**3. Bioactive compounds of** *T. hyemalis* **EO**

In a study conducted by [32], it was observed that *T. hyemalis* EO had a high heterogeneity, there being three different chemotypes: thymol, thymol/linalool, and carvacrol. The main components for the thymol chemotype were thymol (43%) followed by p-cymene (16%) and γ-terpinene (8.4%). For the thymol/linalool chemotype, the major compounds were linalool (16.6%), thymol (16%), γ-terpinene (9.8%), 1–8-cineol (5.4%), borneol (4.7%), and verbenone (4.8%). Finally, the carvacrol chemotype was characterized by a majority composition of carvacrol (40.1%), *p*-cymene (19.8%), borneol (5.0%), and thymol (2.9%).

The variability in the chemical composition of *T. hyemalis* EO may be related to seasonal variations [33, 34] as well as to the edapho-climatic factors [35].

One of the studies that supports the previous statement were carried out by Jordán et al. [36], where it was observed that, in the case of thymol chemotype, the

**119**

*Essential Oils of* Thymbra capitata *and* Thymus hyemalis *and Their Uses Based on Their…*

synthesis of this major compound occurred during the flowering/fruit ripening stage. The precursors of thymol, γ-terpinene, and *p*-cymene (**Figure 1**) were at their maximum concentration during the flowering stage. Therefore, between the stage of flowering and that of the beginning of fruit maturation, the composition of the EO of *T. hyemalis* reached its highest quality. This phenological stage coincides with winter, being recommendable to harvest the specimens in this season of the year. However, according to this study, it is also possible to obtain a high thymol content in the *T. hyemalis* EO during the spring season, but to achieve this, it is necessary to increase the irrigation, a condition that is not always achieved in arid and semiarid regions. In contrast to these results, Cabo et al. [33] proposed that August was the best time to harvest because the *T. hyemalis* EO contained a high concentration of 1,8-cineol in specimens collected during different phases of the vegetative cycle of *T. hyemalis*. These results seem to indicate the existence of a 1,8-cineol chemotype,

Finally, similar to the results of *T. capitata,* it should be noted that although the phenolic compounds, thymol and carvacrol, are mainly responsible for the bioactivity of the EO, the existence of synergistic or antagonistic effects between these phenolic components and other minor compounds (alcohols, other terpenoids, ketones, etc.) of *T. hyemalis* EO has been observed, which are essential for the qual-

It has been observed that the EO of *T. capitata* shows a potent antioxidant activity due to its high content of phenols (thymol or carvacrol) [37]. This statement is supported by Aazza et al. [38], when compared with the antioxidant activity of several thyme species. The results showed a higher antioxidant activity in EO rich in phenolic monoterpenes, like those of *Thymus caespititius* Brot. and *T. capitata*. This antioxidant capacity has been widely researched in order to prevent lipid oxidation during the storage of vegetable oils for culinary use, such as olive or sunflower. Likewise, Miguel et al. [26] showed that *T. capitata* EO, rich in carvacrol, avoided the lipid oxidation of sunflower oil and even turned out to be a more potent antioxidant than butylated hydroxytoluene (BHT), a synthetic antioxidant commonly used in the food industry. In addition, it has been seen that by isolating the carvacrol from the EO, this one by itself showed an antioxidant activity like EO, indicating an absence of synergistic or antagonistic effects due to the interaction between the different components of the EO. However, when the antioxidant activity of the EO of *T. capitata* was tested on the lipid oxidation of olive oil, it was observed that the EO was less potent than BHT [27, 39]. This low antioxidant capacity is also evident in the studies conducted by Saavedra et al. [40], in which it was observed that *T. capitata* EO did not help to avoid the oxidation of olive oil during storage and even increased the peroxidation levels, which indicated a greater

Another study conducted by Miguel et al. [9] on the lipid oxidation of peanut and sunflower oils showed a low antioxidant activity of *T. capitata* EO compared to two synthetic antioxidants, hydroxybutylanisole (BHA) and BHT, as well as a low effectiveness in elimination of free radicals compared to BHT, which contradicts the previous results found in sunflower oil. In addition, when comparing the EO of *T. capitata* with those of other species of the family Lamiaceae (*T. mastichina* and *T. camphoratus*), rich in *p*-cymene-2,3-diol, it was observed that the EO of *T. capitata*,

*DOI: http://dx.doi.org/10.5772/intechopen.89309*

with a very low concentration of thymol and carvacrol.

ity of this EO [32].

**4. Bioactivity of** *T. capitata* **EO**

**4.1 Antioxidant activity**

number of oxidation products.

*Essential Oils of* Thymbra capitata *and* Thymus hyemalis *and Their Uses Based on Their… DOI: http://dx.doi.org/10.5772/intechopen.89309*

synthesis of this major compound occurred during the flowering/fruit ripening stage. The precursors of thymol, γ-terpinene, and *p*-cymene (**Figure 1**) were at their maximum concentration during the flowering stage. Therefore, between the stage of flowering and that of the beginning of fruit maturation, the composition of the EO of *T. hyemalis* reached its highest quality. This phenological stage coincides with winter, being recommendable to harvest the specimens in this season of the year. However, according to this study, it is also possible to obtain a high thymol content in the *T. hyemalis* EO during the spring season, but to achieve this, it is necessary to increase the irrigation, a condition that is not always achieved in arid and semiarid regions. In contrast to these results, Cabo et al. [33] proposed that August was the best time to harvest because the *T. hyemalis* EO contained a high concentration of 1,8-cineol in specimens collected during different phases of the vegetative cycle of *T. hyemalis*. These results seem to indicate the existence of a 1,8-cineol chemotype, with a very low concentration of thymol and carvacrol.

Finally, similar to the results of *T. capitata,* it should be noted that although the phenolic compounds, thymol and carvacrol, are mainly responsible for the bioactivity of the EO, the existence of synergistic or antagonistic effects between these phenolic components and other minor compounds (alcohols, other terpenoids, ketones, etc.) of *T. hyemalis* EO has been observed, which are essential for the quality of this EO [32].

### **4. Bioactivity of** *T. capitata* **EO**

#### **4.1 Antioxidant activity**

*Thymus*

of three different chemotypes for *T. capitata*. In this sense, Miceli et al. [29] found 75 components and the majority being carvacrol and thymol, which, in all cases, constituted more than 50% of the composition of EO, followed by γ-terpinene, borneol, and *p*-cymene, when the chemical composition of the EO of *T. capitata* specimens were analyzed in flowering stage. The analysis of the compounds found in this EO revealed that there was a direct correlation between myrcene, α-terpinene, and γ-terpinene, whose concentrations decreased as the thymol concentration increased. An inverse relationship between linalool and myrcene was also observed. Thus, the analysis of the compounds presents in the EO of the specimens studied revealed that there were three distinct chemotypes: thymol, carvacrol, and thymol/ carvacrol, the most common being those of chemotype thymol. For the first two chemotypes, a negative correlation was observed between thymol and carvacrol, so when one of the components was majority, the other was at low concentration. The thymol /carvacrol chemotype resulted from the crossing between the specimens with the two previous chemotypes. In short, independently of the chemotype, it was observed that the content of monoterpenes reached 78% of the total of com-

In this sense, the experiments carried out by [10] confirmed the existence of these three chemotypes, which supports the hypothesis that *T. capitata* has a high polymorphism in the EO composition. To carry out these experiments, specimens grown in areas at different temperatures and degrees of humidity were used. As a result of this experiment, it was observed that those of carvacrol chemotype only appeared under conditions of high temperatures and low humidity. On the other hand, an experiment was carried out in which nine specimens were used, collected from three different areas, to later be cultivated under the same controlled climatic conditions. The results showed that the specimens maintained the chemotype that they originally presented, which is determined genetically, and did not change in the absence of climatic variations. These data suggest that the chemical composition of the EO is determined by the genetic endowment of the specimen and the different chemotypes are distributed according to the environmental conditions of the

Finally, in relation to other components found in smaller proportion (such as geraniol, camphor, or β-caryophyllene, among others), there is a high variability between populations and even within the same population [24, 31]. This variability can influence the bioactivity of *T. capitata* EO, which does not only depend on the majority component but also depends on the synergistic and antagonistic interactions that occur among all the phenolic and non-phenolic components [9, 25–27].

