 April 2019; Accepted: 6 May 2019; Published: 9 May 2019

**Abstract:** The metal–organic framework MIL-101(Cr) is known as a solid–acid catalyst for the solution conversion of biomass-derived glucose to 5-hydroxymethyl furfural (5-HMF). We study the substitution of Cr3+ by Fe3<sup>+</sup> and Sc3+ in the MIL-101 structure in order to prepare more environmentally benign catalysts. MIL-101(Fe) can be prepared, and the inclusion of Sc is possible at low levels (10% of Fe replaced). On extended synthesis times the polymorphic MIL-88B structure instead forms.Increasing the amount of Sc also only yields MIL-88B, even at short crystallisation times. The MIL-88B structure is unstable under hydrothermal conditions, but in dimethylsulfoxide solvent, it provides 5-HMF from glucose as the major product. The optimum material is a bimetallic (Fe,Sc) form of MIL-88B, which provides ~70% conversion of glucose with 35% selectivity towards 5-HMF after 3 hours at 140 ◦C: this o ffers high conversion compared to other heterogeneous catalysts reported in the same solvent.

**Keywords:** Metal–organic framework; Lewis acid; fructose; 5-hydroxymethyl furfural; biomass


**4. Research algorithm**

Methodological development offered by the author allows to overcome successfully listed problems and also develops the methodology of statistical research of regional investment appeal according to the concept of the Russian state statistics development, which makes them relevant not only for theory but also for the practice of statistical research of the invest-

The proposed methodological developments expand the methodology of Kaplan and Norton [10]. They allow realizing the monitoring of region investment policy on the basis of an integral statistical measure of the region investment appeal. The quantitative level of the integral statistical measure is the proportion of the number of completed relationships between the growth rates of the actual indicators characterizing a particular investigated object to the

The essence of the method of the dynamic standard is the formation in accordance with some objective (e.g., maximization of the company's profit, increasing the cost of equity, increasing regional investment appeal), groups of indicators characterizing the purpose and, to the greatest extent, reflecting the real state of the object of research in dynamics. The quantitative composition of the indicators should be no lower than the established (no less than 6 and no more than 25). The method of the dynamic standard is the procedure of selection of economic

The main idea of the method belongs to Syroezhin [11], it was further developed by his students [12]. It consists of the fact that not commensurable indicators in statics become com-

In modern scientific works and publications, there are examples of the application of the

Syroezhin noticed that noncomparable static characteristics of the national economy are comparable in dynamics. The proposed dynamic standard is organized by pace (coefficients, indexes) growth (or base chain) set (system) of indicators, such that maintaining for a longtime interval specified in a dynamic normative order of indicators provides the maximization of integral evaluation. Form of expression the ordering of the indicators is the ranking of performance (assigning grades), if not all indicators are able to link strictly in order, the presentation days to serve the count of preferences and/or the corresponding matrix of preferences, in this case, the integral meter has the form of a normative model. The quantitative level of the integrated meter (integrated assessment) in this case is the ratio of the number of performed correlations between growth rates (indices) of growth of actual indicators characterizing the specific object under study, to the number of set relations in a normative model. Accordingly, the resulting quantitative levels vary in the range from 0 to 1, the closer the value is to 1, the

The algorithm for constructing normative models is disclosed in detail in the work of Pohostinsky [18]. The normative model differs from the matrix of preferences and its indicators

ment regional situation, including in perspective of its development.

number of given ratios in the normative model.

52 Statistics - Growing Data Sets and Growing Demand for Statistics

**3. The method of the dynamic standard**

indicators and their ordering.

method of dynamic standard [13–17].

more quantitative is the valuation level.

mensurable in dynamics.

### **4.1. Step 1: indicators of region investment appeal**

The classical concept of "*investment appeal*" means the existence of certain investment conditions that affect the investor's goals and determine its choice when considering investment objects. The efficiency of the investment policy of the region is characterized by the degree of achievement of long-term goals of its development, the reproduction of the potential of the region and the growth of the quality of life of the population. Therefore, from our point of view, region investment appeal (RIA) is a complex of natural, geographical and socioeconomic factors that determine the effectiveness of the investment policy of the region and its socioeconomic development.

For the statistical research of investment appeal of Russian regions is proposed to use an integrated statistical indicator, which is based on measures that assess the "state of capacity development in the region" and the influence of the factors such as "the performance of business activities in the region" and "the performance of activities of public administration bodies in the region." **Figure 1** shows the structure of regional investment appeal. This interpretation is based on the scientific idea that the RIA is determined not only by the factors of the investment potential of the region, but also by the factors of the effectiveness of its investment policy.

Statistical estimation of the region potential is a traditional task of statistical measurement. The region potential includes components such as natural and geographical potential, property potential, financial potential, human potential and innovative potential. New for statistical measurement of its factors are "performance of government in the region" and "performance of business in the region."

**Figure 1.** The structure of the region investment appeal as an object of statistical study.

Statistical assessment and monitoring of these factors are new tasks of statistical research. Their solution requires improvement and refinement of the methodology of statistical research of RIA in the search for adequate scientific methods of assessment, the principles of the formation of its information basis.

The study of existing methods for estimating RIA (the methodology of the agency "Universe," the agency "Expert-RA" and others) made it possible to find out that a large number of statistical indicators are used for its construction. The solution of such a problem became possible in the framework of the system approach. Therefore, the region was considered by us as a socioeconomic system, and its investment attractiveness as its system-wide property, for the study of which a system of statistical indicators was formed. It consists of indicators that assess the potential and the results of the development of the region, which characterize the identified factors of the RIA. Absolute indicators selected in the system are presented in **Table 1**.

#### **4.2. Step 2: the normative model of region investment appeal**

The dynamic standard and the normative model of region investment appeal are presented in **Tables 2** and **3**. Formalization of the dynamic standard was tested using pairwise comparisons in accordance with targets of research (see **Table 2**). If, in accordance with the target installation rate in the row of the matrix needs to grow faster than the rate in the column below target was performed setting the "growth" that is put in matrix 1 at the intersection of row and column, while the symmetrical choice is −1. Otherwise, the −1, while the symmetric place puts 1. If relationship between the indicators is not set, then put a zero, the matrix diagonal has only zeros. Thus, in the matrix set 49 targets.

The matrix E is described as follows:

**Table 1.** Absolute indicators of RIA (Pi).

**No. Indicator name**

1 Population in the region

2 Value of fixed assets in the region

5 Investments in fixed assets (Capex)

10 Gross regional product (GRP)

3 Residual value of fixed assets of the region

 *Number of employed in the economy of the region* Incomes of the population of the region Total number of unemployed in the region Fund for remuneration of workers in the region

11 Tax revenues of the consolidated budget of the region 12 Revenues of the consolidated budget of the region 13 Expenditures of the consolidated budget of the region

14 Balanced financial performance of organizations in the region 15 Number of employees employed in small enterprises in the region

4 Internal costs for research and development of the region

*Factor "Potential for development of the region"*

*Factor "Performance of government in the region"*

*Factor "Performance of business in the region"*

⎧ ⎪ ⎨ ⎪ ⎩

16 Number of small enterprises in the region 17 Number of organizations in the region

1, if GR(Pi

18 The number of unprofitable organizations in the region

−1, if GR(Pi

GR(Pi

) > GR(Pj) ;

) < GR(Pj) ; 0, if the reference

ratio between

where i, j are the numbers of indicators in DS; Pi, Pj are indicators having the i-th and j-th numbers in DS, respectively; GR(Pi) > GR (Pj) and GR (Pi) < GR (Pj) are reference of ratio

)and GR(Pj),

Statistical Research of Investment Appeal of Russian Regions

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55

(1)

eij

between rates (indices) of growth.

Formally, the dynamic standard of preferences is set by a matrix (Е = {еij}nxn), each element of which reflects the normative relation between performance (faster/slower) of the row and column of the matrix (see **Table 2**).

