**4. Acknowledgment**

This work is a joint study of Norio Tanaka and Shigeru Takarada of Dainichiseika Color & Chemicals Mfg. Co., Ltd., Noriyasu Siga and Kazuya Ohta of Trimatiz, Ltd., Hirofumi Watanabe, Toshimasa Tamura and Keiji Negi of Inter Energy Co., Ltd. and Masaki Kubo of HIMEJI RIKA CO., LTD.. This study is supported by NEDO.


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## Meet the editors

Jesmine Khan is affiliated with the Faculty of Medicine, Universiti Teknologi MARA (UiTM), Malaysia as an Associated Professor. She obtained her MBBS degree from Mymensingh Medical College, Dhaka University, Bangladesh. She has special interest in nutrition and acquired her PhD degree in surgical nutrition from Osaka University Graduate School of Medicine, Osaka, Japan. She has vast experience in teaching at home and abroad.

She is active in writing, has published numerous articles in scientific journals, works as an editorial board member and reviewer of academic journals and is involved in nutrition related research and activities.

Dr. Po-Shiuan Hsieh is currently a Professor and Director at the Institute of Preventive Medicine, National Defense Medical Center in Taiwan. His research interests focus on obesity and weight control as well as the pathological links between obesity, metabolic syndrome, and type 2 diabetes, especially the causal links between the major characteristics of metabolic syndrome, the role of inflammation in the development of insulin resistance

and impairment of pancreatic insulin secretion in type 2 diabetes; the involvement of hepatic inflammation in the pathogenesis of type 2 diabetes; and the potential therapeutic application in prevention and treatment of metabolic syndromes and diabetes. He has served as the President of the Chinese Physiology Society from 2012 to 2016. He is also the editor-in-chief of two international journals: Chinese Journal of Physiology (SCI) and Journal of Medical Science.

Contents

**Section 1**

**Section 2**

*by Yuan Lu*

**Section 3**

*by Avital Schurr*

*and Karina Elgueta*

*by Gaffar S. Zaman*

Metabolic Syndrome

*by Suzanne Fouad Soliman*

**Section 4**

Pathogenesis of Insulin Resistance

*by Armindo Miguel de Jesus Sousa de Araújo Ribeiro*

Metabolic Syndrome: Impact of Dietary Therapy

*by Po-Shiuan Hsieh*

**Preface XI**

Introductory Section **1**

**Chapter 1 3**

Energy Metabolism **7**

**Chapter 2 9**

**Chapter 3 31**

Diabetes Mellitus **49**

**Chapter 4 51**

**Chapter 5 71**

Metabolic Syndrome **91**

**Chapter 6 93**

**Chapter 7 111**

Cerebral Energy Metabolism: Measuring and Understanding Its Rate

Diabetes Mellitus: A Group of Genetic-Based Metabolic Diseases *by Lilian Sanhueza, Pilar Durruty, Cecilia Vargas, Paulina Vignolo*

Prologue: Energy Metabolism and Weight Control

Brown Adipose Tissue Energy Metabolism

### Contents

Preface

Cellular metabolism is the process within the cells by which food and nutrients are converted into energy. Metabolic processes in the cells must be under strict regulation to maintain homeostasis in the body. There are multiple levels of metabolic regulation in the body. For effective metabolism, different enzymes,

Any disorder of metabolism, either due to imbalance of the regulating enzymes or the hormones, is characterized by the inability to properly utilize and/or store energy. Metabolic disorders affect the ability of the cells to perform critical biochemical reactions that involve the processing or transport of carbohydrates (sugars and starches), proteins (amino acids), or lipids (fatty acids). Disorders of metabolism can be either congenital or acquired. Congenital metabolic disorders are usually due to mutation of genes resulting in deficiency or absence of particular

enzymes. Acquired disorders are usually complex and both genes as well as

The objective of this book was to collect and compile articles on the metabolism of fuels in the brown adipose tissue and brain as well as updates on several disorders of metabolism ranging from diabetes mellitus, insulin resistance, hypothyroidism, and metabolic syndrome to some congenital disorders such as glycogen storage disease

This book is divided into nine sections. The Introductory section includes the prologue entitled energy metabolism and weight control by Prof. Hsieh Po-Shiuan.

The second section, 'Energy Metabolism', includes two chapters. The first chapter, 'Brown Adipose Tissue Energy Metabolism', is written by Prof. Lu Yuan. This chapter describes the mechanism underlying brown fat energy metabolism and the therapeutic potential in metabolic disorders, especially obesity-related metabolic

The second chapter, 'Cerebral Energy Metabolism: Measuring and Understanding its Rate', by Avital Schurr summarizes the history of the science behind the current knowledge of the biochemical processes responsible for the production of adenosine triphosphate (ATP) in the brain. It also briefly reviews the various techniques used to measure cerebral metabolic rates of oxygen and glucose, two of the substrates involved in ATP production and elaborates on the potential of measuring the cerebral metabolic rate of lactate to improve understanding of brain

The third section, 'Diabetes Mellitus', includes the chapter entitled 'Diabetes

Mellitus: A Group of Genetic-based Metabolic Diseases' written by Lilian Sanhueza. This chapter discusses the overall picture of different types of diabetes mellitus including the main clinical presentations with the proven genetic basis. In this chapter, emphasis is given to the candidate genes for Diabetes Mellitus type 1

coenzymes, and hormones are required.

environmental factors are responsible for them.

and urea cycle disorder.

diseases.

energy metabolism.


**Abstract**

Many countries restrict the ability of organ donor families and their recipients to communicate with each other; many make it virtually impossible. These restrictions were made for the best of reasons, mainly because of fears that one side or the other might suffer psychological damage. In the United States, however, for more than 25 years, communication has been strongly encouraged if both parties want it and under conditions set by their medical advisers. In literally tens of thousands of cases, a great majority of those contacts, which can range from the exchange of anonymous letters to face-to-face meetings, have proved to be therapeutic for both sides and significant problems have been very rare. Indeed, it is the families who are kept apart who may suffer most. The author is an American journalist, whose seven-year old son was shot on a family vacation in Italy whose organs and corneas were donated there. He and his wife have met all seven recipients and everyone, he says, has benefited.

**Keywords:** organ donor communication, organ donor contacts, transplant families meet, organ donor families, transplant families, Nicholas Green, Reg Green


**3.3 Thermokinetics as a means to control macrometric length scale molecular organizations through molten to solid transitions under mechanical stress**

A newly developed phenomenological model for pattern formation and growth kinetics of polymers uses thermodynamic parameters, as thermo-mechanical constraints and thermal gradient. It is a system of physically-based morphological laws-taking into account the kinetics of structure formation and similarities between polymer physics and metallurgy within the framework of Avrami's assumptions.

Polymer crystallization is a coupled phenomenon. It results from the appearance (nucleation in a more or less sporadic manner) and the development (growth) of semi-crystalline entities (*e.g.*, spherulites) (Gadomski & Luczka, 2000; Panine et al., 2008). The entities grow in all available directions until they impinge on one another. The crystallization kinetics is described in an overall manner by the fraction *(t)* (surface fraction in two dimensions (2D) or volume fraction in three dimensions (3D)) transformed into morphological entities (disks in 2D or spheres in 3D) at each time *t.*

The introduction of an overall kinetics law for crystallization into models for polymer processing is usually based on the Avrami-Evans's (AE) theory (Avrami, 1939, 1940, 1941; Evans, 1945). To treat non-isothermal crystallization, simplifying additional assumptions have often been used, leading to analytical expressions and allowing an easy determination of the physical parameters, *e.g.*, Ozawa (1971) and Nakamura et al. (1972) approaches. To avoid such assumptions, a trend is to consider the general AE equation, either in its initial form as introduced by Zheng & Kennedy (2004), or after mathematical transformations as presented by Haudin & Chenot (2004) and recalled here after.

## **General equations for quiescent crystallization**

The macroscopic mechanism for the nucleation event proposed by Avrami remains the most widely used, partly because of its firm theoretical basis leading to analytical mathematical equations. In the molten state, there exist zones, the potential nuclei, from which the crystalline phase is likely to appear. They are uniformly distributed throughout the melt, with an initial number per unit volume (or surface) *N0*. *N0* is implicitly considered as constant. The potential nuclei can only disappear during the transformation according to activation or absorption ("swallowing") processes. An activated nucleus becomes a growing entity, without time lag. Conversely, a nucleus which has been absorbed cannot be activated any longer. In the case of a complex temperature history *T(t)*, the assumption of a constant number of nuclei *N0* is no more valid, because *N0 = N0(T) = N0(T(t))* may be different at each temperature. Consequently, additional potential nuclei can be created in the nontransformed volume during a cooling stage. All these processes are governed by a set of differential equations (Haudin & Chenot, 2004), differential equations seeming to be most suitable for a numerical simulation (Schneider et al., 1988).

### **Avrami's Equation**

654 Thermodynamics – Interaction Studies – Solids, Liquids and Gases

The local affinities of AuNPs with PEO/SCCO2 stabilize the thermodynamically unstable SCCO2-plasticized network and keep it stable with time, which cannot be observed without the insertion of gold nano-particles mainly because of diffusion effect of the solvent (Boyer et al., 2006a)**.** The mean height of AuNPs layer is about 3 nm, which is 20 times smaller than PEO cylinders with a 60 nm in length. Thus PEO channels could be considered as nano-dots receptors, schematically as a "compact core–shell model" consisting of a spherical or isotropic AuNP "core" embedded into a PEO channel "shell", consequently leading to isotropic two- and three-dimensional materials. Nicely, AuNPs clusters on PEO channel heads can be numerically expressed. The presence of, 4, 5 and 8 single Au nano-clusters for *m* = 114, 272 and 454 is identified, respectively. It represents a linear function between the number of AuNPs on swollen PEO *versus* SCCO2-swollen diameter with half of ligands of

From this understanding, a fine thermodynamic-mechanical control over extended *T* and *P* ranges would provide a precious way to produce artificial and reliable nanostructured materials. SCCO2-based technology guides a differential diffusion of hydrophilic AuNPs to cluster selectively along the hydrophilic PEO scaffold. As a result, a highly organized hybrid metal-polymer composite is produced. Such understanding would be the origin of a 2D

A newly developed phenomenological model for pattern formation and growth kinetics of polymers uses thermodynamic parameters, as thermo-mechanical constraints and thermal gradient. It is a system of physically-based morphological laws-taking into account the kinetics of structure formation and similarities between polymer physics and metallurgy

Polymer crystallization is a coupled phenomenon. It results from the appearance (nucleation in a more or less sporadic manner) and the development (growth) of semi-crystalline entities (*e.g.*, spherulites) (Gadomski & Luczka, 2000; Panine et al., 2008). The entities grow in all available directions until they impinge on one another. The crystallization kinetics is

or volume fraction in three dimensions (3D)) transformed into morphological entities (disks

The introduction of an overall kinetics law for crystallization into models for polymer processing is usually based on the Avrami-Evans's (AE) theory (Avrami, 1939, 1940, 1941; Evans, 1945). To treat non-isothermal crystallization, simplifying additional assumptions have often been used, leading to analytical expressions and allowing an easy determination of the physical parameters, *e.g.*, Ozawa (1971) and Nakamura et al. (1972) approaches. To avoid such assumptions, a trend is to consider the general AE equation, either in its initial form as introduced by Zheng & Kennedy (2004), or after mathematical transformations as

The macroscopic mechanism for the nucleation event proposed by Avrami remains the most widely used, partly because of its firm theoretical basis leading to analytical mathematical equations. In the molten state, there exist zones, the potential nuclei, from which the crystalline phase is likely to appear. They are uniformly distributed throughout the melt,

*(t)* (surface fraction in two dimensions (2D)

**3.3 Thermokinetics as a means to control macrometric length scale molecular organizations through molten to solid transitions under mechanical stress**

AuNPs linked with PEO polymer chain.

within the framework of Avrami's assumptions.

described in an overall manner by the fraction

presented by Haudin & Chenot (2004) and recalled here after.