In a study conducted by [32], it was observed that *T. hyemalis* EO had a high heterogeneity, there being three different chemotypes: thymol, thymol/linalool, and carvacrol. The main components for the thymol chemotype were thymol (43%) followed by p-cymene (16%) and γ-terpinene (8.4%). For the thymol/linalool chemotype, the major compounds were linalool (16.6%), thymol (16%), γ-terpinene (9.8%), 1–8-cineol (5.4%), borneol (4.7%), and verbenone (4.8%). Finally, the carvacrol chemotype was characterized by a majority composition of carvacrol

The variability in the chemical composition of *T. hyemalis* EO may be related to

One of the studies that supports the previous statement were carried out by Jordán et al. [36], where it was observed that, in the case of thymol chemotype, the

(40.1%), *p*-cymene (19.8%), borneol (5.0%), and thymol (2.9%).

seasonal variations [33, 34] as well as to the edapho-climatic factors [35].

pounds present in the EO of *T. capitata* [29].

area in which they are cultivated [30].

**3. Bioactive compounds of** *T. hyemalis* **EO**

**118**

It has been observed that the EO of *T. capitata* shows a potent antioxidant activity due to its high content of phenols (thymol or carvacrol) [37]. This statement is supported by Aazza et al. [38], when compared with the antioxidant activity of several thyme species. The results showed a higher antioxidant activity in EO rich in phenolic monoterpenes, like those of *Thymus caespititius* Brot. and *T. capitata*.

This antioxidant capacity has been widely researched in order to prevent lipid oxidation during the storage of vegetable oils for culinary use, such as olive or sunflower. Likewise, Miguel et al. [26] showed that *T. capitata* EO, rich in carvacrol, avoided the lipid oxidation of sunflower oil and even turned out to be a more potent antioxidant than butylated hydroxytoluene (BHT), a synthetic antioxidant commonly used in the food industry. In addition, it has been seen that by isolating the carvacrol from the EO, this one by itself showed an antioxidant activity like EO, indicating an absence of synergistic or antagonistic effects due to the interaction between the different components of the EO. However, when the antioxidant activity of the EO of *T. capitata* was tested on the lipid oxidation of olive oil, it was observed that the EO was less potent than BHT [27, 39]. This low antioxidant capacity is also evident in the studies conducted by Saavedra et al. [40], in which it was observed that *T. capitata* EO did not help to avoid the oxidation of olive oil during storage and even increased the peroxidation levels, which indicated a greater number of oxidation products.

Another study conducted by Miguel et al. [9] on the lipid oxidation of peanut and sunflower oils showed a low antioxidant activity of *T. capitata* EO compared to two synthetic antioxidants, hydroxybutylanisole (BHA) and BHT, as well as a low effectiveness in elimination of free radicals compared to BHT, which contradicts the previous results found in sunflower oil. In addition, when comparing the EO of *T. capitata* with those of other species of the family Lamiaceae (*T. mastichina* and *T. camphoratus*), rich in *p*-cymene-2,3-diol, it was observed that the EO of *T. capitata*, rich in carvacrol, had a lower antioxidant activity than those of these two species. These results could be due to the differences found at the time of harvest, since, in this study, the *T. capitata* specimens were collected in the vegetative phase, while in the studies that demonstrated an important antioxidant activity for the EO of *T. capitata*, the specimens were collected during the flowering phase.

On the other hand, Galego et al. [41] carried out a study on the antioxidant capacity of EOs extracted from *T. capitata*, *Origanum vulgare* L., *T. mastichina*, and *Calamintha baetica* Boiss & Reut. For this, the antioxidant activity was determined using modified thiobarbituric acid (TBARS), which consists of the formation of a pink pigment produced by the reaction of thiobarbituric acid with malondialdehyde, a product of lipid peroxidation. Their results indicated that *T. capitata* and *O. vulgare* had the highest antioxidant activity, like BHT and BHA, but although they were effective in eliminating free radicals, at low concentrations, they did not become as effective as BHT and BHA.

In addition, it was observed that the antioxidant capacity of the EO of *T. capitata* was higher than that of the EO of *T. mastichina* and *C. baetica*, since the composition of the EO of these species was lower in phenolic compounds than the EO of *T. capitata*.

Faleiro et al. [15] also used the TBARS method to observe the effectiveness of the EO of *T. capitata* against the lipid oxidation of the egg yolk. Their results showed that, at high concentrations, EO could be as effective as synthetic antioxidants, BHA and BHT.

It should be noted that the antioxidant activity of *T. capitata* EO has not only been investigated in the food industry. In this sense, Hortigón-Vinagre et al. [42] studied the ability of this EO to prevent the cell death of cardiomyocytes in neonatal rats treated with 4-hydroxy-2-nonenal, a compound that induces lipid peroxidation in these cells. The results showed that at low concentrations (less than 40 ppm), the EO of *T. capitata* prevented the loss of membrane potential of the mitochondria and decreased the levels of reactive oxygen species (ROS), preventing the death of cardiomyocytes. However, concentrations higher than the mentioned one caused cell death, since they were toxic for the cells. In addition, this toxicity can be used as antiproliferative activity in in vitro experiments, since the EO extracted from fruits of *T. capitata* inhibited the growth of cells isolated from cervical cancer (HeLa). Likewise, the EO extracted from flowers and fruits of this species inhibited the growth of histiocytosis cells (U937) [30] and tumor cells responsible for acute monocytic leukemia (THP-1) [37].

#### **4.2 Antibacterial activity**

The antibacterial activity of the EO of *T. capitata* as well as its main component, carvacrol, was demonstrated against *Gardnerella vaginalis* by Machado et al. [43, 44]. The EO of *T. capitata* showed a potent activity against *G. vaginalis*, which was evidenced by the low minimum inhibitory concentration (MIC) (0.16 μL/mL) and the minimum lethal concentration (MLC) (0.16–0.31 μL/mL).

This antibacterial activity of *T. capitata* EO has also been observed against *Listeria monocytogenes*, the bacteria responsible for listeriosis, in a study conducted by Faleiro et al. [15].

In addition, Delgado-Adámez et al. [30] showed that the EO extracted from both flowers and fruits of *T. capitata* had a high efficacy against *Listeria innocua* (Gram+), at concentrations higher than 0.01% (v/v), and *Escherichia coli* (Gram−), at concentrations above 0.1% (v/v). Also, Karampoula et al. [45] showed the antibacterial effectiveness of the EO of *T. capitata* in hydrosol (a complex mixture of 24 components, which came from hydrodistillation of the plant, where the

**121**

complexity of this oil.