**Table 1.** Absolute indicators of RIA (Pi).

Statistical assessment and monitoring of these factors are new tasks of statistical research. Their solution requires improvement and refinement of the methodology of statistical research of RIA in the search for adequate scientific methods of assessment, the principles of

The study of existing methods for estimating RIA (the methodology of the agency "Universe," the agency "Expert-RA" and others) made it possible to find out that a large number of statistical indicators are used for its construction. The solution of such a problem became possible in the framework of the system approach. Therefore, the region was considered by us as a socioeconomic system, and its investment attractiveness as its system-wide property, for the study of which a system of statistical indicators was formed. It consists of indicators that assess the potential and the results of the development of the region, which characterize the identified

factors of the RIA. Absolute indicators selected in the system are presented in **Table 1**.

The dynamic standard and the normative model of region investment appeal are presented in **Tables 2** and **3**. Formalization of the dynamic standard was tested using pairwise comparisons in accordance with targets of research (see **Table 2**). If, in accordance with the target installation rate in the row of the matrix needs to grow faster than the rate in the column below target was performed setting the "growth" that is put in matrix 1 at the intersection of row and column, while the symmetrical choice is −1. Otherwise, the −1, while the symmetric place puts 1. If relationship between the indicators is not set, then put a zero, the matrix diago-

of which reflects the normative relation between performance (faster/slower) of the row and

}nxn), each element

**4.2. Step 2: the normative model of region investment appeal**

**Figure 1.** The structure of the region investment appeal as an object of statistical study.

nal has only zeros. Thus, in the matrix set 49 targets.

column of the matrix (see **Table 2**).

Formally, the dynamic standard of preferences is set by a matrix (Е = {еij

the formation of its information basis.

54 Statistics - Growing Data Sets and Growing Demand for Statistics

The matrix E is described as follows:

$$\begin{cases} \text{1, if } \text{GR(P\_{\downarrow})} > \text{GR(P\_{\downarrow})} \; ; \\ \text{-1, if } \text{GR(P\_{\downarrow})} < \text{GR(P\_{\downarrow})} \; ; \\ \quad \text{0, if the reference} \\ \quad \text{ratio between} \\ \text{GR(P\_{\downarrow})} \text{and } \text{GR(P\_{\downarrow})} . \end{cases} \tag{1}$$

where i, j are the numbers of indicators in DS; Pi, Pj are indicators having the i-th and j-th numbers in DS, respectively; GR(Pi) > GR (Pj) and GR (Pi) < GR (Pj) are reference of ratio between rates (indices) of growth.

Thus, formed matrix E, after identifying additional relationships, is a normative model

Reflection of the results of the adopted and implemented managerial decisions is the actual relation matrix of indicators Pi. The closer the actual ordering of the indices to a given normative order in the model, the higher is the level of region investment attractiveness. The matrix of actual correlations of growth performance (F = {fij}nxn) is described

) > *GR*(P*j*) ;

) = *GR*(*Pj*),

) < *GR*(*Pj*)

1, *if eij* = 1 *simultaneously with f*

*or if eij* = −1 *simultaneously with f*

0, *otherwise*

*ij* ≥ 0 ;

*ij* ≤ 0)

(4)

*ij* ≤ 0 ;

*ij* ≥ 0) *or* (*eij* = −1 *and f*

0, *if others case*

;

Statistical Research of Investment Appeal of Russian Regions

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(2)

57

(3)

(**Table 3**).

as follows:

respectively.

y =

Δ*Y*(*Pi*

where ΔY(Pi

of indicators; b0

) = ∑ *j*=1 *n bij* 0 − ∑ *j*=1 *n bij b*

\_\_\_\_\_\_\_\_\_\_\_\_ ∑ *i*=1 *n* ∑ *j*=1 *n* ∣ *eij* ∣

ij, bb

*f*

∑ *i*=1 *n* ∑ *j*=1 *n bij* \_\_\_\_\_\_\_\_\_\_\_\_ ∑ *i*=1 *n* ∑ *j*=1 *n* ∣ *eij* ∣

*ij*

where *bij*

element of the matrix NM, fij is the element of the matrix F = {fij}nxn.

on Y growth, which is the effective rate, determined by the formula:

and *bij*{

relationships between indicators implemented in reality.

⎧ ⎪ ⎨ ⎪ ⎩ **1**, *if GR*(*Pi*

−**1**, *if GR*(*Pi*

**0**, *if GR*(*Pi*

actual and normative models set in order the rates (indices) of growth indicators (Y).

⎧ ⎪ ⎨ ⎪ ⎩

where i, j are the numbers of indicators; Pi, Pj are indicators having the i-th and j-th numbers, respectively; GR(Pi), GR (Pj) are actual rate (index) of growth of the ith and jth indicators,

An integrated assessment of region investment attractiveness is the estimation of proximity of

n is the number of indicators in DD; i, j are the numbers of indicators in DD; bij is the element of the matrix of coincidence of actual and reference ratios of growth rates (В = {bij}nxn); еij is the

Score Y varies from 0 to 1. Equal to 1, if all regulations set the ratio of the rate of improvement is actually implemented. Equal to 0, if the actual order of indices is opposite to the normative order of indicators in the model. The closer Y is to 1, the greater is the proportion of regulatory

The generated model can be considered as the factor system. The influence of each indicator

1, *if*(*eij* = 1 *and f*

growth rate of the ith indicator with others; n is the number of indicators; i, j are the numbers

) is the increase in the assessment caused by the dynamics of the ratio of the

ij are the elements of the matrix of coincidence of actual and reference

**Table 2.** The dynamic standard (DS) of region investment attractiveness.

Thus, formed matrix E, after identifying additional relationships, is a normative model (**Table 3**).

**No and indicator name**

3. Number of employed in the economy of the region

4. Number of employees employed in small enterprises in the region

6. Fund for remuneration of workers in the region

8. Value of fixed assets in

9. Residual value of fixed assets of the region

unprofitable organizations

13. Internal costs for research and development

15. Balanced financial performance of organizations in the

16. Tax revenues of the consolidated budget of

17. Revenues of the consolidated budget of

18. Expenditures of the consolidated budget of

**Table 2.** The dynamic standard (DS) of region investment attractiveness.

10. Number of small enterprises in the region

11. Number of organizations in the

12. Number of

in the region

of the region

region

the region

the region

the region

region

the region

5. Total number of unemployed in the region

1. Population in the

2. Incomes of the population of the region **No indicator in DS**

56 Statistics - Growing Data Sets and Growing Demand for Statistics

**1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18**

0 −1 −1 −1 0 0 −1 −1 −1 −1 −1 0 −1 −1 0 0 −1 1

1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0

1 0 0 −1 1 −1 −1 −1 −1 0 0 0 0 −1 0 0 −1 0

1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 −1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 −1 1 0 0 0 −1 0 0 0 −1 0 0 0 −1 0 0 0

1 0 1 0 0 0 −1 0 −1 0 1 0 0 −1 0 0 0 0

1 0 1 0 0 0 −1 1 0 0 1 0 0 −1 0 0 0 0

1 0 0 −1 0 0 0 0 0 0 1 0 0 0 0 0 0 0

1 0 0 0 0 1 0 −1 −1 −1 0 1 −1 −1 −1 1 0 0

0 0 0 0 0 0 0 0 0 0 −1 0 0 0 0 0 0 0

1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0

0 −1 0 0 0 0 −1 0 0 0 −1 0 0 0 0 0 1 1

1 0 1 0 0 0 −1 0 0 0 0 0 0 0 0 −1 0 1

−1 0 0 0 0 0 −1 0 0 0 0 0 0 0 0 −1 −1 0

7.GRP 1 0 1 0 0 1 0 1 1 0 0 0 −1 1 −1 1 1 1

14.Capex 1 0 1 0 0 0 −1 1 1 0 1 0 0 0 −1 0 0 0

**in DS, Pi**

region

Reflection of the results of the adopted and implemented managerial decisions is the actual relation matrix of indicators Pi. The closer the actual ordering of the indices to a given normative order in the model, the higher is the level of region investment attractiveness. The matrix of actual correlations of growth performance (F = {fij}nxn) is described as follows:

as 1010Ws:
$$f\_{\boldsymbol{\psi}} \begin{cases} \mathbf{1}, \boldsymbol{\text{if}} \, \text{GR}(\boldsymbol{\text{P}}\_{\boldsymbol{\text{i}}}) > \text{GR}(\boldsymbol{\text{P}}\_{\boldsymbol{\text{i}}}) \; ; \\ \mathbf{-1}, \boldsymbol{\text{if}} \, \text{GR}(\boldsymbol{\text{P}}\_{\boldsymbol{\text{i}}}) < \text{GR}(\boldsymbol{\text{P}}\_{\boldsymbol{\text{i}}}) \; ; \\ \mathbf{0}, \boldsymbol{\text{if}} \, \text{GR}(\boldsymbol{\text{P}}\_{\boldsymbol{\text{i}}}) = \text{GR}(\boldsymbol{\text{P}}\_{\boldsymbol{\text{i}}}) \; \end{cases} \tag{2}$$

where i, j are the numbers of indicators; Pi, Pj are indicators having the i-th and j-th numbers, respectively; GR(Pi), GR (Pj) are actual rate (index) of growth of the ith and jth indicators, respectively.