**General equations for quiescent crystallization**

in 2D or spheres in 3D) at each time *t.*

nanocrystal growth.

Avrami's theory (Avrami, 1939, 1940, 1941) expresses the transformed volume fraction ( )*t* by the general differential equation **eq. (8)**:

$$\frac{d\alpha(t)}{dt} = (1 - \alpha(t)) \frac{d\tilde{\alpha}(t)}{dt} \tag{8}$$

( )*t* is the "extended" transformed fraction, which, for spheres growing at a radial growth rate *G(t)*, is given by **eq. (9)**:

$$\tilde{\alpha}(t) = \frac{4\pi}{3} \frac{t}{0} \left[ \int\_{\tau}^{t} G(u) du \right]^3 \frac{d\tilde{N}\_a(\tau)}{d\tau} d\tau \tag{9}$$

*dN t dt a*( )/ is the "extended" nucleation rate, 3 4 ( ) 3 *t G u du* is the volume at time *τ* of a

sphere appearing at time *t* , and ( ) *a dN* are spheres created per unit volume between *τ* and *τ* + *dτ.*

### **Assumptions on Nucleation**

The number of potential nuclei decreases by activation or absorption, and increases by creation in the non-transformed volume during cooling. All these processes are governed by the following equations:

$$\frac{dN(t)}{dt} = -\frac{dN\_a(t)}{dt} - \frac{dN\_c(t)}{dt} + \frac{dN\_g(t)}{dt} \tag{10a}$$

$$\frac{dN\_a(t)}{dt} = q(t)N(t) \tag{10b}$$

$$\frac{dN\_c(t)}{dt} = \frac{N(t)}{1 - \alpha(t)} \frac{d\alpha(t)}{dt} \tag{10c}$$

$$\frac{dN\_{\mathcal{S}}(t)}{dt} = (1 - \alpha(t)) \frac{dN\_0(T)}{dT} \frac{dT}{dt} \tag{10d}$$

Thermodynamics and Thermokinetics to Model Phase Transitions of Polymers

**General equations for shear-induced crystallization**

volume fraction is written as (Haudin et al., 2008):

spherulites and into shish-kebabs, respectively.

Modification of **eqs. (8)** and **(10a)** gives:

functions, *e.g.*:

*t* and

*t* and

*N t*

*A A*1 , with

shear rate

**Spherulitic Morphology**

' is becoming:

the shear rate.

over Extended Temperature and Pressure Ranges Under Various Hydrostatic Fluids 657

the frequency of activation *q* of these nuclei and the growth rate *G* . In isothermal conditions, they are constant. In non-isothermal conditions, they are defined as temperature

Crystallization can occur in the form of spherulites, shish-kebabs, or both. The transformed

*dt dt dt dt dt dt*

( ) ( ) (1 ( )) *dt dt t dt dt*

( ) () () ( ) ( ) *g a c dN t dN t dN t dN t dN t dt dt dt dt dt*

is the number of nuclei per unit volume generated by shear. Two situations are

if ( ) 0 *aAN*

if ( ) 0 *aAN*

*a* and *A*1 are material parameters, eventually thermo-dependent. As a first approximation,

If crystallization proceeds during shear, only the liquid fraction is exposed to shear and the

1/3

  possible, *i.e.*, crystallization occurs after shear or crystallization occurs during shear. If crystallization during shear remains negligible, the number of shear-generated nuclei is:

( )

*aAN*

0

*dN*

*dt*

> *dN dt*

*t* are the thermo-dependent volume fractions transformed *versus* time into

*t* are the actual and extended volume fractions of spherulites, respectively.

*N N N TT* 0 00 exp ( ) 01 0 (20a)

*q q qT T* 0 10 exp ( ) (20b)

*G G GT T* 0 10 exp ( ) (20c)

(21)

(22)

(24a)

(24b)

' /(1 ) (25)

(23)

( ), ( ), ( ), ( ) *Nt N t N t N t acg* are the number of potential, activated, absorbed and generated (by cooling) nuclei per unit volume (or surface) at time *t*, respectively. *q(t)* is the activation frequency of the nuclei at time *t*. The "extended" quantities , *N Na* are related to the actual ones by:

$$N = (1 - \alpha)\bar{N}\tag{11a}$$

$$\frac{dN\_a}{dt} = qN = (1 - \alpha)\frac{d\tilde{N}\_a}{dt} \tag{11b}$$

#### **The System of Differential Equations**

The crystallization process equations are written into a non-linear system of six, **eqs. (12, 13a, 14-17)**, or seven, **eqs. (12, 13b, 14-18)**, differential equations in 2D or 3D conditions, respectively (Haudin & Chenot, 2004):

$$\frac{dN}{dt} = -N\left(q + \frac{1}{1-a}\frac{da}{dt}\right) + (1-a)\frac{dN\_0(T)}{dT}\frac{dT}{dt} \tag{12}$$

$$\frac{d\alpha}{dt} = 2\pi (1 - \alpha) \mathbf{G} (F\tilde{\mathbf{N}}\_a - P) \tag{13a}$$

$$\frac{da}{dt} = 4\pi (1 - a) \mathbf{G} (F^2 \tilde{N}\_a - 2FP + \mathbf{Q})\tag{13b}$$

$$\frac{dN\_a}{dt} = qN\tag{14}$$

$$\frac{d\tilde{N}\_a}{dt} = \frac{qN}{1 - \alpha} \tag{15}$$

$$\frac{dF}{dt} = G \tag{16}$$

$$\frac{dP}{dt} = F \frac{d\tilde{\mathbf{N}}\_a}{dt} = F \frac{qN}{1 - a} \tag{17}$$

$$\frac{d\mathbf{Q}}{dt} = \mathbf{F}^2 \frac{d\tilde{\mathbf{N}}\_a}{dt} = \mathbf{F}^2 \frac{qN}{1 - a} \tag{18}$$

The initial conditions at time *t* = 0 are:

$$N(0) = N\_0$$

$$\alpha(0) = N\_a(0) = \tilde{N}\_a(0) = F(0) = P(0) = Q(0) = 0\tag{19}$$

*F*, *P* and *Q* are three auxiliary functions added to get a first-order ordinary differential system. The model needs three physical parameters, the initial density of potential nuclei *N0*, the frequency of activation *q* of these nuclei and the growth rate *G* . In isothermal conditions, they are constant. In non-isothermal conditions, they are defined as temperature functions, *e.g.*:

$$N\_0 = N\_{00} \exp\left(-N\_{01}(T - T\_0)\right) \tag{20a}$$

$$q = q\_0 \exp\left(-q\_1(T - T\_0)\right) \tag{20b}$$

$$G = G\_0 \exp\left(-G\_1(T - T\_0)\right) \tag{20c}$$

### **General equations for shear-induced crystallization**

Crystallization can occur in the form of spherulites, shish-kebabs, or both. The transformed volume fraction is written as (Haudin et al., 2008):

$$\frac{d\alpha\left(t\right)}{dt} = \frac{d\beta\left(t\right)}{dt} + \frac{d\kappa\left(t\right)}{dt} \tag{21}$$

 *t* and *t* are the thermo-dependent volume fractions transformed *versus* time into spherulites and into shish-kebabs, respectively.

### **Spherulitic Morphology**

656 Thermodynamics – Interaction Studies – Solids, Liquids and Gases

( ), ( ), ( ), ( ) *Nt N t N t N t acg* are the number of potential, activated, absorbed and generated (by cooling) nuclei per unit volume (or surface) at time *t*, respectively. *q(t)* is the activation frequency of the nuclei at time *t*. The "extended" quantities , *N Na* are related to the actual

> *N N* (1 )

(1 ) *a a dN dN qN dt dt*

The crystallization process equations are written into a non-linear system of six, **eqs. (12, 13a, 14-17)**, or seven, **eqs. (12, 13b, 14-18)**, differential equations in 2D or 3D conditions,

> *dN d dN T dT N q dt dt dT dt*

*dt*

>

0 1 ( ) (1 ) 1

2 (1 ) ( ) *a d G FN P*

2 4 (1 ) ( 2 ) *a d G F N FP Q dt*

1 *a dN qN dt*

*dF G*

*a dP dN qN F F*

2 2

0 *N N* (0)

(0) (0) (0) (0) (0) (0) 0

*F*, *P* and *Q* are three auxiliary functions added to get a first-order ordinary differential system. The model needs three physical parameters, the initial density of potential nuclei *N0*,

*a dQ dN qN F F*

*dt dt*

*dt dt*

1

1

*N N FPQ a a* (19)

(12)

(13a)

(13b)

*a dN qN dt* (14)

(15)

*dt* (16)

(17)

(18)

(11a)

(11b)

ones by:

**The System of Differential Equations**

respectively (Haudin & Chenot, 2004):

The initial conditions at time *t* = 0 are:

Modification of **eqs. (8)** and **(10a)** gives:

$$\frac{d\beta(t)}{dt} = (1 - \alpha(t)) \frac{d\bar{\beta}(t)}{dt} \tag{22}$$

$$\frac{dN(t)}{dt} = -\frac{dN\_a(t)}{dt} - \frac{dN\_c(t)}{dt} + \frac{dN\_g(t)}{dt} + \frac{dN\_\gamma(t)}{dt} \tag{23}$$

 *t* and *t* are the actual and extended volume fractions of spherulites, respectively. *N t* is the number of nuclei per unit volume generated by shear. Two situations are possible, *i.e.*, crystallization occurs after shear or crystallization occurs during shear. If crystallization during shear remains negligible, the number of shear-generated nuclei is:

$$\frac{dN\_{\gamma}}{dt} = a\dot{\gamma}(A - N\_{\gamma}) \quad \text{if} \quad a\dot{\gamma}(A - N\_{\gamma}) \ge 0 \tag{24a}$$

$$\frac{dN\_\gamma}{dt} = 0 \text{ if } \ a\dot{\gamma}(A - N\_\gamma) \le 0 \tag{24b}$$

*a* and *A*1 are material parameters, eventually thermo-dependent. As a first approximation, *A A*1 , with the shear rate.