**4.4 Antiparasitic activity**

*Essential Oils of* Thymbra capitata *and* Thymus hyemalis *and Their Uses Based on Their…*

major compound was carvacrol). This antibacterial activity was studied against planktonic cells and biofilms of *Salmonella typhimurium*. The biofilms formed by the bacteria showed a slightly higher resistance to the planktonic cells, but in general, hydrosol was effective as antibacterial agent. In fact, when comparing this hydrosol with benzalkonium chloride, a commonly used synthetic antibacterial, it was observed that the hydrosol from *T. capitata* EO was much more effective as bactericide, since in order for benzalkonium chloride to show the same results as the hydrosol on planktonic cells and biofilms of *S. typhimurium*, a 200 times higher

According to Salgueiro et al. [46], the EO of 22 specimens of *T. capitata* carvacrol chemotype (60–66%), with high percentages of p-cymene (6–7.5%) and γ-terpinene (8.2–9.5%), was effective as a natural antifungal agent against *Candida* spp., *Aspergillus* spp., and some species of dermatophytes (*Trichophyton rubrum*, *T. mentagrophytes*, *Microsporum canis*, and *M. gypseum*), and its effect was mainly due to the generation of lesions on the membrane surface of the microorganism. These results agree with Palmeira-de-Oliveira et al. [47, 48], whose studies demonstrated that the EO of *T. capitata*, rich in carvacrol (75%), showed a great antifungal potential on biomass of *Candida* spp. and on preformed biofilms, since, at concentrations close to MIC (0.32 μL/mL), it caused the inhibition of its metabolism by up to 50%. In the case of biofilms, when the concentration of the EO doubled the MIC, a decrease of 80% of its metabolism was observed. Antifungal activity of the EO of *T. capitata* was also compared with the classic antifungal agents amphotericin B and

fluconazole, proving to be even more effective in some of the fungi studied.

interaction of this formula with the cell wall of *Candida* spp. [46].

Likewise, it has been observed that carvacrol or *p*-cymene isolated from the EO of *T. capitata* by themselves showed antifungal capacity. Therefore, this EO, or its isolated components, could be used for the treatment of mucocutaneous candidiasis and dermatophytosis. In this sense, the EO of *T. capitata* could be used alone or together with other antifungal components used so far [46, 47], as in the case of the association of the EO of *T capitata* with chitosan or chitosan in hydrogel, whose antifungal activity has been demonstrated in in vitro studies. The mechanism of action of this hydrogel has been studied by confocal microscopy, observing the

On the other hand, Russo et al. [23] observed that the EO of *T. capitata* carvacrol chemotype had an antifungal effect at a concentration of 250 ppm against *Sclerotium cepivorum*, a fungus responsible for white rot in garlic, onion, and leek crops. These authors suggested that the EO of *T. capitata* could be used as a natural antifungal, for its plant origin, not being harmful to the environment. In addition, it would be difficult to develop resistance in the fungus, due to the high chemical

Machado et al. [22] analyzed the antiparasitic activity of EOs rich in phenolic compounds of the species *T. capitata*, *O. virens* (Hoffmanns & Link), *T. zygis* subsp. *Sylvestris* (Hoffmanns & Link) Cout., and *Lippia graveolens* Kunth against *Giardia* spp. All EOs, including that of *T. capitata*, decreasing the viability of the parasite; altering its morphology, membrane permeability, and internal organization; and inhibiting its growth, as well as its adhesion capacity, which is essential for the parasite, to be able to bind to the intestine and not be eliminated by peristalsis. EOs blocked this adhesion from the first hours of exposure, not being more than 10% of

*DOI: http://dx.doi.org/10.5772/intechopen.89309*

concentration was needed.

**4.3 Antifungal activity**

*Essential Oils of* Thymbra capitata *and* Thymus hyemalis *and Their Uses Based on Their… DOI: http://dx.doi.org/10.5772/intechopen.89309*

major compound was carvacrol). This antibacterial activity was studied against planktonic cells and biofilms of *Salmonella typhimurium*. The biofilms formed by the bacteria showed a slightly higher resistance to the planktonic cells, but in general, hydrosol was effective as antibacterial agent. In fact, when comparing this hydrosol with benzalkonium chloride, a commonly used synthetic antibacterial, it was observed that the hydrosol from *T. capitata* EO was much more effective as bactericide, since in order for benzalkonium chloride to show the same results as the hydrosol on planktonic cells and biofilms of *S. typhimurium*, a 200 times higher concentration was needed.

#### **4.3 Antifungal activity**

*Thymus*

*T. capitata*.

BHA and BHT.

rich in carvacrol, had a lower antioxidant activity than those of these two species. These results could be due to the differences found at the time of harvest, since, in this study, the *T. capitata* specimens were collected in the vegetative phase, while in the studies that demonstrated an important antioxidant activity for the EO of

On the other hand, Galego et al. [41] carried out a study on the antioxidant capacity of EOs extracted from *T. capitata*, *Origanum vulgare* L., *T. mastichina*, and *Calamintha baetica* Boiss & Reut. For this, the antioxidant activity was determined using modified thiobarbituric acid (TBARS), which consists of the formation of a pink pigment produced by the reaction of thiobarbituric acid with malondialdehyde, a product of lipid peroxidation. Their results indicated that *T. capitata* and *O. vulgare* had the highest antioxidant activity, like BHT and BHA, but although they were effective in eliminating free radicals, at low concentrations, they did not

In addition, it was observed that the antioxidant capacity of the EO of *T. capitata* was higher than that of the EO of *T. mastichina* and *C. baetica*, since the composition of the EO of these species was lower in phenolic compounds than the EO of

Faleiro et al. [15] also used the TBARS method to observe the effectiveness of the EO of *T. capitata* against the lipid oxidation of the egg yolk. Their results showed that, at high concentrations, EO could be as effective as synthetic antioxidants,

It should be noted that the antioxidant activity of *T. capitata* EO has not only been investigated in the food industry. In this sense, Hortigón-Vinagre et al. [42] studied the ability of this EO to prevent the cell death of cardiomyocytes in neonatal rats treated with 4-hydroxy-2-nonenal, a compound that induces lipid peroxidation in these cells. The results showed that at low concentrations (less than 40 ppm), the EO of *T. capitata* prevented the loss of membrane potential of the mitochondria and decreased the levels of reactive oxygen species (ROS), preventing the death of cardiomyocytes. However, concentrations higher than the mentioned one caused cell death, since they were toxic for the cells. In addition, this toxicity can be used as antiproliferative activity in in vitro experiments, since the EO extracted from fruits of *T. capitata* inhibited the growth of cells isolated from cervical cancer (HeLa). Likewise, the EO extracted from flowers and fruits of this species inhibited the growth of histiocytosis cells (U937) [30] and tumor cells responsible for acute

The antibacterial activity of the EO of *T. capitata* as well as its main component, carvacrol, was demonstrated against *Gardnerella vaginalis* by Machado et al. [43, 44]. The EO of *T. capitata* showed a potent activity against *G. vaginalis*, which was evidenced by the low minimum inhibitory concentration (MIC) (0.16 μL/mL) and

This antibacterial activity of *T. capitata* EO has also been observed against *Listeria monocytogenes*, the bacteria responsible for listeriosis, in a study conducted

In addition, Delgado-Adámez et al. [30] showed that the EO extracted from both flowers and fruits of *T. capitata* had a high efficacy against *Listeria innocua* (Gram+), at concentrations higher than 0.01% (v/v), and *Escherichia coli* (Gram−), at concentrations above 0.1% (v/v). Also, Karampoula et al. [45] showed the antibacterial effectiveness of the EO of *T. capitata* in hydrosol (a complex mixture of 24 components, which came from hydrodistillation of the plant, where the

the minimum lethal concentration (MLC) (0.16–0.31 μL/mL).