An integrated assessment of region investment attractiveness is the estimation of proximity of actual and normative models set in order the rates (indices) of growth indicators (Y).

$$\mathbf{y} = \frac{\sum\_{i=1}^{k} b\_i}{\sum\_{i=1}^{k} |\boldsymbol{e}\_{i\cdot}|} \\ \text{where } b\_{\boldsymbol{\eta}} \text{ where } b\_{\boldsymbol{\eta}} \text{ if } \boldsymbol{e}\_{\boldsymbol{\eta}} = \boldsymbol{1} \text{ simultaneously with } f\_{\boldsymbol{\eta}} \ge \boldsymbol{0} \text{ ;} \\ \text{where } \begin{cases} \mathbf{1}, \text{if } \boldsymbol{e}\_{\boldsymbol{\eta}} = \mathbf{1} \text{ simultaneously with } f\_{\boldsymbol{\eta}} \ge \boldsymbol{0} \text{ ;} \\ \text{if } \boldsymbol{e}\_{\boldsymbol{\eta}} = -\mathbf{1} \text{ simultaneously with } f\_{\boldsymbol{\eta}} \le \mathbf{0} \text{ ;} \\ \mathbf{0}, \text{ otherwise} \end{cases} \tag{3}$$

n is the number of indicators in DD; i, j are the numbers of indicators in DD; bij is the element of the matrix of coincidence of actual and reference ratios of growth rates (В = {bij}nxn); еij is the element of the matrix NM, fij is the element of the matrix F = {fij}nxn.

Score Y varies from 0 to 1. Equal to 1, if all regulations set the ratio of the rate of improvement is actually implemented. Equal to 0, if the actual order of indices is opposite to the normative order of indicators in the model. The closer Y is to 1, the greater is the proportion of regulatory relationships between indicators implemented in reality.

The generated model can be considered as the factor system. The influence of each indicator on Y growth, which is the effective rate, determined by the formula:

$$\begin{array}{l}\text{Ohm I'z groowhu, wenn is une eneunve } \mathbf{1ae}; \text{ une\'eunnneu dy une norma.}\\\Delta Y[P\_{\rangle}] = \sum\_{i=1}^{\tilde{\mathbf{e}}} b\_{\tilde{\mathbf{e}}}^{\tilde{\mathbf{e}}} - \sum\_{j=1}^{\tilde{\mathbf{e}}} b\_{\tilde{\mathbf{e}}}^{\tilde{\mathbf{e}}} \\\ \frac{1}{\sum\_{i=1}^{\tilde{\mathbf{n}}} |\\_{\tilde{\mathbf{e}}\_{\tilde{\mathbf{e}}}}}{|\!|} \quad \text{and} \quad b\_{\tilde{\mathbf{e}}} \begin{cases} 1, & \text{if} \{e\_{\tilde{\mathbf{e}}} = 1 \text{ and } f\_{\tilde{\mathbf{e}}} \ge 0 \} \text{ or } \{e\_{\tilde{\mathbf{e}}} = -1 \text{ and } f\_{\tilde{\mathbf{e}}} \le 0\} \\\ 0, & \text{if others} \end{cases} \end{array} \tag{4}$$

where ΔY(Pi ) is the increase in the assessment caused by the dynamics of the ratio of the growth rate of the ith indicator with others; n is the number of indicators; i, j are the numbers of indicators; b0 ij, bb ij are the elements of the matrix of coincidence of actual and reference

ratios of rates (indices) of growth in current and base periods, respectively; еij is the matrix

Statistical Research of Investment Appeal of Russian Regions

http://dx.doi.org/10.5772/intechopen.75465

59

In this chapter, the presented results allow to claim that the proposed methodological developments can be applied to a large variety of tasks related to the monitoring of development strategies of regions and other objects of strategic planning. The decline in balance performance or growth with their help received a quantitative rating, which in turn allows you to implement monitoring of investment strategy in the tactical period. This methodology does not require serious mathematical tools; however, if growth of indicators is necessary to apply automated processing for calculation of the estimates, so the author used her own computer program and implemented the development of regulatory models for integrating quantitative evaluations of the investment attractiveness of the region, the city and municipal district.

As the periods of research are selected: 2001–2007 (base period) and 2009–2015 (reporting period) as well as chain dynamics for 2009–2015. The choice of study periods for the baseline dynamics is due to the fact that in 2006–2007, Russia and its regions were given an investment rating by international agencies. The sense of the reporting period is that this was the period when the regions of Russia left the financial crisis and actively attracted foreign investments,

**Tables 4** and **5** show the quantitative levels of investment attractiveness of regions—leaders

Thus, the imposition of sanctions in 2014 significantly affected the investment climate of the Kaluga region, since its economy is more dependent on the activities of foreign investors. For the Tula, Voronezh and Moscow regions, Moscow and Russia in general, the sanctions played

**Regions Y ΔY National rating of investment**

**attractiveness of regions in 2015 2001–2007 2009–2015**

element of the reference relationships between the growth indicators.

and implemented a large number of investment projects.

in Russia, calculated according to the author's algorithm.

Tula region 0.57 0.79 0.22 2 Russia 0.69 0.76 0.08 — Voronezh region 0.52 0.76 0.24 4 Moscow region 0.61 0.66 0.05 5 Kaluga region 0.65 0.61 −0.04 1 Moscow 0.69 0.52 −0.17 3

**Table 4.** Quantitative levels of investment attractiveness of regions.

**4.3. Step 3: Calculations**

**5. Results**

a stimulating role.

**Table 3.** The normative model of region investment appeal.

ratios of rates (indices) of growth in current and base periods, respectively; еij is the matrix element of the reference relationships between the growth indicators.