If crystallization proceeds during shear, only the liquid fraction is exposed to shear and the shear rate ' is becoming:

$$
\dot{\gamma}' = \dot{\gamma} \left/ \left( 1 - \alpha \right)^{1/3} \right. \tag{25}
$$

Thermodynamics and Thermokinetics to Model Phase Transitions of Polymers

*dN*

*dt*

1/3

nuclei for the oriented structure is given as:

activation frequency of the nuclei, *b* and *B1* the material parameters:

*dt*

1

1/3

*dM d M w dt dt*

The initial conditions at time *t* = 0 are:

**Shish-Kebab Morphology**

 and

*Mt* , *Ma t* , *Mc t* , *M*

shish-kebab,

over Extended Temperature and Pressure Ranges Under Various Hydrostatic Fluids 659

1 ( )

1 1

*N N FPQ a a* (38)

(40)

*dt*

(42)

*dt* (43)

(44)

(41)

*t* are the numbers of potential, activated, absorbed and

(37a)

(39)

(37b)

*aA N*

1 1/3 1

0 *N N* (0) (0) (0) (0) (0) (0) (0) 0

> *N* (0) 0

Firstly are introduced the notions of real and extended transformed volume fractions of

 (1 ) *dt dt dt dt*

 *a c dM t dM t dM t dM t dt dt dt dt*

generated (by shear) nuclei per unit volume, respectively. In the same way as for the spherulitic morphology, a set of differential equations can be defined where *w* is the

> 1 1

> >

2 (1 ) ( ) *a d H RM S*

1 1

1 1/3

*M M d b B*

*a dM wM*

1 *a dM wM dt*

( )*t* is the total transformed volume fraction for both spherulitic and oriented phases. Shish-kebabs are modelled as cylinders with an infinite length. The growth rate *H* is deduced from the radius evolution of the cylinder. The general balance of the number of

, respectively. Both are related by **eq. (39)**:

*dN N N d a A dt dt*

By defining *N* as the extended number of nuclei per unit volume generated by shear in the total volume, then:

$$\frac{d\tilde{N}\_{\gamma}}{dt} = a\dot{\gamma}'(A\_1\dot{\gamma}' - \tilde{N}\_{\gamma})\tag{26}$$

The number *N*of nuclei generated by shear in the liquid fraction is:

$$N\_{\gamma} = (1 - \alpha)\bar{N}\_{\gamma} \tag{27}$$

Under shear, the activation frequency of the nuclei increases. If the total frequency is the sum of a static component, *st q* , function of temperature, and of a dynamic one, *flow q* , then:

$$
\eta = \eta\_{st} + \eta\_{flow} \tag{28}
$$

*flow q* is given by **eq. (29)** where as a first approximation 2 02 *q q* and 3 *q* is constant.

$$q\_{flow} = q\_2(1 - \exp(-q\_3 \dot{\gamma})) \tag{29}$$

The system of differential equations **(12, 13b, 14-18)** is finally replaced by a system taking the influence of shear into account through the additional unknown *N* and through the dynamic component of the activation frequency *flow q* . Two cases are considered, *i.e.*, crystallization occurs after shear **(37a)** or crystallization occurs under **(37b)** shear.

$$\frac{dN}{dt} = -N\left(q + \frac{1}{1-\alpha}\frac{d\alpha}{dt}\right) + (1-\alpha)\frac{dN\_0(T)}{dT}\frac{dT}{dt} + \frac{dN\_\gamma}{dt} \tag{30}$$

$$\frac{d\mathcal{J}}{dt} = 4\pi (1 - \alpha) \mathbf{G} (\mathbf{F}^2 \tilde{\mathcal{N}}\_a - 2FP + \mathcal{Q}) \tag{31}$$

$$\frac{dN\_a}{dt} = qN\tag{32}$$

$$\frac{d\tilde{N}\_a}{dt} = \frac{qN}{1-a} \tag{33}$$

$$\frac{dF}{dt} = G \tag{34}$$

$$\frac{dP}{dt} = F \frac{d\tilde{N}\_a}{dt} = F \frac{qN}{1 - \alpha} \tag{35}$$

$$\frac{dQ}{dt} = \mathbf{F}^2 \frac{d\tilde{\mathbf{N}}\_a}{dt} = \mathbf{F}^2 \frac{qN}{1 - \alpha} \tag{36}$$

$$\frac{dN\_{\mathcal{V}}}{dt} = a\dot{\boldsymbol{\chi}}(A\_1\dot{\boldsymbol{\chi}} - N\_{\mathcal{\boldsymbol{V}}}) \tag{37a}$$

$$\frac{dN\_\gamma}{dt} = a\dot{\gamma} \left( \left( 1 - \alpha \right)^{1/3} A\_1 \dot{\gamma} - \frac{N\_\gamma}{\left( 1 - \alpha \right)^{1/3}} \right) - \frac{N\_\gamma}{1 - \alpha} \frac{d\alpha}{dt} \tag{37b}$$

The initial conditions at time *t* = 0 are:

$$\begin{array}{c} N(0) = N\_0\\ \alpha(0) = N\_a(0) = \tilde{N}\_a(0) = F(0) = P(0) = Q(0) = 0\\ N\_\gamma(0) = 0 \end{array} \tag{38}$$

#### **Shish-Kebab Morphology**

658 Thermodynamics – Interaction Studies – Solids, Liquids and Gases

*dN*

*dt*

*flow q* is given by **eq. (29)** where as a first approximation 2 02 *q q*

the influence of shear into account through the additional unknown *N*

crystallization occurs after shear **(37a)** or crystallization occurs under **(37b)** shear.

of nuclei generated by shear in the liquid fraction is:

*N N* (1 )

Under shear, the activation frequency of the nuclei increases. If the total frequency is the sum of a static component, *st q* , function of temperature, and of a dynamic one, *flow q* , then:

2 3 (1 exp( )) *flow qq q*

The system of differential equations **(12, 13b, 14-18)** is finally replaced by a system taking

dynamic component of the activation frequency *flow q* . Two cases are considered, *i.e.*,

0 1 ( ) (1 ) 1 *dN d dN T dT dN N q dt dt dT dt dt*

> 2 4 (1 ) ( 2 ) *a d G F N FP Q dt*

> > 1 *a dN qN dt*

> > > *dF G*

*a dP dN qN F F*

2 2

*a dQ dN qN F F*

*dt dt*

*dt dt*

1

1

as the extended number of nuclei per unit volume generated by shear in the

1 ( )

(26)

*st flow qq q* (28)

(27)

and 3 *q* is constant.

(29)

(31)

*a dN qN dt* (32)

*dt* (34)

(35)

(36)

(30)

and through the

(33)

*aA N*

By defining *N*

The number *N*

total volume, then:

Firstly are introduced the notions of real and extended transformed volume fractions of shish-kebab, and , respectively. Both are related by **eq. (39)**:

$$\frac{d\kappa\left(t\right)}{dt} = (1 - \alpha)\frac{d\tilde{\kappa}\left(t\right)}{dt} \tag{39}$$

( )*t* is the total transformed volume fraction for both spherulitic and oriented phases. Shish-kebabs are modelled as cylinders with an infinite length. The growth rate *H* is deduced from the radius evolution of the cylinder. The general balance of the number of nuclei for the oriented structure is given as:

$$\frac{dM\left(t\right)}{dt} = -\frac{dM\_a\left(t\right)}{dt} - \frac{dM\_c\left(t\right)}{dt} + \frac{dM\_\gamma\left(t\right)}{dt} \tag{40}$$

*Mt* , *Ma t* , *Mc t* , *M t* are the numbers of potential, activated, absorbed and generated (by shear) nuclei per unit volume, respectively. In the same way as for the spherulitic morphology, a set of differential equations can be defined where *w* is the activation frequency of the nuclei, *b* and *B1* the material parameters:

$$\begin{split} \frac{dM}{dt} &= -M \left( w + \frac{1}{1 - \alpha} \frac{da}{dt} \right) \\ &+ b\dot{\gamma} \left( \left( 1 - \alpha \right)^{1/3} B\_1 \dot{\gamma} - \frac{M}{\left( 1 - \alpha \right)^{1/3}} \right) - \frac{M}{1 - \alpha} \frac{da}{dt} \end{split} \tag{41}$$

$$\frac{d\kappa}{dt} = 2\pi (1 - a) H (R\tilde{M}\_a - S) \tag{42}$$

$$\frac{dM\_a}{dt} = wM\tag{43}$$

$$\frac{d\tilde{M}\_a}{dt} = \frac{wM}{1 - \alpha} \tag{44}$$

$$\frac{d\mathcal{R}}{dt} = H\tag{45}$$

Thermodynamics and Thermokinetics to Model Phase Transitions of Polymers

over Extended Temperature and Pressure Ranges Under Various Hydrostatic Fluids 661

conditions **(eqs. 20a-c)**. The exponential temperature evolution of the three key parameters *N*0, *q*, *G* is possibly calculated from the values of the physical parameters obtained in three different ways: firstly, an approximate physical analysis with direct determination from the experiments *(APA)*, secondly, the use of the Genetic Algorithm method for an optimization based on several experiments (at least 5) done with the same specimen, thirdly, an optimization based on several experiments (at least 8) involving different polymer samples for which an important dispersion of the number of nuclei is observed (Haudin et al., 2008, Boyer et al., 2009). These sets of optimized temperature functions made it possible to validate the mathematical model in the 2D version, as illustrated in **Fig. 5.a-b-inserts**. The selected polymer is a polypropylene that is considered as a 'model material' because of its

aptitude to crystallize with well-defined spherulitic entities in quiescent conditions.

kinetics under low shear with enough accuracy, when the entities are spherulitic.

d *N*/dt ( *N*

structure have to be optimized.

not exceed 19%.

Shear-induced crystallization, with a spherulitic morphology, gives access to the function

time and to the shear dependence of the activation frequency for different relatively low shear rates (up to 20 s-1). A set of seven optimized parameters are identifiable: *N*00, *q*0, *G*0 from quiescent isothermal crystallization, and ( 02 3 1 *q qAa* ,, , ) from isothermal shear-induced crystallization. The agreement between experiment and theory is better for higher shear rates associated with a shorter total time of crystallization. The mean square error does not exceed 12 %, the average mean square error for 5 s-1 is equal to 6.7 %. The agreement between experiment and theory is less satisfactory for the number of spherulites, the mean square error reaches 25 %. Then, the new model is able to predict the overall crystallization

Shear-induced crystallization, with both a spherulitic and an oriented morphology, is a different task. High shear rates (from 75 s-1) enhance all the kinetics (nucleation, growth, overall kinetics) and lead to the formation of micron-size fibrillar (thread-like) structures immediately after shear, followed by the appearance of unoriented spherulitic structures at the later stages **(Fig. 6insert)**. The determination of the parameters for this double crystallization becomes a complicated task for a twofold reason: the quantitative data for both oriented and spherulitic structures are not available at high shear rate, and the double crystallization kinetics model requires to additionally determine the four parameters ( 1 *wHB b* ,,, ). So, optimization is based only on the evolution of the total transformed volume fraction **(eq. 21)**. Parameters characterizing quiescent crystallization ( 00 0 0 *N qG* , , ) and shearinduced crystallization with the spherulitic morphology ( 02 3 1 *q qAa* ,, , ) are taken from the previous 'smooth' analysis, so that four parameters ( 1 *wHB b* ,,, ) characterizing the oriented

**Fig. 6.** gathers the experimental and theoretical variations of the total transformed volume fraction for different shear rates. At the beginning, the experimental overall kinetics is faster than the calculated one most probably because the influence of shear rate on the activation frequency of the oriented structure is not taken into account. Since with higher shear rate thinner samples (~30 µm at 150 s-1) are used, and since numerically the growth of entities is considered as three dimensional, the condition of 3D experiment seems not perfectly respected and the experiments give a slower evolution at the end. The mean square errors between numerical and experimental evolutions of the total transformed volume fraction do

is the number of nuclei per unit volume generated by shear **(eq. 23)**) *versus*

$$\frac{dS}{dt} = R\frac{d\tilde{M}\_a}{dt} = R\frac{wM}{1-a} \tag{46}$$

*F*, *P, Q*, *R* and *S* are five auxiliary functions giving a first-order ordinary differential system. The initial conditions at time *t* = 0 are:

$$M(0) = M\_0$$

$$\kappa(0) = M\_a(0) = \tilde{M}\_a(0) = R(0) = S(0) = 0\tag{47}$$

### **Inverse resolution method for a system of differential equations**

The crystallization, and especially the nucleation stage, is by nature a statistical phenomenon with large discrepancies between the sets of experimental data. The analytical extraction of the relevant crystallization parameters must be then considered as a multicriteria optimization problem. As such the Genetic Algorithm Inverse Method is considered. The Genetic Algorithm Inverse Method is a stochastic optimization method inspired from the Darwin theory of nature survival (Paszkowicz, 2009). In the present work, the Genetic Algorithm developed by Carroll (Carroll, "FORTRAN Genetic Algorithm Front-End Driver Code"*,* site: *http://cuaerospace.com/ga*) is used (Smirnova et al., 2007; Haudin et al., 2008). The vector of solutions is represented by a parameter *Z*. In quiescent crystallization **(eqs. 20a-c)**, 00 01 0 1 0 1 *Z N N qqGG* [ , ,,, , ] with *N00*, *N01*, *q0*, *q1*, *G0*, *G1* the parameters of non-isothermal crystallization for a spherulitic morphology. In shear-induced crystallization, 00 01 0 1 02 3 0 1 0 1 1 *Z N N q q q q G G M wHA aB b* [ , , , , , , , , , , , ,, ,] with ( 02 3 1 *q qAa* ,, , ) the parameters of shear-induced crystallization for a spherulitic morphology **(eqs. 26,29)** and ( 0 1 *M* ,, , , *wHB b* ) the parameters of shear-induced crystallization for an oriented, like shish-kebab, morphology **(eqs. 41,43,45,47)**.