*T. capitata*, the specimens were collected during the flowering phase.

become as effective as BHT and BHA.

monocytic leukemia (THP-1) [37].

**4.2 Antibacterial activity**

by Faleiro et al. [15].

**120**

According to Salgueiro et al. [46], the EO of 22 specimens of *T. capitata* carvacrol chemotype (60–66%), with high percentages of p-cymene (6–7.5%) and γ-terpinene (8.2–9.5%), was effective as a natural antifungal agent against *Candida* spp., *Aspergillus* spp., and some species of dermatophytes (*Trichophyton rubrum*, *T. mentagrophytes*, *Microsporum canis*, and *M. gypseum*), and its effect was mainly due to the generation of lesions on the membrane surface of the microorganism.

These results agree with Palmeira-de-Oliveira et al. [47, 48], whose studies demonstrated that the EO of *T. capitata*, rich in carvacrol (75%), showed a great antifungal potential on biomass of *Candida* spp. and on preformed biofilms, since, at concentrations close to MIC (0.32 μL/mL), it caused the inhibition of its metabolism by up to 50%.

In the case of biofilms, when the concentration of the EO doubled the MIC, a decrease of 80% of its metabolism was observed. Antifungal activity of the EO of *T. capitata* was also compared with the classic antifungal agents amphotericin B and fluconazole, proving to be even more effective in some of the fungi studied.

Likewise, it has been observed that carvacrol or *p*-cymene isolated from the EO of *T. capitata* by themselves showed antifungal capacity. Therefore, this EO, or its isolated components, could be used for the treatment of mucocutaneous candidiasis and dermatophytosis. In this sense, the EO of *T. capitata* could be used alone or together with other antifungal components used so far [46, 47], as in the case of the association of the EO of *T capitata* with chitosan or chitosan in hydrogel, whose antifungal activity has been demonstrated in in vitro studies. The mechanism of action of this hydrogel has been studied by confocal microscopy, observing the interaction of this formula with the cell wall of *Candida* spp. [46].

On the other hand, Russo et al. [23] observed that the EO of *T. capitata* carvacrol chemotype had an antifungal effect at a concentration of 250 ppm against *Sclerotium cepivorum*, a fungus responsible for white rot in garlic, onion, and leek crops. These authors suggested that the EO of *T. capitata* could be used as a natural antifungal, for its plant origin, not being harmful to the environment. In addition, it would be difficult to develop resistance in the fungus, due to the high chemical complexity of this oil.

#### **4.4 Antiparasitic activity**

Machado et al. [22] analyzed the antiparasitic activity of EOs rich in phenolic compounds of the species *T. capitata*, *O. virens* (Hoffmanns & Link), *T. zygis* subsp. *Sylvestris* (Hoffmanns & Link) Cout., and *Lippia graveolens* Kunth against *Giardia* spp. All EOs, including that of *T. capitata*, decreasing the viability of the parasite; altering its morphology, membrane permeability, and internal organization; and inhibiting its growth, as well as its adhesion capacity, which is essential for the parasite, to be able to bind to the intestine and not be eliminated by peristalsis. EOs blocked this adhesion from the first hours of exposure, not being more than 10% of cells able to adhere after 7 hours of treatment. In addition, by affecting membrane permeability, they caused swelling in the cells and alterations in the cytoplasm, which ultimately leads to cell death. Therefore, these EOs could be used as an alternative treatment for giardiasis, since they are not toxic to mammalian cells.


**6. Concluding Remarks**

Mammalian mitochondrial DNA codes for 13 proteins, all of which are subunits of the respiratory complexes and ATP synthase. Most of the organelle, however, is encoded in the nuclear genome, by what, in yeast, is estimated to be at least 900 genes or 15% of the genome. It stands to reason that a large number of human genetic disorders will stem from mutations that affect mitochondrial function. In the past, studies of yeast *pet* mutants have had a direct impact on understanding the genetic and mechanistic basis of human mitochondrial diseases. However, about 20% of the eukaryotic proteome has not yet been characterized, even in well-studied model organisms such as *S. cerevisiae* [302]. A substantial number of proteins still lacking an ascribed function are encoded by genes that affect mitochondrial respiration. In this context, yeast is still a powerful platform for discovering the function of uncharacterized mitochondrial proteins, as well as to gain a better understanding of the underlying molecular consequences of pathogenic mutations that could prove to be promising targets for mitochondrial disease therapies.

**Author Contributions:** Conceptualization: L.V.R.F.; writing: L.V.R.F., L.B., and M.H.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** L.V.R.F. was supported by FAPESP Post-Doctoral Fellowship 2019/02799-2.

**Acknowledgments:** The authors would like to express gratitude for the guidance of Alexander Tzagoloff in the preparation of this manuscript.

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


*Article* **Predicting the Future Distribution of** *Ara rubrogenys***, an Endemic Endangered Bird Species of the Andes, Taking into Account Trophic Interactions**

**Alain Hambuckers 1,\*, Simon de Harenne 1,2, Eberth Rocha Ledezma 3, Lilian Zúñiga Zeballos 1,4 and Louis François 2**

**Abstract:** Species distribution models (SDMs) are commonly used with climate only to predict animal distribution changes. This approach however neglects the evolution of other components of the niche, like food resource availability. SDMs are also commonly used with plants. This also suffers limitations, notably an inability to capture the fertilizing effect of the rising CO2 concentration strengthening resilience to water stress. Alternatively, process-based dynamic vegetation models (DVMs) respond to CO2 concentration. To test the impact of the plant modelling method to model plant resources of animals, we studied the distribution of a Bolivian macaw, assuming that, under future climate, DVMs produce more conservative results than SDMs. We modelled the bird with an SDM driven by climate. For the plant, we used SDMs or a DVM. Under future climates, the macaw SDM showed increased probabilities of presence over the area of distribution and connected range extensions. For plants, SDMs did not forecast overall response. By contrast, the DVM produced increases of productivity, occupancy and diversity, also towards higher altitudes. The results offered positive perspectives for the macaw, more optimistic with the DVM than with the SDMs, than initially assumed. Nevertheless, major common threats remain, challenging the short-term survival of the macaw.

**Keywords:** red-fronted macaw; Andes; dynamic vegetation model; biotic interactions; climate change; RCP2.6; RCP8.5

### **1. Introduction**

The problems of habitat destruction and climate change are the main threat to tropical mountain birds. Mountain bird species in the tropics are particularly at risk because they are isolated by hotter lowland zones which often makes them sedentary. In addition, when shifting their distribution up to higher altitudes, the new area of occupancy narrows [1]. The structure of the mountains itself also appears to be a constraining factor limiting the distribution shift with possible decline of habitat quality, for instance, the absence of suitable nesting sites or even vertical gaps between actual and potential future areas of distribution [2].