#### **4.3. Step 3: Calculations**

**No and indicator name**

3. Number of employed in the economy of the region

4. Number of employees employed in small enterprises in the region

6. Fund for remuneration of workers in the region

8. Value of fixed assets in

9. Residual value of fixed assets of the region

unprofitable organizations

13. Internal costs for research and development

15. Balanced financial performance of organizations in the

16. Tax revenues of the consolidated budget of

17. Revenues of the consolidated budget of

18. Expenditures of the consolidated budget of

**Table 3.** The normative model of region investment appeal.

10. Number of small enterprises in the region

11. Number of organizations in the

12. Number of

in the region

of the region

region

the region

the region

the region

region

the region

5. Total number of unemployed in the region

1. Population in the

2. Incomes of the population of the region **No indicator in DS**

58 Statistics - Growing Data Sets and Growing Demand for Statistics

**1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18**

0 −1 −1 −1 0 −1 −1 −1 −1 −1 −1 0 −1 −1 −1 −1 −1 1

1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1

1 −1 0 −1 1 −1 −1 −1 −1 −1 −1 0 −1 −1 −1 −1 −1 1

1 0 1 0 1 1 0 0 0 1 1 1 0 0 0 1 1 1

0 −1 −1 −1 0 −1 −1 −1 −1 −1 −1 0 −1 −1 −1 −1 −1 0

1 −1 1 −1 1 0 −1 −1 −1 −1 −1 0 −1 −1 −1 0 0 1

1 0 1 0 1 1 −1 0 −1 0 1 1 −1 −1 −1 1 1 1

1 0 1 0 1 1 −1 1 0 0 1 1 −1 −1 −1 1 1 1

1 0 1 −1 1 1 0 0 0 0 1 1 0 0 0 1 1 1

1 0 1 −1 1 1 −1 −1 −1 −1 0 1 −1 −1 −1 1 1 1

0 0 0 −1 0 0 −1 −1 −1 −1 −1 0 −1 −1 −1 0 0 0

1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1

1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1

1 −1 1 −1 1 0 −1 −1 −1 −1 −1 0 −1 −1 −1 0 1 1

1 −1 1 −1 1 0 −1 −1 −1 −1 −1 0 −1 −1 −1 −1 0 1

−1 −1 −1 −1 0 −1 −1 −1 −1 −1 −1 0 −1 −1 −1 −1 −1 0

7.GRP 1 0 1 0 1 1 0 1 1 0 1 1 −1 1 −1 1 1 1

14.Capex 1 0 1 0 1 1 −1 1 1 0 1 1 −1 0 −1 1 1 1

**in DS, Pi**

region

In this chapter, the presented results allow to claim that the proposed methodological developments can be applied to a large variety of tasks related to the monitoring of development strategies of regions and other objects of strategic planning. The decline in balance performance or growth with their help received a quantitative rating, which in turn allows you to implement monitoring of investment strategy in the tactical period. This methodology does not require serious mathematical tools; however, if growth of indicators is necessary to apply automated processing for calculation of the estimates, so the author used her own computer program and implemented the development of regulatory models for integrating quantitative evaluations of the investment attractiveness of the region, the city and municipal district.


*2.2. Module 2. The DRPPM-EASY-Integration App Implementation*

The DRPPM-EASY-Integration provides an explorer for the user to upload normalized RNA expression, proteomic quantification, or ssGSEA scores to evaluate the potential relationship between these features (Figure 1B). These can be evaluated by either a 1:1 scatter plot or 1:n rank of Spearman correlation rho values (Table 4). The integrative app also allows the user to perform concurrent differential expression analysis and integration of two expression matrices, for example, to compare RNA and protein expression matrices. The fold change can be compared between the two datasets (Table 4), and differentially expressed genes can be compared by reciprocal GSEA or ssGSEA. Direct overlap between the differentially expressed genes is shown as a Venn diagram and further compared to existing gene set databases by Fisher's exact test, Cohen's kappa score, and the Jaccard index.

#### **Table 4.** Integrative Analysis.

#### *2.3. Installation and User Guide*

The source code and user guide are available for download on the project's GitHub page. The GitHub page includes the list of individual R packages and their version along with an installation script for all package dependencies.

## *2.4. RNA Sequencing Analysis*

USP7 samples were prepared as described in Shaw et al. [21]. Briefly, human T-ALL cell lines Jurkat (ATCC) cells were transduced with USP7 shRNA lentivirus and sorted for GFP positive cells or selected by puromycin. RNA samples were isolated using RNeasy Mini Kit (QIAGEN) and subjected to paired-end 2 × 151 base-pair RNA-seq sequencing (Illumina), 10 Jurkat samples—of which 6 were treated with shRNA and 4 were treated with a scramble RNA—were profiled by RNA-seq. RNA-seq data were processed by a custom pipeline (WRAP, https://github.com/gatechatl/DRPPM\_Example\_Input\_Output/tree/ master/WRAP:Wrapper-for-my-RNAseq-Analysis-Pipeline (accessed on 1 August 2021. RNA-seq reads were aligned using the STAR 2.7.1a aligner [22] in the two-pass mode to the human hg38 genome build using gene annotations provided by the Gencode v31 gene models. Read count for each gene was obtained with HT-seq [23]. Reads were normalized to fragments per kilobase million (FPKM) for each gene.

#### *2.5. Whole Proteomics Mass Spectrometry and Data Analysis*

The 10-plex TMT labeled mass spectrometry experiment was performed with a previously published protocol with slight modification [24,25] (See Supplementary Method,

Supplementary Figure S3 for the experimental design). Protein for each sample was digested by trypsin (Promega). The TMT labeled samples were mixed equally, desalted, and fractionated on an offline HPLC (Agilent 1220) using basic pH reverse-phase liquid chromatography (pH 8.0, XBridge C18 column, 4.6 mm × 25 cm, 3.5 µm particle size, Waters). In total, 20 fractions were derived, and the eluted peptides were ionized by electrospray ionization and detected by an inline Orbitrap Fusion mass spectrometer (Thermo Scientific. Waltham, MA, USA). The MS/MS raw files were processed by a tag-based hybrid search engine JUMP [26]. The data were searched against the UniProt human concatenated with a reversed decoy database for evaluating false discovery rate. Searches were performed using a 25 ppm mass tolerance for precursor ions and 25 ppm mass tolerance for fragment ions, fully tryptic restriction with two maximal missed cleavages, three maximal modification sites, and the assignment of *a*, *b*, and *y* ions. TMT tags on lysine residues and N-termini (+229.162932 Da) were used for static modifications, and Met oxidation (+15.99492 Da) was considered as a dynamic modification. MS/MS spectra were filtered by mass accuracy and matching scores to reduce the protein false discovery rate to approximately 1%. Proteins were quantified by summing up reporter ion counts across all matched PSMs using the JUMP software suite [25,26].

#### *2.6. Pre-Processing of the GSEA Analysis*

To optimize the user experience, we provided a script to pre-generate a GSEA result table (Supplementary Figure S1). The GitHub page contains "Getting Started Scripts", which allows the user to pre-process GSEA results for downstream table visualization. Enriched signature tables can take a long time to process depending on the number of samples or the size of the GMT file provided by the user. At the top of the script, there are key input parameters, such as file path and name to the expression matrix, metadata, and gene set file, as well as the preferred output file path of the output table(s). Additionally, the getting started scripts include a script to generate an R Data list of the ssGSEA analysis. Large gene sets may require several minutes, so pre-computing can facilitate a better user experience.

#### **3. Results**

#### *3.1. DRPPM-EASY Analysis of RNA-seq and Proteomics Data Use Case 1*

We previously identified that USP7 knockdown in T-ALL reduces the activity of E-proteins in a TAL1 dependent manner [21]. To highlight the functions of the DRPPM-EASY application, we re-examined the RNA sequencing profiling data of Jurkat cells after USP7 shRNA silencing. RNA-seq sample grouping was assessed by unsupervised hierarchical clustering (Figure 2A). Notably, altering the clustering methods and the number of (selected) top variables did not change the clustering result, suggesting robust grouping of our data (Supplementary Figure S2). Differential gene expression was then performed by LIMMA and visualized as a Volcano and MA plot. As expected, differential gene expression analysis found downregulated USP7 expression after silencing (Figure 2B,C). Notably, MYC, NOTCH1, TRIB2, and EOMES were upregulated after USP7 knockdown (Figure 2B). In the pathway analysis view, enriched pathways can be examined with preloaded gene sets from MsigDB, cell marker, and L1000 drug response. By GSEA and single-sample GSEA, we found USP7 knockdown upregulated with MYC and TAL1 associated targets (Figure 2D,E) and found downregulated apoptotic gene signature from the Hallmark database (Figure 2F). Overall, the RNA-seq analysis supports our previous finding that USP7 is implicated in the negative regulation of TAL1-dependent leukemia growth [21].