The optimization is applied to the experimental evolution of the overall kinetics coupled with one kinetic parameter at a lower scale, the number of entities (density of nucleation *Na(t)*). The system of differential equations is solved separately for each experimental set and gives the evolutions of *(t)* and of the nuclei density defining a corresponding data file. The optimization function *Qtotal* is expressed as the sum of the mean square errors of the transformed volume fraction *Qα* and of the number of entities *QNa* .

#### **Model-experiment-optimization confrontation**

The structure development parameters are identifiable by using the optical properties of the crystallizing entities. The experimental investigations and their analysis are done thanks to crossed-polarized optical microscopy (POM) (Magill, 1962, 1962, 2001) coupled with optically transparent hot stages, a home-made sliding plate shearing device and a rotating parallel plate shearing device (*e.g.*, Linkam). Data accessible directly are: *i)* the evolution of the transformed fraction *(t)*, and the number of activated nuclei *Na(t), ii)* the approximate values of the initial number of potential nuclei *N*0*(T),* activation frequency *q(T),* and growth rate *G(T)* for isothermal conditions and their functions of temperature for non-isothermal

*dR H*

*a dS dM wM R R*

*F*, *P, Q*, *R* and *S* are five auxiliary functions giving a first-order ordinary differential system.

0 *M*(0) *M*

The crystallization, and especially the nucleation stage, is by nature a statistical phenomenon with large discrepancies between the sets of experimental data. The analytical extraction of the relevant crystallization parameters must be then considered as a multicriteria optimization problem. As such the Genetic Algorithm Inverse Method is considered. The Genetic Algorithm Inverse Method is a stochastic optimization method inspired from the Darwin theory of nature survival (Paszkowicz, 2009). In the present work, the Genetic Algorithm developed by Carroll (Carroll, "FORTRAN Genetic Algorithm Front-End Driver Code"*,* site: *http://cuaerospace.com/ga*) is used (Smirnova et al., 2007; Haudin et al., 2008). The vector of solutions is represented by a parameter *Z*. In quiescent crystallization **(eqs. 20a-c)**, 00 01 0 1 0 1 *Z N N qqGG* [ , ,,, , ] with *N00*, *N01*, *q0*, *q1*, *G0*, *G1* the parameters of non-isothermal crystallization for a spherulitic morphology. In shear-induced crystallization, 00 01 0 1 02 3 0 1 0 1 1 *Z N N q q q q G G M wHA aB b* [ , , , , , , , , , , , ,, ,] with ( 02 3 1 *q qAa* ,, , ) the parameters of shear-induced crystallization for a spherulitic morphology **(eqs. 26,29)** and ( 0 1 *M* ,, , , *wHB b* ) the parameters of shear-induced crystallization for an oriented, like shish-kebab,

The optimization is applied to the experimental evolution of the overall kinetics coupled with one kinetic parameter at a lower scale, the number of entities (density of nucleation *Na(t)*). The system of differential equations is solved separately for each experimental set

The optimization function *Qtotal* is expressed as the sum of the mean square errors of the

The structure development parameters are identifiable by using the optical properties of the crystallizing entities. The experimental investigations and their analysis are done thanks to crossed-polarized optical microscopy (POM) (Magill, 1962, 1962, 2001) coupled with optically transparent hot stages, a home-made sliding plate shearing device and a rotating parallel plate shearing device (*e.g.*, Linkam). Data accessible directly are: *i)* the evolution of the transformed fraction *(t)*, and the number of activated nuclei *Na(t), ii)* the approximate values of the initial number of potential nuclei *N*0*(T),* activation frequency *q(T),* and growth rate *G(T)* for isothermal conditions and their functions of temperature for non-isothermal

*dt dt*

 (0) (0) (0) (0) (0) 0

**Inverse resolution method for a system of differential equations**

The initial conditions at time *t* = 0 are:

morphology **(eqs. 41,43,45,47)**.

and gives the evolutions of

**Model-experiment-optimization confrontation**

transformed volume fraction *Qα* and of the number of entities *QNa* .

1

*M M RS a a* (47)

*(t)* and of the nuclei density defining a corresponding data file.

*dt* (45)

(46)

conditions **(eqs. 20a-c)**. The exponential temperature evolution of the three key parameters *N*0, *q*, *G* is possibly calculated from the values of the physical parameters obtained in three different ways: firstly, an approximate physical analysis with direct determination from the experiments *(APA)*, secondly, the use of the Genetic Algorithm method for an optimization based on several experiments (at least 5) done with the same specimen, thirdly, an optimization based on several experiments (at least 8) involving different polymer samples for which an important dispersion of the number of nuclei is observed (Haudin et al., 2008, Boyer et al., 2009). These sets of optimized temperature functions made it possible to validate the mathematical model in the 2D version, as illustrated in **Fig. 5.a-b-inserts**. The selected polymer is a polypropylene that is considered as a 'model material' because of its aptitude to crystallize with well-defined spherulitic entities in quiescent conditions.

Shear-induced crystallization, with a spherulitic morphology, gives access to the function d *N* /dt ( *N* is the number of nuclei per unit volume generated by shear **(eq. 23)**) *versus* time and to the shear dependence of the activation frequency for different relatively low shear rates (up to 20 s-1). A set of seven optimized parameters are identifiable: *N*00, *q*0, *G*0 from quiescent isothermal crystallization, and ( 02 3 1 *q qAa* ,, , ) from isothermal shear-induced crystallization. The agreement between experiment and theory is better for higher shear rates associated with a shorter total time of crystallization. The mean square error does not exceed 12 %, the average mean square error for 5 s-1 is equal to 6.7 %. The agreement between experiment and theory is less satisfactory for the number of spherulites, the mean square error reaches 25 %. Then, the new model is able to predict the overall crystallization kinetics under low shear with enough accuracy, when the entities are spherulitic.

Shear-induced crystallization, with both a spherulitic and an oriented morphology, is a different task. High shear rates (from 75 s-1) enhance all the kinetics (nucleation, growth, overall kinetics) and lead to the formation of micron-size fibrillar (thread-like) structures immediately after shear, followed by the appearance of unoriented spherulitic structures at the later stages **(Fig. 6insert)**. The determination of the parameters for this double crystallization becomes a complicated task for a twofold reason: the quantitative data for both oriented and spherulitic structures are not available at high shear rate, and the double crystallization kinetics model requires to additionally determine the four parameters ( 1 *wHB b* ,,, ). So, optimization is based only on the evolution of the total transformed volume fraction **(eq. 21)**. Parameters characterizing quiescent crystallization ( 00 0 0 *N qG* , , ) and shearinduced crystallization with the spherulitic morphology ( 02 3 1 *q qAa* ,, , ) are taken from the previous 'smooth' analysis, so that four parameters ( 1 *wHB b* ,,, ) characterizing the oriented structure have to be optimized.

**Fig. 6.** gathers the experimental and theoretical variations of the total transformed volume fraction for different shear rates. At the beginning, the experimental overall kinetics is faster than the calculated one most probably because the influence of shear rate on the activation frequency of the oriented structure is not taken into account. Since with higher shear rate thinner samples (~30 µm at 150 s-1) are used, and since numerically the growth of entities is considered as three dimensional, the condition of 3D experiment seems not perfectly respected and the experiments give a slower evolution at the end. The mean square errors between numerical and experimental evolutions of the total transformed volume fraction do not exceed 19%.

Thermodynamics and Thermokinetics to Model Phase Transitions of Polymers

optimization of experimental and calculation procedures.

support in the development of «CRISTAPRESS» project.

10.1016/0022.5096(93)900 13-6

between polymer and metal transformations.

physico-chemical nature.

**5. Acknowledgments**

**6. References**

18.1141

respect.

over Extended Temperature and Pressure Ranges Under Various Hydrostatic Fluids 663

thermokinetics explicitly applied over extended temperature and pressure ranges, particularly under hydrostatic stress generated by pressure transmitting fluids of different

Clearly, such an approach rests not only on the conjunction of pertinent coupled experimental techniques and of robust theoretical models, but also on the consistency and

Illustration is made with selected examples like molten and solid polymers in interaction with various light molecular weight solvents, essentially gases. Data obtained allow evaluating specific thermal, chemical, mechanical behaviours coupled with sorption effect during solid to melt as well as crystallization transitions, creating smart and noble hybrid metal-polymer composites and re-visiting kinetic models taking into account similarities

This work generates a solid platform for polymer science, addressing formulation, processing, long-term utilization of end-products with specific performances controlled via a clear conception of greatly different size scales, altogether with an environmental aware

The principal author, Séverine A.E. Boyer, wishes to address her grateful acknowledgments for financial supports from Centre National de la Recherche Scientifique CNRS (France) ; Institut Français du Pétrole IFP (France) with Mrs. Marie-Hélène Klopffer and Mr. Joseph Martin ; Core Research for Evolutional Science and Technology - Japan Science and Technology Agency CREST-JST (Japan) with Prof. Tomokazu Iyoda (Tokyo Institute of Technology TIT, Japan) ; ARMINES-CARNOT-MINES ParisTech (France) ; Conseil Régional de Provence-Alpes-Côte d'Azur and Conseil Général des Alpes-Maritimes (France) for

Séverine A.E. Boyer wishes to expresses her acknowledgements to Intech for selectionning the current research that has been recognized as valuable and relevant to the given theme.

Ahzi, S.; Parks, D.M.; Argon, A.S. (1991). Modeling of deformation textures evolution in

Arruda, E.M.; Boyce, M.C. (1993). A three-dimensional constitutive model for the large

Asta, M.; Beckermann, C.; Karma, A.; Kurz, W.; Napolitano, R.; Plapp, M.; Purdy, G.;

semi-crystalline polymers. *Textures and Microstructures,* Vol.14-18, No1, (January 1991), pp. 1141-1146, ISSN 1687-5397(print) 1687-5400(web); doi: 10.1155/TSM.14-

stretch behaviour of rubber elastic materials. *Journal of the Mechanics and Physics of Solids,* Vol.41, No2, (February 1993), pp. 389-412, ISSN 0022-5096; doi:

Rappaz, M.; Trivedi, R. (2009). Solidification microstructures and solid-state parallels: Recent developments, future directions. *Acta Materialia*, Vol.57, No4, (February 2009), pp. 941-971; ISSN 1359-6454; doi: 10.1016/j.actamat.2008.10.020

Fig. 5. Experimental (symbols) and numerically predicted (lines) of **(a)** the overall kinetics and **(b)** the number of activated nuclei *vs.* temperature at constant cooling-rate. The inserts illustrate the events at 10, 3 and 1 °C.min-1. Sample: iPP in 2D (5 μm-thick layer).