The slopes of the Andes are recognized as supporting the highest avian diversity in the world combined with high endemism rate but also more than 20% of threatened species [3]. In Bolivia, the red-fronted macaw (*Ara rubrogenys* Lafresnaye, 1847) is one of the 15 endemic species of this country [4]. Less than 30 years ago, *A. rubrogenys* was a little-known species [5]. It lives on the east Andean slopes of south-central Bolivia from

**Citation:** Hambuckers, A.; de Harenne, S.; Rocha Ledezma, E.; Zúñiga Zeballos, L.; François, L. Predicting the Future Distribution of *Ara rubrogenys*, an Endemic Endangered Bird Species of the Andes, Taking into Account Trophic Interactions. *Diversity* **2021**, *13*, 94. https://doi.org/10.3390/d13020094

Academic Editor: Miguel Ferrer

Received: 22 January 2021 Accepted: 18 February 2021 Published: 21 February 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

553 m up to 3094 m a.s.l. (Figure 1) and breeds between 1188 and 2696 m [6]. Its natural habitat is mainly semi-deciduous dry forest but this is most often severely degraded by pastoralism and by timber extraction into thorny scrubs with scattered trees [7]. While it was estimated that the threats were of limited extent in the early nineties [8], the status of the species worsens over the course of time with land conversion for agriculture, with poaching for illegal trade, with killing by farmers who consider them a pest and with poisoning with pesticides when they feed on the crops [6,9–11]. The small breeding population (only 67 to 136 pairs) in eight close areas was also pointed out as major risk which increases their extinction risk due to correlated environmental fluctuations [6]. It was found that the birds use agriculture-scrub ecotones more than the forests for foraging, probably because the forests do not offer enough resources. *A. rubrogenys* is now ranked as "Critically Endangered" in Bolivia [11] and on the International Union for Conservation of Nature Red list [12]. In addition, it could be particularly threatened by climate change. In its area of distribution, *A. rubrogenys* uses only terrains along river valleys for roosting, feeding, resting and nesting [9]. Most of the nesting sites are located in steep river cliffs but such environments are not necessarily available at higher altitudes given the magnitude of warming predictions. Climate change is supposed to particularly affect the tropical Andes and notably Bolivia [13]; while warming already averaged 0.1 ◦C/decade between 1939 and 1998, it accelerated to 0.33 ◦C/decade between 1980 and 2005. Climate change scenarios suggest warming as high as 7.5 ◦C by 2080 and important modifications of the precipitation regime with respect to pre-industrial times.

**Figure 1.** Distribution area (alpha hull) in Bolivia of *Ara rubrogenys* at altitudes with occurrences. 96% of the pixels over the area have an altitude between 1100 and 3000 m.

Species distribution models (SDMs) are based on the computation of an empirical relationship between the presence of a species (a sample of its distribution) and the actual values of the selected explaining factors. Range projections are obtained by computing a probability of presence over the study area using the relationship. The methodology has

been applied to parrots several times with different objectives. For instance, comparing the projection of models fitted to historical data with actual ranges allowed to study the conservation status of Andean *Pyrrhura* in Colombia [14] or of *Amazona* in Venezuela [15]. Thanks to climate driven SDMs, the substitution of the actual climate with future climate allows projection of decreasing and shifting ranges in *Amazona pretrei* [16]. Climate driven SDMs also permitted computing of habitat suitability of 13 parrot species in invaded countries and to test the consequences of two successive trade bans in the US and the EU on the invasion success [17]. In some situations, however, climate factors are not sufficient explaining variables, they only improve the fit of models describing the niches in combination with other factors reflecting species requirements like habitat characteristics. Authors reached this conclusion for modelling Bonelli's eagle nesting sites with topography, disturbance, land-use or climate variable at several geographic scales [18]. Bad results were obtained with climate variables only, but climate significantly improves the quality of the prediction offered by the other sets of variables. In light of their results, the authors suggest that snow and low winter temperature may cause physiological stress hampering breeding success; however, they underlined that with more complex models, the interpretation of the effect of each explaining climate factor could become hard to find. Another interesting approach to model bird distribution consists of including biotic interactions as well as abiotic factors. For instance, in [19] SDMs driven by climate were used to simulate several shrub species making up the habitat of a bird species and the outputs of these models were set up as input variables to model animal presence with climate and topography. The authors found that this approach outperforms climate only models, which stressed the importance of taking into account, as far as possible, the different types of niche components to produce consistent simulations. This SDM approach was applied to refine the mapping of the suitability area of *Amazona tucumana* in Argentina and Bolivia. Here, the niche was defined with climate, land-use and the output of another SDM forecasting the distribution of a key plant resource for nestlings, *Podocarpus parlatorei,* providing niche cavities and food [20].

However, while SDMs driven by climate variables are now considered as a standard method to predict plant species distribution under future climate, this approach fails to consider the effect of the increasing CO2 concentration in air on plant physiology. Indeed, it is well established that increased air CO2 concentration improves the capacity of plants to resist water stress because the plants can minimize transpiration while still satisfying their CO2 requirements [21]. Contrary to SDMs, dynamic vegetation models (DVMs) are commonly able to reproduce this effect. Although, questions remain about the acclimation (organism trait responses occurring in days to weeks) and adaptation processes of plants (response occurring through evolutionary processes) to new climates which could lower future changes [22]. Forecasting under future climates with these types of models, forcing, or not, the increasing air CO2 concentration, gives contrasting results and the projected distributions of plant species or habitats under future climate with increased CO2 concentrations appear better conserved than with SDMs (e.g., [23,24]).

The objectives of this study are to evaluate the potential impact of climate change on the distribution of *A. rubrogenys*. We compare the results produced by SDMs driven by climate variables for *A. rubrogenys* and for 17 resource plant species over the area of *A. rubrogenys* and the results produced by a DVM for the same plant species, under present conditions and future climates (2070–2100) under RCP2.6 and RCP8.5 forcing. We predict that for the future, approaches with SDMs should conserve less of the original distribution area of *A. rubrogenys* than obtained with DVM.

### **2. Materials and Methods**

### *2.1. Species Occurrences*

Censuses of *A. rubrogenys* were conducted between 2008 and 2010 by two of us (E. Rocha Ledezma and L. Zúñiga Zeballos). The birds were observed between 5 and 11 a.m., in the course of linear surveys and near known nesting, feeding and roosting sites, while