*Biology* **2022**, *11*, x FOR PEER REVIEW 7 of 14

**Figure 2.** Expression analysis example of RNA-seq data USP7 silenced Jurkat cells. (**A**) Unsupervised clustering of the RNA sequencing data using the top 100 genes ranked based on mean absolute deviation (MAD). (**B**) Differential gene expression analysis comparing USP7 knockdown and scramble. Genes upregulated after USP7 knockdown are shown in red and genes downregulated after USP7 knockdown are shown in blue (USP7-associated targets). (**C**) Boxplot showing the USP7 expression in log2 FPKM. (**D**) Gene set enrichment analysis of MYC targets. (**E**) Boxplot showing the single sample GSVA analysis of the TAL1 gene set. (**F**) Boxplot showing the single sample GSVA analysis of the Hallmark Apoptosis gene set. **Figure 2.** Expression analysis example of RNA-seq data USP7 silenced Jurkat cells. (**A**) Unsupervised clustering of the RNA sequencing data using the top 100 genes ranked based on mean absolute deviation (MAD). (**B**) Differential gene expression analysis comparing USP7 knockdown and scramble. Genes upregulated after USP7 knockdown are shown in red and genes downregulated after USP7 knockdown are shown in blue (USP7-associated targets). (**C**) Boxplot showing the USP7 expression in log2 FPKM. (**D**) Gene set enrichment analysis of MYC targets. (**E**) Boxplot showing the single sample GSVA analysis of the TAL1 gene set. (**F**) Boxplot showing the single sample GSVA analysis of the Hallmark Apoptosis gene set.

Next, tandem-mass-tagged proteomics profiling was performed on the same set of samples with RNA-seq profiling (Figure 3A; Supplementary Figure S3). A joint analysis of the transcriptome and proteome data was carried out by the DRPPM-EASY-Integration pipeline, identifying genes with altered protein abundance and unaltered mRNA levels, such as TRIM27, NOTCH2, UBR3, and USP22 (Figure 3B). Consistent with our previous observation, TRIM27, a known target of USP7 [27], observed decreased protein abundance in T-ALL cell lines with a haploinsufficient *USP7* [21]. The altered abundance of UBR3 and USP22 suggests an altered ubiquitin ligase network. Furthermore, our result suggests that USP7 loss-of-function alters NOTCH2 protein abundance. Of note, NOTCH1 [28] pro-Next, tandem-mass-tagged proteomics profiling was performed on the same set of samples with RNA-seq profiling (Figure 3A; Supplementary Figure S3). A joint analysis of the transcriptome and proteome data was carried out by the DRPPM-EASY-Integration pipeline, identifying genes with altered protein abundance and unaltered mRNA levels, such as TRIM27, NOTCH2, UBR3, and USP22 (Figure 3B). Consistent with our previous observation, TRIM27, a known target of USP7 [27], observed decreased protein abundance in T-ALL cell lines with a haploinsufficient *USP7* [21]. The altered abundance of UBR3 and USP22 suggests an altered ubiquitin ligase network. Furthermore, our result suggests that USP7 loss-of-function alters NOTCH2 protein abundance. Of note, NOTCH1 [28] protein abundance was unaltered after USP7 knockdown (Figure 3B). Thus, the precise

*Biology* **2022**, *11*, x FOR PEER REVIEW 8 of 14

mechanism of USP7 to drive the NOTCH association leukemia signature will need to be carefully examined in future studies. anism of USP7 to drive the NOTCH association leukemia signature will need to be carefully examined in future studies.

tein abundance was unaltered after USP7 knockdown (Figure 3B). Thus, the precise mech-

**Figure 3**. Integrated analysis example of proteomics and transcriptomics USP7 silenced Jurkat cells. (**A**) Jurkat samples treated with USP7 shRNA and scramble were profiled by RNA sequencing and TMT mass spectrometry. (**B**) The log2 fold change from the differential expression analyses is plotted. Positive log2FC indicates upregulated expression after USP7 silencing. Negative log2FC indicates downregulated expression after USP7 knockdown. Dotted line indicates the -1 and 1 log2FC cutoff. (**C**) Upregulated and downregulated gene signatures derived from differentially expressed mRNAs. (**D**) Venn diagram of genes differentially upregulated (top panel) and downregulated (bottom panel) in the transcriptome (left) and proteome (right). (**E**) Up-regulated and downregulated gene signatures derived from differentially expressed proteins. (**F**,**G**) Reciprocal GSEA of differentially expressed genes derived from the transcriptome and examined in the proteomics data (**F**)**.** Similarly, differentially expressed proteins were first derived then examined in the transcriptome data by GSEA (**G**)**. Figure 3.** Integrated analysis example of proteomics and transcriptomics USP7 silenced Jurkat cells. (**A**) Jurkat samples treated with USP7 shRNA and scramble were profiled by RNA sequencing and TMT mass spectrometry. (**B**) The log2 fold change from the differential expression analyses is plotted. Positive log2FC indicates upregulated expression after USP7 silencing. Negative log2FC indicates downregulated expression after USP7 knockdown. Dotted line indicates the −1 and 1 log2FC cutoff. (**C**) Upregulated and downregulated gene signatures derived from differentially expressed mRNAs. (**D**) Venn diagram of genes differentially upregulated (top panel) and downregulated (bottom panel) in the transcriptome (left) and proteome (right). (**E**) Up-regulated and downregulated gene signatures derived from differentially expressed proteins. (**F**,**G**) Reciprocal GSEA of differentially expressed genes derived from the transcriptome and examined in the proteomics data (**F**)**.** Similarly, differentially expressed proteins were first derived then examined in the transcriptome data by GSEA (**G**).

The DRPPM-EASY-Integration includes features assessing the consistency between two datasets. Using the RNA-seq and proteomic data as proof of concept, DRPPM-EASY-Integration found 987 genes consistently upregulated, and 622 genes consistently downregulated in both datasets (Figure 3C–E). A connectivity map-inspired strategy [29,30] was applied to compare the consistency between the two datasets using reciprocal enrichment. Specifically, differential expressed genes in one dataset was used to derive a gene signature for GSEA to test in the other dataset. For example, differentially expressed proteins (Figure 3F) were applied as a GSEA gene set and tested for enrichment in the transcriptome data (Figure 3G). Similarly, gene sets derived from differentially expressed transcripts (Figure 3C) were tested for enrichment in the proteome data (Figure 3H). We then compared the significance of the overlapping differentially expressed genes against other pathway databases, such as Hallmark and KEGG. The overlap was evaluated by Fisher's exact test, Cohen's kappa, and Jaccard index. Consistently, the RNA and protein were most significantly overlapped compared to other gene sets. Moreover, the spliceosome and ubiquitin-mediated proteolysis pathways from KEGG and the unfolded protein response and MYC pathway from Hallmark were consistently enriched in both datasets (Supplementary Figure S3B,C; Supplementary Tables S1 and S2).

#### *3.2. DRPPM-EASY-CCLE Use Case 2*

To further illustrate the DRPPM-EASY functionality, we developed DRRPM-EASY-CCLE, an extended app with features to select samples from the Cancer Cell Line Encyclopedia (CCLE) data. The app is preloaded with 1379 CCLE samples spanning 37 lineages, 96 lineage sub-types, and 33 diseases. For the genetic characterization, 299 cancer drivers [31] were selected and further divided based on the damaging and non-damaging variant status from DepMap [32] (see Supplementary Table S3 for the complete phenotype categories). As an example, we extracted ovary cancer cell lines and performed expression analysis comparing *TP53* mutation status to its wild-type counterpart (Figure 4A). In *TP53* mutated ovary cancer cells, we found a decreased DNA damage response gene signature (Figure 4B), thereby solidifying the role of *TP53* loss-of-function for regulating DNA damage in these ovarian cancer cells.

Previously, KRAS was found to be frequently mutated in non-small cell lung cancer (NSCLC) and is associated with drug resistance [33]. Thus, we analyzed NSCLC cell lines and compared KRAS mutation status to its wild-type counterpart (Figure 4C). By pathway analysis, the MsigDB defined KRAS signature was consistently upregulated in our KRAS mutated samples (Supplementary Figure S4A). Interestingly, top pathways enriched in the KRAS mutated samples are associated with an anti-apoptosis signature (Supplementary Figure S4B). By ssGSEA, amplified expression in KRAS mutated NSCLC cells were enriched with genes that negatively regulate apoptosis (Figure 4D) and upregulating genes that associated with stress granule assembly and disassembly (Figure 4E), which is a dynamic process fundamental to surviving under stress [34]. Interestingly, oncogenic KRAS-driven stress granules were previously identified in pancreatic and colorectal adenocarcinoma [35]; thus, our result suggests a similar stress response in NSCLC cells.