Fig. 6. Experimental (dashed-line curves) and numerically predicted (solid curves) total overall kinetics, *i.e.*, spherulitic and oriented structures, *vs.* time in constant shear, *T* = 132 °C. The insert illustrates the event at 150 s–1. Sample: iPP in 2-3D (~30 μm-thick layer).

The present differential system, based on the nucleation and growth phenomena of polymer crystallization, is adopted to describe the crystalline morphology evolution *versus* thermomechanical constraints. It has been implemented into a 3D injection-moulding software. The implementation allows us to estimate its feasibility in complex forming conditions, *i.e.*, anisothermal flow-induced crystallization, and to test the sensitivity to the accuracy of the values of the parameters determined by the Genetic Algorithm Inverse Method.

### **4. Conclusion**

Fundamental understanding of the inherent links between multiscale polymer pattern and polymer behaviour/performance is firmly anchored on rigorous thermodynamics and thermokinetics explicitly applied over extended temperature and pressure ranges, particularly under hydrostatic stress generated by pressure transmitting fluids of different physico-chemical nature.

Clearly, such an approach rests not only on the conjunction of pertinent coupled experimental techniques and of robust theoretical models, but also on the consistency and optimization of experimental and calculation procedures.

Illustration is made with selected examples like molten and solid polymers in interaction with various light molecular weight solvents, essentially gases. Data obtained allow evaluating specific thermal, chemical, mechanical behaviours coupled with sorption effect during solid to melt as well as crystallization transitions, creating smart and noble hybrid metal-polymer composites and re-visiting kinetic models taking into account similarities between polymer and metal transformations.

This work generates a solid platform for polymer science, addressing formulation, processing, long-term utilization of end-products with specific performances controlled via a clear conception of greatly different size scales, altogether with an environmental aware respect.


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*3.2. Trends in Meteorological and Hydrological Drought Occurrences*

The results of the trend analysis for a series of SPI values (time scales of 1, 3, 6, 9 and 12 months) for individual meteorological stations are presented in Table 6. A statistically insignificant trend was noted at most meteorological stations. Out of eight meteorological stations, a statistically significant upward trend was recorded at only two stations, and a downward trend at one station (the drought intensified). The downward trend in the SPI value for the meteorological station in Kłodawa means that the meteorological drought increases in the period of accumulation of 3–12 months. The Z value obtained for the Kłodawa station was relatively high and ranged from −1.817 to −4.317, and the Theil– Sen slope was between −0.0007 and −0.0018. The test results for the Kłodawa station (Tables 6 and 7) show that the expected Z value is negative for the indices, and the Theil–Sen slope is also always negative.

**Table 6.** Results of trend analysis SPI in different time scales in the period of 1981–2016 at meteorological stations.

**Table 6.** *Cont.*

Description: N—no significant trend, D—significant decreasing trend, I—significant increasing trend.

**Table 7.** Results of trend analysis SRI in different time scales in the period of 1981–2016 at hydrological stations.

Description: N—no significant trend, D—significant decreasing trend, I—significant increasing trend.

The upward trend in the SPI value at the stations in Sompolno and Kołuda Wielka indicates that the phenomenon of meteorological drought is decreasing. The highest Z values, between 3.25 and 5.08, were obtained during the 3-, 6-, 9- and 12-month accumulation periods, while the Theil–Sen slope values were between 0.009 and 0.0019.

In the case of hydrological droughts, a statistically significant downward trend was recorded at the Łysek station in all analysed periods of accumulation. This means a noticeable increase in drought occurrences in the analysed multi-year period of 1981–2016. Z values were negative and ranged from −5.34 to −6.27, while Theil–Sen values were between −0.0013 to −0.0019. An upward trend was recorded at the No´c Kalina station. Z values ranged from 2.21 to 4.07, while the Theil–Sen slope ranged from 0.0008 to 0.0017. In the case of Pako´s´c station, the trend was statistically insignificant. The Z value calculated in the 12-month accumulation period in Pako´s´c approaches the region of a trend acceptance, which might indicate that the trend in Pako´s´c in the longer accumulation period is determined by the occurrence of hydrological droughts at the Łysek station.

Spatial distributions of trends (significant and insignificant) for a series of SPI and SRI values for the 1, 3-, 6-, 9- and 12-month time scales are shown in Figure 3.

#### *3.3. Correlations between SPI and SRI Values*

In order to establish the relationship between meteorological droughts occurring in the catchment area of the Upper Note´c and hydrological droughts, an analysis of the correlation between the SPI and SRI indices was carried out using the Pearson correlation analysis. The results showed that the strongest correlation between SPI and SRI in the analysed period of 1981–2016 was obtained at the 12-month time scale (r = 0.51) (Figure 5). In the case of individual years, the highest correlation indicators between hydrological and meteorological droughts varied depending on the length of the accumulation period. The strength of the relationship between SPI and SRI in the catchment area of the Upper Note´c River was higher for long accumulation periods (6 and 9 months), and lower for the short ones (1 and 3 months). The highest correlation values for the 1-month accumulation period were recorded in 1998 (r = 0.76), while for the 3-month accumulation period the highest values were recorded in 1982 (r = 0.83) and 1996 (r = 0.81). For the 6-month accumulation period, the maximum correlation index was recorded in 1987 (r = 0.94) and in 1982 (r = 0.91). High values were obtained in 1987 for the 9-month accumulation period (r = 0.94) and the 12-month accumulation period (r = 0.90).

#### *3.4. Discussion*

Understanding the changes in the intensity of droughts in the past and being able to predict expected changes over different time scales is incredibly important, as precipitationdriven hydrological processes (e.g., evapotranspiration and surface and groundwater discharge) affect all water reserves [86]. In this study, we found that in the analysed period of 1981–2016, there was a relationship between the occurrence of meteorological and hydrological droughts. The strength of this relationship varied. The analysed multi-year period of 1981–2016 showed a high variability, from dry years (SPI ≤ −1.0) to wet years (SPI ≥ 1.0), which can be concluded from the SPI values in various time scales. The driest years included 1982, 1989, 1992, 2003 and 2015. In these years, meteorological droughts covering not only the region of Poland, but parts of Europe, were recorded. Meteorological droughts which have occurred in Europe since the beginning of the 21st century, and were accompanied by heat waves in 2003, 2006, 2010, 2015, are great examples of such phenomena [87–91].

In the studied area, an increase in the intensity of meteorological droughts (downward trend) was observed at only one out of eight meteorological stations. A statistically significant, clear upward trend in SPI drought was identified at two stations. More distinct trends, but opposite in direction, were observed in the case of hydrological droughts recorded at the stations in Łysek and No´c Kalina. The obtained statistics for the Pako´s´c station, calculated in the 12-month accumulation period, point to the rejection of the null hypothesis on the lack of a statistically significant trend. The same direction of changes in the trend was recorded at the Łysek and Pako´s´c stations. This means an increase in the intensity of hydrological droughts. In the longer accumulation period, the occurrence of hydrological droughts at the Łysek station determines the hydrological droughts at the Pako´s´c station.

The strength of the relationship between meteorological droughts and hydrological droughts shows significant variation. This variation is not only the result of the size of the annual sums of precipitation, but also if an increase in air temperature in the analysed area (Figure 2), which leads to an increase in evapotranspiration [92]. Anthropogenic activities related to the operation of a lignite open pit have a significant impact on the analysed area. Some of the water from the mine drainage was directed to the Note´c River above the No´c Kalina station. The amount of water varied in individual years and depended on the location of the exploitation operations. According to Wachowiak [93], in the period of 1995–2009 the Upper Note´c was flooded with some of the mine water from the drainage of the Lubstów open pit. Since 2009, there have been cases of mine water discharge from the Tomisławice open pit via the Pichna River. The correlations between meteorological and hydrological droughts were variable, in some years the strength of the relationship was high (positive correlation), while in other years the relationship was low (negative correlation).

**Figure 5.** Correlation coefficient r between average SPI and SRI in Pako´s´c for different periods of accumulation in the period of 1981–2016 (*n*—number of accumulated months).

The Pearson correlation analysis shows that there is a relationship between meteorological and hydrological droughts in the study area. However, it should be emphasized that these results should not be directly interpreted. In the correlation analysis, a nonlinear relationship can be inadequately described or undetected [94]. Non-linear models (polynomial, exponential and logarithmic) for the relationship between meteorological and hydrological droughts were analysed in the research conducted by Salimi et al. [95].

The research on the correlation between droughts conducted by Tokarczyk and Szali ´nska [44] for catchments with large areas showed that the largest correlations between SPI and SRI occurred for longer periods of accumulation. Similar relationships between meteorological and hydrological droughts were obtained for other catchments in Poland [43]. In the case of the catchment area of the Upper Note´c River, the relationships between meteorological and hydrological conditions are not natural. The flow regime depends on the amount of water discharged in particular periods, and on the retention capacity of lakes, which is particularly noticeable at the Pako´s´c station. The amount of water accumulated in the Pako´s´c reservoir, through which the Note´c flows, is regulated by a water accumulating weir.

#### **4. Conclusions**

The study analysed the trends in meteorological and hydrological drought occurrences in the long-term period of 1981–2016, for the catchment area of the Upper Note´c River. The identification of a meteorological drought was carried out with the use of an SPI, based on monthly precipitation totals from eight meteorological stations. Hydrological drought was determined by means of an SRI for the monthly discharges of the Note´c, which were obtained from three hydrological stations. Non-parametric Mann–Kendall tests and the Sen slope were used to determine trends. The following conclusions might be drawn:

The example of the catchment area of the Upper Note´c River indicates that the management of water resources requires the use of at least several indicators that will allow an assessment of the actual state of water reserves. Using the SPI to detect meteorological droughts can be used as a drought warning system [96]. In some cases, the value of the SRI depends on the way water is managed within the catchment area. The size of the runoff may be disturbed by anthropogenic factors. Effective water resource management strategies require constant monitoring of water reserves, which should be consulted among various stakeholder groups related in particular to industry, and agriculture.