prospections were also realized in fields, forests and scrubs. Observations of *A. rubrogenys* in its area of distribution indicated that it mainly feeds on the fruits and seeds of the following wild species [25,26]: *Anadenanthera colubrina* (Vell.) Brenan, *Anisocapparis speciosa* (Griseb.) Cornejo and Iltis, *Aspidosperma quebracho-blanco* Schltdl., *Celtis ehrenbergiana* (Klotzsch) Liebm., *Cenchrus* (i.e., the species occurring in Bolivia in GBIF: *C. alopecuroides* J. Presl., *C. bambusiformis* (E. Fourn.) Morrone, *C. brevisetus* E. Fourn., *C. brownii* Roem. and Schult., *C. chilensis* (É. Desv.) Morrone, *C. ciliaris* L., *C. echinatus* L., *C. insularis* Scribn., *C. latifolius* (Spreng.) Morrone, *C. longisetus* M.C. Johnst, *C. myosuroides* Kunth, *C. nervosus* (Nees) Kuntze, *C. polystachios* (L.) Morrone, *C. purpureus* (Schumach.) Morrone, *C. setosus* Sw., *C. viridis* Spreng.), *Cnidoscolus tubulosus* I.M. Johnst., *Jatropha hieronymi* Kuntze, *Lithraea molleoides* Engl. (only occurrences in South America), *Loxopterygium grisebachii* Hiern ex Griseb., *Neoraimondia herzogiana* (Backeb.) Buxb. and Krainz, *Parasenegalia visco* (Lorentz ex Griseb.) Seigler and Ebinger, *Parkinsonia praecox* (Ruiz and Pav.) Hawkins, *Prosopis chilensis* Stuntz, *Prosopis kuntzei* Harms ex Kuntze, *Sarcomphalus mistol* (Griseb.) Hauenschild, *Schinopsis marginata* Engl. and *Selaginella sellowii* Hieron. We obtained the plant species coordinates of occurrences by querying the Global Diversity Information Facility site database (GBIF) in January 2017 and in August 2020, further checking for duplicates (Table 1). A second check for duplicates was conducted after combining with climate factors for coordinates belonging to the same pixels. This had limited consequences on plant sample size but not for *A. rubrogenys* sample size which dropped to 63. *A. rubrogenys*' area of distribution was defined with the alpha-hull polygon method [27] using the ashape function of the R package "alphahull" [28].

### *2.2. Climate Data*

For the current climate (monthly values for temperature, difference between maximum and minimum daily temperatures, precipitation, relative humidity, sunshine hours, wind speed), we used Worldclim version 2 for the time period between 1970 and 2000 at 2.5 arcminutes [29]. For the future climate, we used the CMIP5 projections of the HadGEM2-AO global circulation model [30] under the representative concentration pathways of greenhouse gases corresponding to end of 21st century radiative forcings of 2.6 and 8.5 W/m2 (RCP2.6 and RCP8.5, [31]). RCP2.6 would require a decline of greenhouse gas to reach no emission after 2072. With respect to the pre-industrial period, this scenario would keep global temperature rise below 2 ◦C in 2100. RCP8.5 is the worst hypothesis with emission of greenhouse gases continuing to increase throughout the 21st century producing global temperature increase between 2.6 and 4.8 ◦C for the period 2081–2100 [32].

### *2.3. Dynamic Vegetation Modelling*

The DVM CARAIB (CARbon Assimilation In the Biosphere) was mainly described in [23,33–36]. This model was initially conceived to simulate vegetation at global or continental scale and its response to climate change in the future or in the past [37–43]. The model was also applied to agricultural systems [44–46] or to tree species [24,47–49]. It is a grid point model composed of 5 main interacting modules (hydrology budget, photosynthesis and stomatal regulation, carbon allocation and growth, heterotrophic respiration and carbon dynamics in the soil, competition between ecosystem strata and biogeography) and it is also possible to activate natural fire and migration modules. Input data are spatial monthly climates (minimal and maximal temperature, precipitation, relative humidity, sunshine hours and wind speed), CO2 air concentration, soil texture and color, elevation and a set of information describing the morpho-physiological characteristics of the plant species (traits), like the specific leaf area, leaf and sapwood C:N, plant height, deciduousness nature, et cetera, and climatic thresholds extracted from the distribution samples. Since trait information for the species was lacking, it was replaced by the values of the plant functional type to which the species belongs (Table 4 of Electronic Supplementary Material of [49]). Threshold values controlling germination and mortality under stress conditions are extracted from prescribed percentiles in their actual climate distribution

extracted from the occurrence samples [36,40]. For computing the fitness statistics, we also ran the simulation for the present on the coordinates of the sets of pseudo-absences drawn for SDM modelling (see below). As output of the model, we used the net primary productivities of the species fractions (fNPP, gC/m2/y) and we selected thresholds of presence maximizing the true skill statistic (TSS, [50]). The threshold of presence allows us to compute the sensitivity (proportion of presences correctly predicted) and specificity (proportion of absences correctly predicted). We also computed the receiver operating characteristic curve (AUC). Positive TSS values and AUC larger than 0.7 indicate better agreement than random.

**Table 1.** Species occurrences and data sources (Global Diversity Information Facility site database: GBIF).

### *2.4. A. rubrogenys and Plant Species SDM*

We used multiple logistic regressions, also called logit models. Studies have showed that it is difficult to rank the SDM methods according to their performances because they vary with dataset properties [51–53], but logistic regression has the best theoretical background and it is used here as a reference methodology for SDM. The models were computed in R with the glm function of the R package "stat" (The R Core Team) and we interpreted the output as a probability of presence (Px). Since absence data are also needed, we generated pseudo-absences for each species (*A. rubrogenys* and plant species). The pseudo-absences were sets of points randomly drawn around the occurrences containing twice as many points as occurrences. Our strategy is based on the fact that for rare events, rather than randomly sampling, it is more efficient to collect positive cases (presences), as much as possible, and to complete them with a limited number of random cases as absences [54]. There is no common rule to select the ratio of absences to presences and this varied considerably in ecological studies (see [55]). For each plant species, we built 10 datasets containing the occurrence coordinates and pseudo-absence coordinates drawn in radius buffers comprised of between 200 and 2000 km around presences. Furthermore, drawing pseudo-absences within the presences is justified by the fact that the species do not occupy their entire range. For each dataset, we identified the best combination of climate factor effects, in other words, a maximum of 6 (only 4 for *N. herzogiana*, due to smaller sample size) linear or quadratic effects, after exhaustive screening based on the Akaike information criterion (AIC), using the glmulti function of the R package "glmulti" [56]. This procedure allowed us to select the shortest distance for the pseudo-absences providing evident AUC increase, which may give more accuracy and meaningful fit of the models [57]. We finally used the datasets giving the highest AUC and the model selected in the above procedure with the lowest AIC. Possibly, we chose a model with slightly higher AIC so that most of the model coefficients may have Z-test *p*-values lower than 0.05. For validation we tested the selected models against their null models using the likelihood ratio test (with critical *p*-value = 0.05) and the AUC (auc function of "SDMTools" [58]). The cutting thresholds were also selected thanks to TSS. A linear effect alone reveals a strictly positive or negative ecological response to the considered factor. The combination of a linear effect with the quadratic effect of the same factor produces a bell-shaped ecological response curve. Owing to the logit link function, a quadratic effect alone acts as a threshold. As climate variables, we first selected annual mean temperature, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the coldest month, annual precipitation, precipitation seasonality, precipitation of the wettest quarter and precipitation of the driest quarter. Then, to minimize collinearity, we computed the matrix of Pearson correlation coefficients in the species datasets and pointed the couple of variables with coefficients >0.7 [59]. Thus, we had to drop the maximum temperature of the warmest month, minimum temperature of the coldest month and annual precipitation for the plant species, and additionally, precipitation of the driest quarter for *A. rubrogenys*. For projections, we set missing value pixels with at least one of the explanatory factors outside the range encountered in the calibration datasets.

It should be noted that we tested the approach consisting of including outputs of the plant models to drive *A. rubrogenys* SDM using restricted estimation of the linear coefficients of the plant model output to impose positive coefficients. However, this approach failed mainly due to the fact that the productivity (DVM outputs) or the probability of presence (SDM outputs) were not uniformly higher over the *A. rubrogenys* area than outside. We understood this negative result as that animals are able to rely on resources made by plants growing in sub-optimal conditions, which seemed evident.