To further expand our functionality for exploring these large project data, we have also implemented features that enable users to upload their own expression matrix to perform an integrative analysis in CCLE and lung squamous cell carcinoma CPTAC datasets https://github.com/shawlab-moffitt/DRPPM-EASY-LargeProject-Integration (accessed on 1 February 2022) (Supplementary Figures S5A–C). Altogether, our framework provides a user-friendly environment to categorize the samples for downstream analysis with a high potential for novel discovery.

**Figure 4.** Use case analysis example of CCLE Expression data. (**A**) Drop-down menu selection of sample cohort and sample phenotype characteristic. CCLE ovary samples and TP53 mutation status were selected from the drop-down menu option. (**B**) Single-sample GSEA analysis of genes defining the DNA damage response by Amundson et al. Analyzed samples were selected from the dropdown menu from (**A**). (**C**) Drop-down menu selection of sample cohort and sample phenotype characteristic. CCLE non-small cell lung cancer samples and phenotype associated with the KRAS mutation status were selected from the drop-down menu option. (**D**) Single sample GSEA analysis of genes negatively regulating the DNA damage response. (**E**) Single sample GSEA of genes defining the stress granule assembly and disassembly. Gene sets were compiled from Biological Pathways from the Gene Ontology database (GOBP). Analyzed samples were selected from the drop-down menu from (**C**). **Figure 4.** Use case analysis example of CCLE Expression data. (**A**) Drop-down menu selection of sample cohort and sample phenotype characteristic. CCLE ovary samples and TP53 mutation status were selected from the drop-down menu option. (**B**) Single-sample GSEA analysis of genes defining the DNA damage response by Amundson et al. Analyzed samples were selected from the drop-down menu from (**A**). (**C**) Drop-down menu selection of sample cohort and sample phenotype characteristic. CCLE non-small cell lung cancer samples and phenotype associated with the KRAS mutation status were selected from the drop-down menu option. (**D**) Single sample GSEA analysis of genes negatively regulating the DNA damage response. (**E**) Single sample GSEA of genes defining the stress granule assembly and disassembly. Gene sets were compiled from Biological Pathways from the Gene Ontology database (GOBP). Analyzed samples were selected from the drop-down menu from (**C**).

#### Previously, KRAS was found to be frequently mutated in non-small cell lung cancer **4. Discussion**

(NSCLC) and is associated with drug resistance [33]. Thus, we analyzed NSCLC cell lines and compared KRAS mutation status to its wild-type counterpart (Figure 4C). By pathway analysis, the MsigDB defined KRAS signature was consistently upregulated in our KRAS mutated samples (Supplementary Figure S4A). Interestingly, top pathways enriched in the KRAS mutated samples are associated with an anti-apoptosis signature (Supplementary Figure S4B). By ssGSEA, amplified expression in KRAS mutated NSCLC cells were enriched with genes that negatively regulate apoptosis (Figure 4D) and upregulating genes that associated with stress granule assembly and disassembly (Figure 4E), which is An effective method for visualization and data analysis is key to the analysis of multiomics data that captures the molecular processes of cancer initiation and progression. Several Shiny apps have been published to date and can be categorized into the following three categories: (1) tools that focus on pairwise differential expression and biomarker discovery (e.g., POMAShiny 10], TCC-GUI [11], and START App [12]), (2) tools that perform pathway and network analysis (e.g., iOmics [14] and JUMPn [15]), and (3) tools that facilitate the query of large datasets, such as from public repositories or consortium

deposited datasets and deposited expression data (e.g., shinyGEO [16], ImaGEO [17], and GENAVi [13]). While numerous web tools have been developed thus far, there is a lack of tools that directly address challenges associated with multi-data integration, such as evaluating the consistency between omics datasets.

Here, we developed an interactive software tool, DRPPM-EASY, that allows users to perform complex omics data integration in both small (pairwise comparison) and large (consortium) projects. DRPPM-EASY puts together an interactive flexible interface that enables the exploration of biomarkers and enriched pathways across multiple datasets. DRPPM-EASY can perform routine gene analysis, such as hierarchical clustering, differential gene expression, pathway analysis, GSEA, and ssGSEA. Additionally, DRPPM-EASY can perform a joint analysis of two expression datasets. As an example, we have highlighted the application's ability to evaluate the consistency between transcriptome and protein datasets. This is made possible by deriving a gene set feature in one dataset (i.e., transcriptomics), which is applied in the GSEA analysis of the other dataset (i.e., proteomics). DRPPM-EASY can be easily adapted for large consortium data, which we highlight as an example in CCLE cancer cell lines and lung squamous cell carcinoma CPTAC proteome data. Finally, to further expand the utility of our tool, the user can upload their own expression data and use it to compare against CCLE cell lines and lung squamous cell carcinoma proteome data. One major limitation of our application requires the user to normalize their gene expression matrix prior to using our application. Existing pipelines are available to streamline the normalization procedure, such as Shiny-Seq [36]. A normalization procedure will be included in future updates of our application.

Finally, the ability to run the application with a user interface on a local desktop reduces the need for computational domain knowledge of expression analysis. The DRPPM-EASY application can be set up on the server in real-time, enabling collaborative discussion on potential hypotheses derived from the high-throughput data. Our tool also ensures reproducibility of the data analysis, which is one of the most significant issues in omics research [37]. While the current application is highlighted to work in RNA-seq and proteomics data, our framework could easily be adapted to incorporate drug response, genetic screening, or splicing associated features in future versions of our application. Thus, we believe DRPPM-EASY will be a useful and valuable tool for the biomedical research community.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/biology11020260/s1, Supplementary Method. Supplementary Table S1. KEGG Pathways Jointly Enriched in the Transcriptome and Proteome. Supplementary Table S2. Hallmark Pathways Jointly Enriched in the Transcriptome and Proteome. Supplementary Table S3. CCLE Sample Meta-Information. Supplementary Figure S1. Schematic of the GSEA pre-processing. Supplementary Figure S2. Unsupervised hierarchical clustering of Jurkat samples after USP7 knockdown. Supplementary Figure S3. Experimental design of the total proteome profiling of the USP7 knockdown experiment. Supplementary Figure S4. Pathway enrichment analysis of genes differentially upregulated in KRAS mutated samples in NSCLC. Supplementary Figure S5. Screen shot showing the user option to upload user data in the DRPPM-Large Project Integration.

**Author Contributions:** Conceptualization, T.I.S. and A.-C.T.; methodology, T.I.S., A.O.; software, T.I.S., A.O., Q.H. and J.D.N.; validation, L.D.; formal analysis, T.I.S.; data curation, M.G.; writing original draft preparation, T.I.S., A.-C.T.; writing—review and editing, T.I.S., A.-C.T., P.R., T.J.R. and M.T.; supervision, T.I.S.; funding acquisition, T.I.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** Moffitt Cancer Center (NCI P30 CA076292) and the Moffitt Cancer Center Department of Biostatistics and Bioinformatics Pilot Projects (PI: T.S.).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The developed software and processed data can be downloaded from the following GitHub page https://github.com/shawlab-moffitt/DRPPM-EASY-ExprAnalysisShinY (accessed on 1 February 2022).

**Acknowledgments:** This work has been supported in part by the Biostatistics and Bioinformatics Shared Resource at the Moffitt Cancer Center (NCI P30 CA076292) and the Moffitt Cancer Center Department of Biostatistics and Bioinformatics Pilot Project (PI: T.S.). We would like to thank Rodrigo Carvajal and Guillermo Gonzalez-Calderon for their help in setting up the internal web server. We would like to thank Dongliang Du and Ling Cen for their help in the USP7 RNAseq analysis.