**Author Contributions:** Conceptualization, K.K.-W., A.P. and D.K.; methodology, K.K.-W. and A.P.; software, K.K.-W. and A.P.; validation, K.K.-W. and A.P.; formal analysis, K.K.-W., A.P. and D.K.; resources, K.K.-W. and A.P.; data curation, K.K.-W. and A.P.; writing—original draft preparation, K.K.-W.; writing—review and editing, K.K.-W., A.P. and D.K.; visualization, A.P.; supervision, K.K.-W., A.P. and D.K.; project administration, K.K.-W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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

#### **References**


**Author details**

Chloe R. Grabanski1 , Adedoyin Gloria Okanlawon1 , Raina V. Lamade1 \*, and Katherine J. Goulden2

1 University of Massachusetts Dartmouth, Dartmouth, MA, USA

2 Boston, MA, USA

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*Psychosocial Factors Associated with Happiness DOI: http://dx.doi.org/10.5772/intechopen.106952*


A ViiATM 7 Real-Time PCR System (Applied Biosystems, Burlington, ON, Canada) and PowerUpTM SYBRTM Green Master Mix (Applied Biosystems) were used for RT-qPCR. The primers (**Table 1**) were synthesized by Eurofins Genomics (Huntsville, USA). The primer efficiency was calculated as described before (23) with the formula: E = 10 (−1/slope). The PCR was performed in a total volume of 20 µl; 10 µl of PowerUpTM SYBRTM Green Master Mix, 1.6 µl of forward primer (5µM), 1.6 µl of reverse primer (5µM), 5 µl of 1/10 diluted cDNA and 1.8 µl of molecular-grade water with the final primer concentration of 400 nM for all the genes except 16S (ribosomal RNA gene) and rpoD (sigma factor). For these two genes, a final primer concentration of 200 nM was used with reaction mixture, 10 µl of SYBR R Select Master Mix, 0.8 µl of forward primer (5µM), 0.8 µl of reverse primer (5µM), 5 µl of 1/10 diluted cDNA and 3.4 µl of molecular-grade water. Each PCR was performed in triplicate in the 96 well plates (MicroAmpTM Optical 96-Well Reaction Plate with Barcode, Fisher Scientific, Canada). PCR conditions were as follows: UDG activation at 50◦C for 2 min and Dual-LockTM DNA polymerase activation at 95◦C for 2 min, followed by 40 repeated cycles of denaturation, annealing and amplification, at 95◦C for 15 s, 54◦C for 30 s and 72◦C for 45 s. Subsequently, a default dissociation curve (95◦C for 15 s, 60◦C for 1 min and 95◦C for 15 s) was performed in the instrument and specific amplicon was verified by a single melting-temperature peak. The transcript levels were normalized to the geometric average of expression for all housekeeping genes for each sample (24). The relative changes in gene expression were calculated by using the formula: dCT=CT (target)-CT (Normalizer), then ddCT is calculated by subtracting the dCT of the untreated sample from the treated one: ddCT= dCT (treated)- dCT (untreated). The relative gene expression is calculated as, 2−ddCT . Finally, a fold change was calculated as −1/2−ddCt (25).

### Genome Sequencing of *L. helveticus* LH-2 and *L. acidophilus* LA-5

Genomic DNA was extracted from L. helveticus LH-2 and L. acidophilus La-5 by using the UltraClean Microbial DNA Isolation Kit (Mo-Bio Laboratories, Inc., Canada) according to

the manufacturer's instructions. DNA was eluted in 10 mM Tris-HCl (pH 8.0). The concentration and the purity of the purified DNA was measured at 260 and 280 nm using a Nanodrop 1000 spectrophotometer. The integrity of the DNA was verified by agarose (1%) gel electrophoresis. Extracted DNA samples were stored at −20◦C. Library preparation and sequencing were performed at the Plateforme d'Analyses Génomiques of the Institut de Biologie Intégrative et des Systèmes (IBIS, Université Laval Quebec, Canada). In short, the libraries were prepared using 500 ng of mechanically fragmented DNA by a Covaris M220 (Covaris) using the NEBNext Ultra II kit (New England Biolabs). TruSeq HT adapters (Illumina) were ligated instead of NEBNext adaptors. The libraries were checked for quality using Bioanalyzer and quantified with Picogreen. The libraries were sequenced on a fraction of a MiSeq run (v3 600 cycles, Illumina) following the manufacturer's instructions. De novo assembly of the reads was performed using CLC Bio Genomic Workbench version 10.1 (Qiagen Inc., Mississauga, ON, Canada) at the Genomics Facility of the Advanced Analysis Centre, University of Guelph. Both the assembled reads and the de novo assembled contigs were BLAST searched (https://blast.ncbi.nlm.nih.gov/ Blast.cgi). Comparative genomic analysis of the proteolytic system was conducted using other related strains L. helveticus CNRZ 32 (abbreviation LAC LHE, accession number CP002081) for L. helveticus LH-2 and L. acidophilus NCFM (abbreviation LAC, accession number CP000033) for L. acidophilus La-5 using the NCBI microbial genome database and Blast alignment tools.

### Measurement of the Cell Envelope Proteinase (CEP) Activities

L. helveticus LH-2 and L. acidophilus La-5 were grown anaerobically in WPI—sugar based medium supplemented with 10 mM CaCl2.2H2O at 37◦C for 24 h. The cells were harvested by centrifugation at 15,000 × g for 10 min at 4◦C and washed twice with 10 mM CaCl2-saline solution. The supernatant designated as extracellular extract (EE) was used for the measurement of cell lysis rate while the cells were re-suspended in Tris buffer (50 mM, pH 7.8) to an optical density at 600 nm (OD600) of approximately 10 and were used for CEP and aminopeptidase enzyme assays.

The CEP activity assay of intact cells was performed using the chromogenic substrate N-succinyl-Ala-Ala-Pro-Phep-nitroanilide (Sigma, Markham, ON, Canada) as described previously (26). The reaction mixture was consisted of 200 µl resuspended cells, 287.5 µl phosphate buffer (0.2 M, pH 7.0), 225 µl 5 M NaCl and 37.5 µl N-succinyl-Ala-Ala-Pro-Phe-pnitroanilide (20 mM). The reaction components were mixed gently and incubated at different temperatures (35, 40, 45, 50, and 55◦C) for 30 min to determine the effect of temperature on the proteinase activity. The reactions were stopped by adding 175 µl of 80% (v/v) acetic acid followed by centrifugation at 13,000 × g for 5 min. The release of p-nitroanilide (pNA) was measured at 410 nm using Beckman DU 500-Spectrophotometer. The enzyme activity was calculated using a molar absorption coefficient (ε) of 8,800 mol−1 cm−1 . One unit of protease activity was defined as 1 nmol of p-nitroanilide released per minute. The specific protease activity was calculated as one unit of protease from 1 mg of cell protein. The protein content was estimated using the method of Bradford (27). Bovine serum albumin (Thermoscientific, Rockford, USA) was used as a standard to measure the protein content.

### Aminopeptidase Activities

Suspensions of harvested cells with OD600 of approximately 10 were used for preparation of intracellular extracts (IE) according to Pescuma et al. (28), cells were disrupted by using Microbead tubes (Mo-Bio Laboratories, Inc., Canada) in a microbead tube vortex for 6 min at maximum speed with 1 min interruption on ice after each minute. Beads, cell debris and unbroken cells were removed by centrifugation (10,000 × g at 4◦C for 5 min) and the supernatant fluid was designated as the intracellular enzymatic extract (IE) which was maintained on ice and immediately used for enzymatic assays. Intracellular aminopeptidase activity was assayed with the chromogenic substrate Lys-ρNa (Sigma) as previously described (29) by incubation of 100 µl of IE with 400 µl of Tris-HCl buffer (50 mM, pH 7.0) and 50 µl Lys-ρNa (10 mM) at five temperatures (35, 40, 45, 50, and 55◦C) for 20 min to determine the effect of temperature on the aminopeptidase activity. The reaction was stopped by addition of 1 mL of 30% acetic acid. The concentration of ρ-nitroanilide released was quantified at 410 nm. Enzyme activities were calculated by using a molar absorbance coefficient of 9,024 mol−1 cm−1 under the assay conditions. One unit of enzyme activity was defined as the amount of enzyme required to release 1 nmol of ρ-nitroanilide per min under the assay conditions. The specific peptidase activity was calculated as one unit of peptidase from 1 mg of cell protein. Aminopeptidase activity was measured in EE by the same procedures to detect rate of cell lysis.

### Identification of Peptides Through Liquid Chromatography–Mass Spectrometry Analysis (LC–MS)

Filtered CFSM was fractionated at three successive cut-off protein concentrator sizes 100, 50, and 3 kDa, respectively, using GE Healthcare VivaspinTM 20 protein concentrator (Fisher Scientific). The filtrate was freeze dried for 72 h. A control sample of filter sterilized WPI CFSM was included. The freeze-dried filtrate of 30 ml CFSM was reconstituted in 300 µl Milli-Q water and peptides were identified on an Agilent 1200 HPLC liquid chromatograph interfaced with an Agilent UHD 6530 Q-Tof mass spectrometer at the Mass Spectrometry Facility of the Advanced Analysis Centre, University of Guelph (30). Briefly, a C18 column (Agilent Advance Bio Peptide Map, 100 × 2.1 mm 2.7µm) was used for chromatographic separation with the following solvents, water with 0.1% formic acid for A and acetonitrile with 0.1% formic acid for B. The mobile phase gradient was 2% B increasing to 45% B in 40 min and then to 55% B in 10 more minutes followed by column wash at 95% B and 10-min re-equilibration with a flow rate 0.2 mL/min.

The mass spectrometer electrospray capillary voltage was maintained at 4.0 kV and the drying gas temperature at 350◦C with a flow rate of 13 L/min. Nebulizer pressure was 40 psi and the fragmentor was set to 150. Nitrogen was used as both nebulizing and drying gas, and collision-induced gas. The mass-to-charge ratio was scanned across the m/z range of 300–2,000 m/z in 4 GHz extended dynamic range positiveion auto MS/MS mode. Three precursor ions per cycle were selected for fragmentation. The sample injection volume was 20 µl. Raw data files were loaded directly into PEAKS 8 software (Bioinformatics Solutions Inc.) and subjected to de novo sequencing with SwissProt database searching. The tolerance values used were 15 ppm for parent ions and 0.05 Da for fragment ions. See **Supplementary Table S1** for information used to identify peptides.

### Statistical Analysis

All experiments were carried out three independent times with triplicates of each sample. Means and standard deviations were analyzed using ANOVA followed by Tukey's post hoc test with a P-value of ≤ 0.05 considered significant.

### RESULTS

### Activity of *L*. *helveticus* LH-2 and *L. acidophilus* La-5 CFSM on Bioluminescent Reporter Strains

Bioluminescent reporter strains S. Typhimurium hilA::lux and ssrB::lux were used for initial screening of the antivirulence effects of CFSM obtained from WPI fermented with L. helveticus LH-2 and L. acidophilus La-5. CFSM from both strains reduced luminescence of the S. Typhimurium hilA::lux while only La-5 could decrease ssrB::lux reporter luminescence; indicating down-regulation of the hilA and ssrB genes (**Figures 1A,B**). Both uninoculated control WPI had no downregulatory effect on S. Typhimurium hilA::lux light emission with a slight but significant increase in expression of the same gene in WPI glucose based media (**Figure 1A**). The uninoculated WPI sucrose based medium had no effect on S. Typhimurium ssrB::lux luminescence while WPI glucose based media showed significant down regulation of S. Typhimurium ssrB::lux luminescence (**Figure 1B**).

Bioluminescent reporter genes are considered a rapid tool for initial monitoring of gene expression. However, bioluminescence may be influenced by several factors, including other components of the medium. Previous observations using luminescent constructs suggest the occurrence of false positives and negatives with respect to virulence gene expression (12, 19, 31, 32). This is why an alternative method was chosen to complement the bioluminescence assay results. RT-qPCR is a highly sensitive and specific method for gene expression analysis.

### Effect of *L*. *helveticus* LH-2, *L. acidophilus* La-5 Bioactive Molecules and Mixture of Synthetic Peptides on Virulence Genes in *S*. Typhimurium DT 104 Wild Type and *S.* Typhimurium *oppA* Mutant Strains

The effects of the neutralized and reconstituted CFSM from L. helveticus LH-2 and L. acidophilus La-5 on virulence gene expression in multi-drug resistant S. Typhimurium DT104 and S. Typhimurium oppA mutant were analyzed by a 2-step RT qPCR method after incubation for 3 h (**Figure 2**). A statistically significant down-regulation of both the hilA and ssrB genes of S. Typhimurium DT 104 was observed after incubation with 10% CFSM from both LH-2 and La-5. La-5 CFSM showed stronger repression of S. Typhimurium DT104 hilA and ssrB

genes (−17.8 and −10.49, respectively) than LH-2 CFSM (−2.56 and −1.58, respectively). The expression of hilA and ssrB genes in S. Typhimurium oppA mutant strain was −2.23 and −1.61, respectively, in the presence of La-5 CFSM while in the presence of LH-2 CFSM, expression of both genes was −0.92 and −0.77, respectively (**Table 2**).

obtain the fold-change and compared with those of the non-fermented media.