#### **3. Results**

Climate over the *A. rubrogenys* area varied a lot, exemplified by mean annual temperatures which fall between 12.31 and 23.00 ◦C and precipitation of the driest quarter which ranged between 6 and 62 mm (Table S1). Under RCP2.6 forcing, temperature and

precipitation over the *A. rubrogenys* area increased while seasonality decreased with nevertheless increases of the ranges between extreme higher and lower values. Under RCP8.5 forcing, temperature and precipitation followed the same direction but more intensively than under RCP2.6 forcing.

### *3.1. Dynamic Vegetation Modelling*

Fitness statistics (Table 2) suggested limited to acceptable agreements for plant species DVM modelling. It is normal that the mechanistic model had significantly lower AUC than those obtained with the SDM. Indeed, first there is no training step in the DVM computation and, second, the AUC of the SDM has been maximized in the generation of pseudo-absences by varying the distance to the points of presence. Thus, comparing the performances of the two models in terms of AUC, or any other evaluator using pseudo-absences, cannot be made without bias. In this respect, it should be noted that the performance of the DVM in terms of sensitivity (Se) (which do not use pseudo-absences) is generally quite high, since they are generally larger than 0.8 or even 0.9. Otherwise, owing to excessive computing time, it was not possible to compute maps over the entire areas of distribution of the plant species. All the species but *C. tubulosus* were simulated as occurring over the *A. rubrogenys* presence area (Figures 2 and 3 and Figure S1). The range of fNPP also varied considerably between the species and the climate conditions.

**Table 2.** Dynamic vegetation modelling (DVM) information: sample size for statistics (*N*), area under the receiver operating characteristic curve (AUC), true skill statistic (TSS), threshold for TSS (net primary productivity of the species fraction: fNPP, gC/m2/y), maximal net primary productivity of the species fraction over the area (Max fNPP, gC/m2/y), sensitivity (Se), specificity (Sp).

### *3.2. SDM Modelling*

It was possible to obtain models with acceptable to very good agreement for each of the plant species and for *A. rubrogenys*, with fitness indicators generally higher than those obtained for the DVM results (Table 3), except for the sensitivity (Se). For plants, projection maps revealed large areas of potential presence without any presence point which could be due to uneven sampling but also to natural barriers to migration, to competition or to speciation (Figures 4 and 5, Figures S2 and S3). Ten of the plant species were largely simulated as occurring over the *A. rubrogenys*' present area (Figure 6). The simulated distributions over Bolivia sometimes showed large similarities with those produced by the DVM. For *A. rubrogenys*, the model defined a close region over the alpha hull defining the present area (Figure 5). The original range was conserved under the future climates, the

Px increased (Figure 6), while new suitable areas appeared connected with the former one (Figure 5).

**Figure 2.** Examples of plant species distributions over Bolivia (*Ara rubrogenys* area delimited by orange lines) predicted by the DVM CARAIB (CARbon Assimilation In the Biosphere) for present and future climates under RCP2.6 andRCP8.5 forcings (+: occurrences of the species, light to dark color variation shows plant net productivity of the species fraction, in blue-gray between 0 and threshold of presence, in green between threshold and maximal value; the other plant species are shown in Figure S1).

**Figure 3.** *Cont*.

**Figure 3.** Area of *Ara rubrogenys* occupancy and fraction of the net primary productivity (fNPP) of the resource plant species predicted by the DVM CARAIB for present and future climates under the RCP2.6 andRCP8.5 forcings (*Cnidoscolus tubulosus* was not predicted on this area).

**Figure 4.** Examples of continental distribution of resource plant species predicted by the SDMs for the present (+: present occurrences of the species, light to dark color variation shows the probability of presence, in blue-grey between 0 and threshold of presence, in green between threshold and maximal value, pink color masks the area where at least one of the climate factors was out of the range of the model computation dataset, *Ara rubrogenys* area in orange; other plant species shown in Figure S2).

**Figure 5.** *Cont*.

**Figure 5.** Examples of distributions of plant species and distribution of *Ara rubrogenys* over Bolivia predicted by SDMs for present and future climates under 2.6 and RCP8.5 forcings (+: present occurrences of the species, light to dark color variation shows the probability of presence, in blue-grey between 0 and threshold of presence, in green between threshold and maximal value, pink color masks the area where at least one of the climate factors was out of the range of the model computation dataset, *Ara rubrogenys* area delimited by orange lines; other plant species shown in Figure S3).

#### *3.3. Comparisons of Plant Model Predictions for the Future*

Under future conditions, changes predicted by the DVM over Bolivia were limited but plant presences tended rather to spread (Figures 2 and 3 and Figure S1). The trend was more pronounced over *A. rubrogenys*' area, particularly under RCP8.5 forcings while the fNPP of the pixel presence over the *A. rubrogenys* area behaved more or less similarly but with some exceptions (*Cenchrus*, *P. visco*, Figure 3). The mean number of plant species per pixel (plant diversity, Figure S4) computed for present climate was 10.3. It increased to 12.0 under RCP2.6 and 14.2 under RCP8.5 forcings. Furthermore, 80% (RCP2.6) to 96% (RCP8.5) of the pixels lost no species while a majority of them gained up to 11 new species (Figure 7). In addition, the maximal altitude increased for the majority of the species under the future climates (Figure 8).

**Figure 6.** *Cont*.

**Figure 6.** Area of *Ara rubrogenys* occupancy and mean probability of presence (Px) of *A. rubrogenys* and of the 18 resource plant species predicted with SDM for present and future climates under the RCP2.6 andRCP8.5 forcings.

With the SDMs, changes predicted over Bolivia were more significant than with the DVM (Figures 5 and 6 and Figure S3) but generally the presence also tended to spread. Over *A. rubrogenys*' area, no clear trend emerged for occupancy or Px. Diversity over pixels (Figure S4) for the present was higher than with the DVM (mean: 13.2) but decreased to 12.6 under RCP2.6 and to 13 under RCP8.5 forcings. Indeed, more pixels lost species while the gains were more modest (Figure 7). Furthermore, less species than with the SDM had maximal altitude increase under the future climates (Figure 8).With the SDMs, changes predicted over Bolivia were more significant than with the DVM (Figures 5 and 6 and Figure S3) but generally the presence also tended to spread. Over *A. rubrogenys*' area, no clear trend emerged for occupancy or Px. Diversity over pixels (Figure S4) for the present was higher than with the DVM (mean: 13.2) but decreased to 12.6 under RCP2.6 and to 13 under RCP8.5 forcings. Indeed, more pixels lost species while the gains were more modest (Figure 7). Furthermore, less species than with the SDM had maximal altitude increase under the future climates (Figure 8).

**Figure 7.** Proportion of pixels exhibiting a given number (0, 1, 2, etc.) of species lost (**a**,**c**) or gained (**c**,**d**) over the area of *Ara rubrogenys* under the RCP2.6 andRCP8.5 forcings compared to the present, for studies with the dynamic vegetation model (DVM: **a**,**b**) and the species distribution model (SDM: **c**,**d**).

**DVM**

**Figure 8.** *Cont*.

**Figure 8.** Maximal altitudes of the species over the area of *Ara rubrogenys* for present and future climates under the RCP2.6 andRCP8.5 forcings predicted by the dynamic vegetation model (DVM) and the species distribution model (SDM).