**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


*Edited by Vivek Patel*

The petrochemical industry is an important constituent in our pursuit of economic growth, employment generation and basic needs. It is a huge field that encompasses many commercial chemicals and polymers. This book is designed to help the reader, particularly students and researchers of petroleum science and engineering, understand the mechanics and techniques. The selection of topics addressed and the examples, tables and graphs used to illustrate them are governed, to a large extent, by the fact that this book is aimed primarily at the petroleum science and engineering technologist. This book is must-read material for students, engineers, and researchers working in the petrochemical and petroleum area. It gives a valuable and cost-effective insight into the relevant mechanisms and chemical reactions. The book aims to be concise, self-explanatory and informative.

ISBN 978-953-51-0411-7

Petrochemicals

Photo by RonFullHD / iStock


**PAMAM Dendrimer as Potential Delivery System for Combined Chemotherapeutic and MicroRNA-21 Gene Therapy**

Xuan Zhou, Yu Ren, Xubo Yuan, Peiyu Pu and Chunsheng Kang *Department of Neurosurgery, Laboratory of Neuro-Oncology, Tianjin Medical University Tianjin research center of basic medical science, Tianjin medical university First Department of Head and Neck Cancer Tianjin Medical University Cancer Institute & Hospital School of Materials Science & Engineering, Tianjin University China*


*2.1. Sampling*

This study was approved by the Institutional Review Board of the School of Dentistry, Kyung Hee University (KHD-IRB-1509-5). Ten children were recruited as study subjects. Six of the children had black stained teeth while four children maintained good oral hygiene and had no black stained teeth. Oral examinations were performed on all the children, and decayed-missing-filled teeth (DMFT) indices were estimated. The BS scale was estimated using the Shourie method [10]. The criteria for classifying BS were as follows: 1; no line, 2; incomplete coalescence of pigmented spots and 3; continuous line formed by pigmented spots. After classification, subjects with parental consent were instructed not to brush their teeth for 24 h before sampling. Sampling was performed in the morning from the subjects, who were instructed to skip breakfast. Sterilized dental explorers were used to collect supragingival plaque on the buccal and lingual surfaces of all teeth. The samples were collected in 1 mL of sterile phosphate-buffered saline and stored at −80 ◦C after liquid nitrogen treatment.


**5. POD analysis of flow field**

In this section, the wind incident angle of 0 degree is studied, which exactly corresponds to the side ratio case of 3:1, because in this case, the flow phenomenon is more complex. To identify the mechanisms that underlie the twisted-wind effect and elucidate the essential difference of the flow patterns under CWP and TWP, POD analysis was conducted on fluctuating velocity field at different horizontal planes (z = 1/6H, 1/2H and 5/6H) in the near-wake, and the spatial distribution and temporal evolution of the mode patterns were systematically compared for these two cases.


**3. Measurement of moisture in the soil**

The infiltration process in the soil can be modeled by the Richards equation whose solution implies knowing the hydraulic functions of the soil. These functions depend on some parameters they need for their calibration of the hydraulic properties, determined by means of measurements. The measurements of the hydraulic properties of the soil present numerous complications due to two important factors: the nonlinearity of the conductivity/ suction function and the nonlinearity of the moisture/suction function [12].

Given the problem of the heterogeneity of the porous medium, the modeling of the watersolute-soil-plant system is more complicated. Therefore, obtaining the parameters that allow adjusting the model as close to reality is what takes most of time and money.

The adequate measurement of the water content in soil plays a critical role for the estimation of water and energy balances, as well as for understanding the biological and chemical processes in the entire soil-plant system [13].

The evaluation of soil moisture content at different suction conditions in the field requires considerable time and effort, as well as equipment. The effort, time, and equipment needed will depend on the range of interest required for the data.

The surface and depth to study must be carefully defined. The surface to study will depend on the variability existing in the place. In certain occasions the characteristics of the soil-water vary more with the depth that with the area.

The moisture in the soil depends mainly on the texture or the particle size distribution. On the other hand, the content of organic matter and the composition of the solution phase can play a determining role in soil moisture function or retention function. Organic matter has a direct effect on the retention function due to its hydrophilic nature and an indirect effect due to the modification of the soil structure that can be affected by the presence of organic matter [12].

**3.3. Granular matrix sensors**

**3.4. Dielectric sensors**

*3.4.1. Time domain reflectometry (TDR)*

same location or be used in a portable way.

connecting the fixed sensors to a data logger.

*3.4.2. Reflectometry in the frequency domain (FDR)*

related empirically to the volumetric content of water.

In it, the evolution of the potential over time is measured.

They have been developed recently (they were patented in 1985 and manufactured commercially since 1989). It measures the electrical resistance between two electrodes inserted in a small cylinder composed of a porous material. Each device is covered by a membrane consisting of a stainless steel coupling, externally covered by a rubber that makes the sensor more durable than the plaster block. However, the recorder is calibrated to give the value in water tension, by means of an equation that takes into account the temperature of the soil estimated or measured near the sensor. The size of the pores in the matrix is greater than that of the pores in the gypsum blocks, allowing greater sensitivity in the more humid range of water content in the soil [14].

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The TDR and FDR probes measure the dielectric constant of the medium, which is an intrinsic property of the medium. The FDR system calculates the humidity of a soil by responding to changes in the dielectric constant of the medium using a frequency domain reflectometry

The TDR system consists of an oscilloscope connected to two or three metal rods that are inserted parallel to the ground. If a difference of power is applied to one end of the rods, the energy is transmitted along the ends to the end, where they are reflected to the oscilloscope.

Some equipment consists of two main parts: the electronic unit and the waveguides. The electronic unit contains the oscilloscope and the central processor, which controls all measurement, display, and storage functions. The waveguides can be installed horizontally or vertically and remain permanently on the ground to make periodic measurements in the

The TDR uses a series of conversion tables to convert the dielectric constant to a percentage of moisture in the soil. There are different conversion tables to be used with the different types of waveguides. It is not necessary to have a different table for the different soil types since the dielectric constant depends more on the amount of water than on the other soil components. The apparatus calculates the average value of the humidity over the total length of the waveguides. The apparatus allows manual measurements or continuous measurements by

The FDR method is also known as a capacitance probe. The electrodes and the adjacent floor form a capacitor whose capacity is a function of the dielectric constant of the soil. This is

A capacitance sensor requires a probe calibration for each floor and horizon to obtain an optimal measurement of volumetric moisture. The volume of soil measured is not dependent

technique known as capacitance, while the TDR uses time domain reflectometry [16].

Currently there are different equipment and measurement techniques for moisture content such as capacitance, reflectometry, geo-electric (called dielectrics), and neutrons. Descriptions of them are developed in the following points.

#### **3.1.  It requires calibration and operation by a licensed operator.

This technique is based on the theory that fast neutrons are thermalized when they collide with a body of similar mass, such as hydrogen nuclei. The energy of the neutrons is transmitted to the protons which causes the neutron "bounce or shock" to be much lower.

The application of this technique consists of three steps: (1) emission of fast neutrons from a radioactive source, (2) attenuation of the velocity of the neutrons after successive collisions with the atoms at the point of emission, and (3) accounting for neutrons with attenuated velocity by a detector near the source.

By means of electrical impulses the neutrons captured by the detector are translated into a digital reading.

To convert the reading of the neutron probe to volumetric moisture, a calibration model is necessary, where the volumetric moisture of the soil is the main factor. There are some factors that influence measurements such as hydrogen from organic matter, chlorine, iron, and boron present in the soil, capable of attenuating neutrons and absorbing thermally neutron nuclei [14].

### **3.2. Tensiometers**

Tensiometers are widely used to measure the available water content of the soil when the matrix potential is high [15]. These devices are simple, they are not very expensive, and they are very practical in agricultural systems.

They consist of a porous ceramic capsule permeable to water and solutes, connected to a pressure gauge by means of a transparent plastic tube that is filled with water, in such a way that the column of water in its interior forms a continuous with the water of the solution of the floor in the surrounding space, through the porous capsule.

The values obtained reflect the soil tension, are negative values, and their operating range is 0−80 kPa, below this value the water column breaks, penetrating the air and invalidating the following measurements.