The synthetic peptides mixture could also downregulate the expression of hilA and ssrB genes in S. Typhimurium DT104 (−14.66 and −11.99, respectively). The relative expression of the same genes in the S. Typhimurium oppA mutant strain in the presence of the same peptide mixture was −1.60 and −1.67, respectively (**Figure 2**).

### Genes Associated With Oligopeptide Metabolism in *L. helveticus* LH-2 and *L. acidophilus* LA-5

L. helveticus CNRZ 32 and L. acidophilus NCFM have previously sequenced complete genomes with the most thoroughly characterized proteolytic system (15, 33). Whole Genome Sequencing (WGS) and comparative analysis of the proteolytic components of L. helveticus LH-2 and L. acidophilus La-5 with L. helveticus CNRZ 32 and L. acidophilus NCFM, respectively, show some genetic differences in the distribution of proteolytic elements in LH-2 and La-5 (**Tables 3**, **4**). The L. helveticus LH-2 genome codes for prtH3, prtH4, and prtM2 CEP genes (99, 99, and 100% nucleotide identity, respectively) with the absence of prtH, prtH2 and prtM genes (**Table 3**). The L. acidophilus La-5 genome codes for prtP and prtM CEP genes (99, 100% identity, respectively) (**Table 4**).

Comparative genome analysis of L. helveticus LH-2 for oligopeptide transport elements and peptidases shows the presence of these proteolytic components with high identity (97–100%) (**Table 3**). The L. acidophilus La-5 genome codes for oligopeptide transport elements and peptidases of L. acidophilus NCFM with high identity (99–100%), except for oppB2 (33% identity) (**Table 4**).

### CEP Activities of *L*. *helveticus* LH-2 and *L. acidophilus* LA-5 in WPI

The CEP activities of intact cells from the medium fermented by L. helveticus LH-2 and L. acidophilus La-5 were measured based on the in vitro hydrolysis of the chromogenic substrate N-succinyl-Ala-Ala-Pro-Phe-p-nitroanilide. The optimal temperature for the proteinase activity of LH-2 strain was 45◦C with enzyme activity 3.60 while the maximum CEP activity of La-5 was 0.25 ± 0.01 at 40◦C (**Table 5**).

### Intracellular Aminopeptidase Activities of *L*. *helveticus* LH-2 and *L. acidophilus* LA-5 in WPI

For LH-2 intracellular extract, the highest aminopeptidase activity was 22.06 ± 0.60 at 40◦C while La-5 optimal activity of aminopeptidase was 5.19 ± 0.55 at 35◦C (**Table 5**). Negligible peptidase activity was detected in the extracellular supernatant using Lys-pNa, indicating that peptidase release from the cells during our experiments was below the detection limit (data not shown).

### Peptide Profiling of CSFM of Unfermented WPI and WPI Fermented With *L. helveticus* LH-2 and *L. acidophilus* LA-5 Strains

Liquid chromatography–mass spectrometry (LC-MS) analysis of unfermented WPI and WPI fermented with LH-2 and La-5 showed that peptides are almost all fragments of the main milk proteins (109, 75, and 15 milk protein derived peptides, respectively) (**Figure 3**). Out of these, 56 peptides were unique to the 3-kDa fraction from LH-2 fermented medium while 9 were unique to La-5 fermented medium. CEP cleavage sites are not evenly distributed throughout the protein sequences. Regions of highest proteolytic activity are demonstrated by heat maps of peptides (**Figure 4**). Thus, the presence of hot spots of cleavage sites are represented by changes in color scale, where

TABLE 3 | Comparative genome analysis of the proteolytic system of L. helveticus LH-2 with L. helveticus CNRZ 32 (abbreviation LAC LHE, accession number CP002081) at the nucleotide level using NCBI microbial genome database and Blast alignment tools.

TABLE 2 | Effect of L. helveticus LH-2 and

L. acidophilus La-5 CFSM on virulence gene expression

 of S.

Typhimurium

 DT104 and

S.

Typhimurium

oppA

mutant compared with the control CFSM.

Ali et al. Reducing Salmonella Virulence With Fermented Whey

TABLE 4 | Comparative genome analysis of the proteolytic system of L. acidophilus La-5 with L. acidophilus NCFM (abbreviation LAC, accession code CP000033) at the nucleotide level using NCBI microbial genome database and Blast alignment tools.

(Continued)

TABLE 4 | Continued

red represents more numerous peptides. The undigested WPI shows many plasmin and cathepsin cleavage sites (**Figures 5**– **8**). Endogenous milk peptides were mostly not found after fermentation with LH-2 and La-5 strains, except 14 peptides from β-casein and one peptide from αs2-casein (**Figure 3**). Most peptides found in the supernatant of LH-2 and La-5 fermented media display low-affinity residues for oligopeptide binding proteins (negatively charged amino acids, peptides with glycine, proline and glutamine at position 4, 5, or 6, peptides with proline at second position, VPP and IPP containing peptides and phosphorylated peptides) and higher resistance to peptidase hydrolysis (**Figures 5**–**9**).

Identification of bioactive peptide sequences was conducted through a search of the peptide literature databases (**Table 6**). Out of the 109 peptides in undigested WPI, 39 peptides have previously reported bioactivity (antihypertensive, antimicrobial, antioxidative, immunomodulatory, antithrombotic, opioid and antidiabetic activity). A total of 75 peptides were found in L. helveticus LH-2 CFSM, 19 of these peptides have previously been associated with several categories of bioactivity (ACEinhibitory, opioid, antimicrobial and antioxidant activities). For L. acidophilus La-5 CFSM, a total of 15 peptides were sequenced, 5 of these peptides have previously been reported with ACEinhibitory, antioxidant, antimicrobial and anti-caries activities. Some of these peptides have multifunctional properties that can modulate two or more physiological processes (**Table 6**).

### DISCUSSION

Several previous studies showed the ability of lactic acid bacteria to down regulate the expression of virulence genes of enteropathogenic bacteria (31, 86, 87). In this study, CFSM collected from WPI fermented by L. helveticus LH-2 and L. acidophilus La-5 reduced the expression of both the hilA and ssrB genes of the S. Typhimurium DT 104 wild strain. L. acidophilus La-5 has the most significant down-regulatory effect on the virulence genes, which indicates that the antivirulence effect is strain dependent and affected by the nature and components of CFSM. WPI fermented by La-5 contains 9 unique peptides not found in LH-2-fermented or unfermented medium.

Delcenserie et al. (32) reported that glucose could up or down-regulate the expression of virulence genes in E. coli

TABLE 5 | Specific proteinase and aminopeptidase activities of intact cells and intracellular enzymatic extract of L. helveticus LH-2 and L. acidophilus La-5 after growth anaerobically in WPI—sugar based media at 37◦C for 24 h.

Values are the means and SD of independent triplicates. non- detectable (nd) = SD < 0·01. Specific enzyme activity is expressed as nmoles ρNa released /mg protein/min.

O157:H7. In our study, glucose and sucrose may also affect virulence gene expression in S. Typhimurium. A peptidic fraction, isolated from milk fermented with L. helveticus, downregulated ssrB gene expression of S. Typhimurium in the absence of glucose in the growth medium (10). CFSM from MRS without glucose fermented with some bifidobacteria species showed down-regulation of genes hilA, ssrB2, and sopD (88). These studies suggest that down-regulation of virulence genes could be caused by other non-carbohydrate metabolites produced after fermentation.

Absence of the downregulatory effect of L. helveticus LH-2 and L. acidophilus La-5 CFSM on the Salmonella oppA mutant strain signifies the importance of peptide transport inside S. Typhimurium to inhibit virulence gene expression. Regulation of virulence gene expression requires sensing of a specific signal in the environment such as autoinducers for quorum sensing (QS) and these signals affect the growth, metabolism and virulence of bacteria (89). Exogenous leucine increases the transport of peptides by the Opp system and enhances OppA synthesis (90). Also, activity of Opp increases the intracellular amino acid pool, which in turn activates global regulators such as Lrp or CodY (91). Baek et al. (92) reported that Lrp responds to the nutritional environment and has a repressor function on key virulence regulator genes of S. enterica Serovar Typhimurium by strong interaction with hilA and other SPI-1 and SPI-2 genes, affecting their expression by binding directly to their promoter regions PhilA, PinvF and PssrA. From these studies and our data, it can be concluded that the presence of the S. Typhimurium oppA gene is a key factor in sensing oligopeptides, which can act as a specific signal for down regulation of virulence genes once transported into the cell.

The downregulatory effect of the synthetic peptide mixture demonstrates the antivirulence effect of specific peptides produced by L. helveticus LH-2 and L. acidophilus La-5 CFSM on the S. Typhimurium virulence genes. Also, the absence of this downregulatory effect for the S. Typhimurium oppA mutant strain shows the importance of internalization of such peptides inside S. Typhimurium to exhibit the antivirulence effect.

Presence of more than one CEP paralog is common among L. helveticus strains (93). Also, the presence of prtH3 and prtH4 with the absence of prtH in L. helveticus LH-2 is not unusual as prtH is not broadly distributed within L. helveticus and has no effect on growth rate in milk and cheese whey (94). prtH and prtH2 distribution is also strain dependent (95). The prtH3/prtH4 combination is common in L. helveticus strains (96). These studies agree with our findings for the presence of prtH3 and prtH4 genes and absence of prtH and prtH2 genes in the genome of the LH-2 strain. PrtM is required for activation of PrtH and PrtM2 plays a role in activation of other CEP paralogs in L. helveticus (97). Thus, absence of PrtM in LH-2 is expected as this gene was almost exclusively restricted to strains that also contained prtH (33).

Presence of both PrtP (proteinase precursor) and PrtM (maturase) in L. acidophilus La-5 confirmed that La-5 can digest large proteins extracellularly and produce small peptides. Genay et al. (95) reported that the genetic biodiversity of CEP paralogs between LAB strains could affect growth rate, strain functionality and bioactive peptide production in dairy products. This could cause differences in the antivirulence activities and bioactive peptides between of LH-2 and La-5 after growth in WPI.

Peptide transport systems can be specific for oligopeptides, or di- and tri-peptides (98). L. helveticus LH-2 contains all oligopeptide ABC transport elements, which is not different from previous studies of L. helveticus strains (99). L. acidophilus La-5 shows low identity of oppB2 which is encoded by the opp2-type operon (100). Azcarate-Peril et al. (101) reported that Opp1 and Opp2 might have different specificities, which could explain accumulation of some peptides in La-5 growth media. Doeven et al. (98) also concluded that the lack of some elements of the membrane complex, OppBCDF, leads to presence of some peptides outside the cells due to the specificity of the transport process.

The functionality of these enzymes after genome analysis of L. helveticus LH-2 and L. acidophilus La-5 strains was determined by measuring the proteolytic enzyme activities. The optimal temperature for the proteinase activity of LH-2 (45◦C) is not different from other previously reported L. helveticus strains (40– 50◦C) (102–104). The maximum CEP activity of La-5 (40 and 50◦C) also is not significantly different from other L. acidophilus strains (45–50◦C) previously reported (105).