The tensiometers are insensitive to the osmotic potential of water in the soil and therefore do not provide an adequate measurement of the water potential in soils with significant salinity.

Tensiometers are often used in combination with the neutron sprayer, resistance blocks, or psychrometers to cover the full range of soil moisture. They require relatively frequent maintenance, which consists of adding water plus a solution for the control of algae [14].

#### **3.3. Granular matrix sensors**

effect on the retention function due to its hydrophilic nature and an indirect effect due to the modification of the soil structure that can be affected by the presence of organic matter [12]. Currently there are different equipment and measurement techniques for moisture content such as capacitance, reflectometry, geo-electric (called dielectrics), and neutrons. Descriptions

A neutron probe is a sophisticated and accurate piece of equipment that measures the mois-

This technique is based on the theory that fast neutrons are thermalized when they collide with a body of similar mass, such as hydrogen nuclei. The energy of the neutrons is transmit-

The application of this technique consists of three steps: (1) emission of fast neutrons from a radioactive source, (2) attenuation of the velocity of the neutrons after successive collisions with the atoms at the point of emission, and (3) accounting for neutrons with attenuated

By means of electrical impulses the neutrons captured by the detector are translated into a

To convert the reading of the neutron probe to volumetric moisture, a calibration model is necessary, where the volumetric moisture of the soil is the main factor. There are some factors that influence measurements such as hydrogen from organic matter, chlorine, iron, and boron present in the soil, capable of attenuating neutrons and absorbing thermally neutron nuclei [14].

Tensiometers are widely used to measure the available water content of the soil when the matrix potential is high [15]. These devices are simple, they are not very expensive, and they

They consist of a porous ceramic capsule permeable to water and solutes, connected to a pressure gauge by means of a transparent plastic tube that is filled with water, in such a way that the column of water in its interior forms a continuous with the water of the solution of the

The values obtained reflect the soil tension, are negative values, and their operating range is 0−80 kPa, below this value the water column breaks, penetrating the air and invalidating the

The tensiometers are insensitive to the osmotic potential of water in the soil and therefore do not provide an adequate measurement of the water potential in soils with significant salinity. Tensiometers are often used in combination with the neutron sprayer, resistance blocks, or psychrometers to cover the full range of soil moisture. They require relatively frequent main-

tenance, which consists of adding water plus a solution for the control of algae [14].

ture content in soil. It requires calibration and operation by a licensed operator.

ted to the protons which causes the neutron "bounce or shock" to be much lower.

of them are developed in the following points.

62 Soil Contamination and Alternatives for Sustainable Development

velocity by a detector near the source.

are very practical in agricultural systems.

floor in the surrounding space, through the porous capsule.

**3.1. Neutron probe**

digital reading.

**3.2. Tensiometers**

following measurements.

They have been developed recently (they were patented in 1985 and manufactured commercially since 1989). It measures the electrical resistance between two electrodes inserted in a small cylinder composed of a porous material. Each device is covered by a membrane consisting of a stainless steel coupling, externally covered by a rubber that makes the sensor more durable than the plaster block. However, the recorder is calibrated to give the value in water tension, by means of an equation that takes into account the temperature of the soil estimated or measured near the sensor. The size of the pores in the matrix is greater than that of the pores in the gypsum blocks, allowing greater sensitivity in the more humid range of water content in the soil [14].

### **3.4. Dielectric sensors**

The TDR and FDR probes measure the dielectric constant of the medium, which is an intrinsic property of the medium. The FDR system calculates the humidity of a soil by responding to changes in the dielectric constant of the medium using a frequency domain reflectometry technique known as capacitance, while the TDR uses time domain reflectometry [16].

### *3.4.1. Time domain reflectometry (TDR)*

The TDR system consists of an oscilloscope connected to two or three metal rods that are inserted parallel to the ground. If a difference of power is applied to one end of the rods, the energy is transmitted along the ends to the end, where they are reflected to the oscilloscope. In it, the evolution of the potential over time is measured.

Some equipment consists of two main parts: the electronic unit and the waveguides. The electronic unit contains the oscilloscope and the central processor, which controls all measurement, display, and storage functions. The waveguides can be installed horizontally or vertically and remain permanently on the ground to make periodic measurements in the same location or be used in a portable way.

The TDR uses a series of conversion tables to convert the dielectric constant to a percentage of moisture in the soil. There are different conversion tables to be used with the different types of waveguides. It is not necessary to have a different table for the different soil types since the dielectric constant depends more on the amount of water than on the other soil components. The apparatus calculates the average value of the humidity over the total length of the waveguides. The apparatus allows manual measurements or continuous measurements by connecting the fixed sensors to a data logger.

#### *3.4.2. Reflectometry in the frequency domain (FDR)*

The FDR method is also known as a capacitance probe. The electrodes and the adjacent floor form a capacitor whose capacity is a function of the dielectric constant of the soil. This is related empirically to the volumetric content of water.

A capacitance sensor requires a probe calibration for each floor and horizon to obtain an optimal measurement of volumetric moisture. The volume of soil measured is not dependent on the type of soil or water content and approaches a cylinder 10 cm high with a diameter of about 25 cm, assuming there are no spaces with air [17].

*4.1.1. Inhibitors of photosynthesis*

this type of herbicide.

absorbed by the roots.

nitriles, benzothiadiazoles, and amides [23].

tracks, industrial zones, and warehouses [24].

(b) Herbicides that uncouple the electron transport chain.

The inhibitors of photosynthesis can be classified into mobile or systemic herbicides and nonmobile or contact herbicides. The inhibitors of photosynthesis include the chemical families of the triazines, triazinones, triazolinones, phenylureas, and uracils and the contact ones to the

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It is subdivided into four groups, the first three being those that act on the luminous phase of photosynthesis. Besides, they are not only used in agriculture but also to clean land, railroad

Ureas, uracils, and triazines correspond to this group of herbicides. This type of herbicides are applied to the soil and absorbed by the roots, transported via xylem to the aerial part, reach the chloroplasts of the leaves, and there inhibit the light phase. Any plant can be affected by

They have the ability to capture the electrons preventing oxidation and forming free radicals "superoxides." Superoxides are very powerful oxidants, which oxidize the unsaturated lipids of the chloroplast membranes, losing structure, and the chloroplast stops working. These molecules are formulated as bromides and chlorides; they are very soluble and very easily

Acylanilides, hydroxybenzonitriles, dinitrophenols, pyridazines, N-phenylcarbamates belong to this group. In photosynthesis, ATP is synthesized from the thylakoid membrane (sites of the photochemical reactions of photosynthesis) of chloroplast cells of plants. The photon particles from the sunlight excite the chloroplast thylakoid membrane, which in turn converts this excitation into ATP's chemical energy. Found in all forms of life, ATP is often referred to as the "molecular unit of currency" of intracellular energy transfer. It is used by the plant for metabolic processes. They are applied differently. They can present an important toxicity for

They act at some point in the synthesis of lycopene. The most important is the amino triazole.

**a.** Herbicides that alter the biosynthesis of aromatic amino acids: These amino acids are then part of proteins. The herbicide capable of inhibiting the synthesis of aromatic amino acids

(a) Herbicides that inhibit the transfer of electrons by inhibiting photosynthesis.

(c) Herbicides that prevent the formation of adenosine triphosphate (ATP).

animals. Hence, some can be used as herbicides and fungicides.

*4.1.2. Those that alter the biosynthesis of metabolites other than carbohydrates*

(d) Herbicides that alter the biosynthesis of carotenoids.

The most used inhibitors of photosynthesis are type (a).

They are subdivided into three groups:

All capacitance sensors installed in floors, even with similar characteristics, must be calibrated with the aim of improving their accuracy given the influence on the measurement of other factors independent of the moisture content such as pH variability or electrical conductivity inside of the porous matrix.

Capacitance sensors are the most economical and easy to install. In addition, it allows a continuous recording of the moisture values in the soil, enabling direct information and in real time. They are very useful for the planning of alert monitoring systems. They can be used as substitutes for neutron probes.


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