L. helveticus LH2 CEP activity in WPI was lower than the CEP activity of other L. helveticus strains previously reported in skim milk (104, 106). The CEP activity of La-5 was slightly lower than the CEP activity of the same strain in skim milk (106) and significantly lower than the CEP activities of other L. acidophilus

FIGURE 3 | The Venn diagram shows the number and distribution of milk peptides in non-fermented WPI and WPI fermented with L. helveticus LH-2 or L. acidophilus La-5.

strains in chemically defined medium (107). The presence of peptides in the growth medium could inhibit the proteinase activity of some LAB (108). Mass spectrometry analysis of non-fermented WPI in this study confirmed the presence of endogenous milk protein derived peptides (β-, αs1-, αs2-, κcasein, and β-lactoglobulin) after ultrafiltration of milk during the WPI production process. The presence of these endogenous peptides in the growth media could explain the lower CEP activities of LH-2 and La-5 in WPI compared with other related strains in milk-based growth media. The CEP activity levels of lactobacilli depend on the strain, nature and quantity of peptides present in the growth media (96). The regulation of proteinase activity is also strain dependent (107). These factors suggest that CEP activities of LH-2 and La-5 are different in WPI than other milk-based media.

L. helveticus LH-2 aminopeptidase activity in WPI based medium was significantly lower compared with previously studied L. helveticus strains after growth in simplified chemically defined medium (SCDM) supplemented with different nitrogen sources (109). L. acidophilus La-5 had higher aminopeptidase activity compared with L. acidophilus strains mentioned in Pescuma et al. (28) when grown in a chemically defined medium (CDM). The aminopeptidase activities are not influenced by the peptide content of the medium (109), so this difference may be mainly strain dependent (110).

The presence of endogenous peptides in unfermented WPI indicates the activity of endogenous milk proteases which are mainly plasmin, elastase and cathepsin D, B, and G (111). Plasmin has little or no activity toward κ-casein and whey proteins (112), so the presence of endogenous peptides from these proteins in CFSM of L. acidophilus La-5 and L. helveticus LH-2 could be due to the action of other milk proteases such as cathepsin B, D, and G (34, 113). Dallas et al. (114) concluded that minimal proteolysis by native milk enzymes continued to function during incubation in the heat-treated milk when compared with that carried out by the proteases of kefir microorganisms which were mainly L. acidophilus and L. helveticus. This observation could explain the presence of some peptides shared between the unfermented and fermented WPI. Absence of some peptides after WPI fermentation is likely due to either further hydrolysis by LH-2 and La-5 extracellular proteases or their uptake by these microorganisms. The presence of four shared peptides (three from β-casein and one from β-lactoglobulin) in WPI fermented by LH-2 and La-5 strains indicates some similarity in the affinity of the proteinases of these strains for certain cleavage sites.

According to peptide analysis data, most of the identified peptides are casein derived, even though whey was used. The presence of casein derived peptides indicates that these proteins were exposed to proteolytic cleavage during processing (115). The three dimensional structure of caseins is more open and flexible than the globular, rigid, compact structures of whey proteins, which make casein proteins more susceptible to the effect of proteases (116).Whey proteins also are not an important peptide precursor during initial fermentation or endogenous proteolysis (117). This could explain why most peptides in the unfermented and fermented WPI media originate from casein proteins.

amino acids (Aspartate D, glutamate E) are in italics, Glycine G, Proline P, and Glutamine Q at position 4, 5, or 6 are marked with bold. P at second position is marked with bold and underline. VPP and IPP are inverse colors (white characters on black background). Phosphorylation sites are marked with a black dot above the serine. Cleavage sites of endogenous milk proteases are adapted from Baum et al. (34) and are marked with colored vertical arrows.

FIGURE 8 | Bovine κ-casein derived peptides identified in non-fermented control WPI and WPI fermented with L. helveticus LH-2 or L. acidophilus La-5. Negative charged amino acids (Aspartate D, glutamate E) are in italics, Glycine G, Proline P, and Glutamine Q at position 4, 5, or 6 are marked with bold. P at second position is marked with bold and underline. VPP and IPP are inverse colors (white characters on black background). Phosphorylation sites are marked with a black dot above the serine. Cleavage sites of endogenous milk proteases are adapted from Hurley et al. (35) and are marked with colored vertical arrows.

Negative charged amino acids (Aspartate D, glutamate E) are in italics, Glycine G, Proline P, and Glutamine Q at position 4, 5, or 6 are marked with bold. P at second position is marked with bold and underline. VPP and IPP are inverse colors (white characters on black background). Phosphorylation sites are marked with a black dot above the serine.

Although β-casein is not the most abundant protein in casein, our data showed that about half (47, 72, and 53%) of identified peptides in the WPI and WPI fermented with LH-2 and La-5, respectively, were derived from β-casein. Thus, it can be concluded that the endogenous and microbial proteases of LH-2 and La-5 may preferentially attack β-casein. These findings are consistent with previously studies on bovine casein and kefir (49). The degradation of αs-casein is strain dependent (107). Our results confirm this finding, as La-5 shows different cleavage sites for αs1-casein while αs1-casein derived peptides were not found after fermentation with LH-2. Nielsen et al. (67) reported no peptides from αs1-casein digested with L. helveticus 1198 while Sadat-Mekmene et al. (118) concluded that αs1-casein hydrolysis was enhanced in the presence of both CEPs (prtH, prtH2). The lack of the prtH CEP gene in the L. helveticus LH-2 genome does not explain the absence of αs1-casein derived peptides in LH-2 CFSM, as some of these are present in the control medium. Strain LHC2, which has a similar combination of CEP homologs (PrtH3/PrtH4), does produce peptides from αs1-casein within a 3-h time scale (119). The longer 48-h time scale of the

TABLE 6 | Bioactive peptides identified in Control WPI and WPI fermented with L. helveticus LH-2 and L. acidophilus La-5 strains by searching the Milk Bioactive Peptide Database with the reported function of the identified peptides.

Protein Peptide Function Control LH-2 La-5 References

(Continued)

TABLE 6 | Continued

fermentation of WPI by LH-2 may explain the disappearance of αs1-casein peptides, and the lack of larger casein proteins in the WPI medium would preclude the release of additional αs1-casein peptides by LH-2. In contrast, LH-2 can produce αs2-casein derived peptides while La-5 cannot produce them. Pescuma et al. (107) reported that L. acidophilus strain CRL 636 was unable to degrade αs2-casein, which supports the current findings. LH-2 shares some κ-casein cleavage sites with other L. helveticus strains (120) while κ-casein is poorly degraded by La-5 (105). βlactoglobulin is one of the major milk allergens (121). LH-2 and La-5 strains were able to degrade this protein, which indicates the potential of these strains in the production of hypoallergenic dairy products.

Fira et al. (105) reported that the optimal pH for CEP of L. acidophilus strains tested was 6.5 with a temperature optimum of 45–50◦C, which might explain the lower number or diversity of peptides produced by La-5 at a lower pH and temperature (pH 4.5–5.2 at 37◦C). The La-5 genome codes for only one CEP when compared with LH-2, which could result in a lower number or diversity of released peptides accumulating in the growth medium. Even though La-5 produces fewer types of peptides, those that are produced have higher antivirulence activity compare to those produced by LH-2, which depends on the nature and structure of the peptides. None of the peptides found in WPI fermented by La-5 are similar to those found in previous studies of the same strain grown in skim milk (52), which emphasizes the significant effect of growth media on the proteolytic activity of LAB.

Accumulation of peptides with predicted antivirulence effect after fermentation despite the active proteolytic and peptidolytic enzymes of L. helveticus LH-2 and L. acidophilus La-5 may be due to the nature and structure of these peptides. Detmers et al. (122) reported that peptides with hydrophobic and aromatic residues have high binding affinity to OppA, while those with proline, glycine, negatively charged amino acids (Aspartate D, glutamate E) and peptides with neutral or positively charged N-terminal residues having Gly, Pro and/or Gln in position 4, 5 and/or 6 significantly lower the affinity of the peptide for OppA protein. Proline at the second position of a nonameric peptide also resulted in a dramatic drop of the OppA binding affinity (122). Phosphorylated peptides and peptides high in proline content are more resistant to hydrolysis by proteolytic enzymes (123, 124). Oligopeptides containing VPP and IPP sequences could release branched chain amino acids (BCAAs) in the presence of specific peptidases. BCAAs could enhance binding of the Branched Chain Amino Acids Responsive Transcriptional Regulator (BCARR) to DNA sequences in the upstream region of the pepV gene, which would repress the expression of some peptidase genes in L. helveticus (125). Most LH-2 and La-5 peptides found in the supernatant display some of these characteristics, which may help explain their accumulation in the fermented WPI medium.

### CONCLUSIONS

L. helveticus LH-2 and L. acidophilus La-5 produce peptides with antivirulence effect against Salmonella enterica subsp. enterica serovar Typhimurium after growth in whey protein isolate medium. Accumulation of peptides with antivirulence activities may be related to the composition of these peptides and low affinity of these peptides to the oligopeptide-binding protein (OppA) of these strains, thus remaining in the spent medium if they are not transported into the cell. In Salmonella, the antivirulence activity of milk proteinderived peptides is related to the presence of the oppA gene. The undigested and fermented WPI by LH-2 and La-5 strains could be considered as a possible source of natural and functional ingredients, which may be used to increase the biological activity of food products. Further studies are required to explore the antivirulence ability of individual synthetic peptides with the same sequence at different concentrations compared to the antivirulence activity of fermented WPI.

### DATA AVAILABILITY STATEMENT

The datasets generated for this study can be found in the the University of Guelph Research Data Repository [https://doi.org/10.5683/SP2/43M6GX].

### AUTHOR CONTRIBUTIONS

EA carried out the experiments, interpreted the results, and wrote the first draft of the manuscript. SN participated in interpretation and presentation of the peptide results. SA-E, AE-L, and ES provided help for interpretation of the results. GL was involved in the design of the study, interpretation of the results, and writing of the manuscript.

### FUNDING

This work was supported and funded by Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chair (IRCSA 505386-15) held

### REFERENCES

by GL and the Cultural Affairs and Mission Sector in Egypt.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2019. 00152/full#supplementary-material

Supplementary Table S1 | Information used to identify peptides in unfermented control medium and media fermented by strain La-5 or LH-2.

Paneth cells along the small intestine. J Nutr Biochem. (2013) 24:213–21. doi: 10.1016/j.jnutbio.2012.05.004

in mice with bioluminescent Salmonella. J Food Prot. (2003) 66:2160–3. doi: 10.4315/0362-028X-66.11.2160

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

Copyright © 2019 Ali, Nielsen, Abd-El Aal, El-Leboudy, Saleh and LaPointe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


**5.16 Anti-oral mucositis effect**

Oral mucositis refers to the ulcerative and lesions of the oral mucosa found in people having cancer when treated with chemotherapy and radiation therapy of

*Pharmacological Investigation of Genus* Pistacia *DOI: http://dx.doi.org/10.5772/intechopen.97322*

areas, including the oral cavity. Oral mucositis lesions also are extremely painful and impair diet and oral health and raise the severity of the local and systemic infection. An experimental analysis conducted by Tanideh et al. [100] verified that the essential oil of *P. atlantica* (bene) accelerated the healing status of oral mucositis induced by 5-fluorouracil in hamsters. The healing influence of bene oil could predominantly be local and due to possessing antioxidants and fatty acids in saponified and non-saponified fractions, respectively [100].