### **4. Discussion**

The SDM allowed accurately modelling of *A. rubrogenys* presence with climate. Under future climate, occupancy, probability of presence and maximal altitude increased and new areas of occupancy appeared, connected with its present area. For the plants, DVM and SDM modelling allowed us to successfully compute the distribution of 17 plant species which are feeding resources of *A. rubrogenys.* Under present conditions, only one species, *C. tubulosus*, was not simulated as present over the area of *A. rubrogenys* by the DVM. SDMs gave better fits than the DVM, except for sensitivity (Se). The DVM mainly produced fNPP, and occupancy increases over the area of *A. rubrogenys*, also towards higher altitudes, under the RCP2.6 and RCP8.5 forcings. With DVM, we also obtained pixel diversity increases resulting from few species loss and more species gains. By contrast, the plant SDMs had no overall response under the future climate conditions for occupancy, probability of presence, altitude or diversity.

The lowest agreement of the DVM simulations compared to the plant SDMs were the result of lower specificity of the DVM. Here, we added an optimization step in which we selected a threshold for each species thanks to TSS. However, these thresholds were low compared to commonly fixed values. They probably reflected the fact that we used fNPP and thus the competition between species for water and light. Lowering the thresholds increases sensitivity at the expense of specificity. It was already noted in previous studies that the DVM may tend to simulate the fundamental niche of the species rather than the realized niche, in the absence of some critical biotic interactions. This could ultimately

**SDM**

produce wider distributions compared to those produced by the SDMs as observed elsewhere [49,60]. Nevertheless, the results of both approaches, DVM and SDMs, were rather congruent for present conditions. For the future, the results were mostly in accordance with literature, in other words, the SDMs generally predict more widespread distribution shifts than DVM [24,49,61–63] while this is not absolute. Therefore, it is not surprising that in limited parts of their present area, the Px of some species computed with SDM increased under the RCP forcings. In the Andes, SDMs predicted with dramatic reductions of the Andean vascular plant diversity for the future without excluding some small areas of better climatic stability where species numbers could increase [64]. Otherwise, the DVM results showing increases in diversity and the migrations towards higher altitudes seemed in accordance with the observations from the recent past. Permanent plots in the Andes showed thermophilization, in other words, the increasing number of tropical and subtropical tree species from lower altitudes showing shifts to higher altitudes [65]. This phenomenon, was also observed on European mountain summits. However, it should finally threaten the mountains species of the former communities because they are composed of slower growing species more adapted to harsher conditions compared to species from lower altitudes [66].

It is established that birds are directly sensitive to climate factors. While one of the main advantages of body temperature regulation in homeotherms is to allow an optimal functioning of the organisms under a wide temperature range, the limit of the mechanism is clearly the dissipation of heat in excess during exercise [67]. Thus, air temperature has to be considered as an effective component of the bird's niche. Homeotherms could also be directly sensitive to other climate factors than temperature, for instance, evaporation can limit the survival of birds in deserts during heat waves [68]. Furthermore, it was showed that birds have following their realized climate niche during the course of the last century [69]. For these reasons, the use of species distribution models (SDMs) with climate factors only to predict their future distribution has to be considered as a useful tool. The common response of birds in the Andes to climate change would be a decline [64,70]. Species with restricted distribution, like endemic species, are generally characterized by narrow climate niche and they seem to resist extinction by relatively high local abundance and good demographic resilience resulting from the accumulation of local adaptions [71]. The study of species, including parrots, naturalized outside their original area of distribution indicates however that the climate tolerance could be higher than estimated in the natural area, particularly for the species occupying narrow ranges of climate conditions or marginal climates in their native region [72–74]. With the climate factor, we obtained a very well-defined geographic range (Figure 5). We supposed that the climate niche of *A. rubrogenys* would be broad compared to the other endemic species on the basis of the range of the climate factors over its present area provoked by the steepness of the climate gradient in the mountains (Table S1). Therefore, it would not be surprising that the SDM is driven by climate predicted occupancy increase of the present area under the RCP forcings and also on areas directly connected, at higher altitudes. However, this kind of situation would be rare. Another possibility would be that the area of *A. rubrogenys* is climatically stable enough to guarantee no species loss or even species gain even if those situations are rare [64]. If so, it would also explain the limited changes found for the plant species under the RCP forcings with both modelling approaches. Thus, as long as the non-climate components of the niche of *A. rubrogenys* remain available, we could venture to forecast limited risk of extinction due to climate change provided that the diversity of the plant resources could increase and that species turnover could satisfy animal needs.

While the DVM approach is more promising than the SDM one for evaluating plant future, DVMs are nevertheless perfectible. DVMs need to elaborate an optimal strategy for parametrization [45,48,75]. It is indeed limited by the knowledge of the species-specific traits but also by the response of those traits to environmental factors and results could be improved by determining those responses and integrating them into the DVM [76–78]. The collection of plant traits with biogeographic information is a fast-growing field of

knowledge [79]. Since the DVM simulates competition for light and water resources, another interesting point could be to integrate the species of the former communities with the potential newcomers to compute the emerging communities. The newcomers, in addition, could also constitute potential resources.

### **5. Conclusions**

Under the milder RCP2.6 and the harsher RCP8.5 forcings, the SDM for *A. rubrogenys* and SDMs and the DVM for plant feeding resources of *A. rubrogenys* showed that the current area of suitability would be mostly preserved or even broadened. The actual feeding resources could be more precisely evaluated by taking into account the landuse and the productivity of the agricultural species which constitute important feeding resources for *A. rubrogenys* [6,25,26]. Nevertheless, predictions of the evolution of those resources in the course of time would require additional effort since uncertainties increase with the characteristics of the cultivated varieties and the decisions of the landowners or the farmers. The use of the DVM for predicting the future of plant species distribution and productivity is thus improvable. Our result boded well because *A. rubrogenys* would not be subjected to unprecedented climate conditions in the present area and the feeding resources so far would be preserved.

From topology, it could be possible to examine whether new suitable areas include cliffs appropriate for nesting. However, is it possible that the new suitable areas could be colonized even without appropriate cliffs thanks to behavioral flexibility? For instance, *A. rubrogenys* have been observed nesting in the palm *Parajubaea torallyi* [80] but, this tree species has an extremely small range distribution, and there are no other tree species that could offer large cavities for nesting within the range of the red-fronted macaw. Moreover, recent population genetic analyses show a low capacity for the species to colonize distant areas. Despite its restricted range, the population is structured in genetic clusters with low or null gene flow among them, despite that some colonies are separated by few tens of kilometers, that there are no ecological barriers and that macaws make larger daily and seasonal movements for feeding. Therefore, it is highly improbable that the species could colonize very distant areas in its future niche suitability [81].

Thus, while bird decline at global scale seems mainly provoked by increasing temperature [82], the main concerns about *A. rubrogenys*' future remained the direct human threats, in other words, habitat conversion, poaching and killing, which need to be urgently solved before the species rapidly goes extinct in nature [6,11,12].

**Supplementary Materials:** The following are available online at https://www.mdpi.com/1424-281 8/13/2/94/s1, Table S1: Bioclimate variables over *Ara rubrogenys* area and changes under the RCP2.6 and RCP8.5 forcings, Table S2: Coefficients of the climate logistic models for *Ara rubrogenys* and the 17 plant species, Figure S1: Plant species distribution over Bolivia predicted by the DVM CARAIB for present and future under RCP2.6 andRCP8.5 forcings, Figure S2: Plant species continental distribution predicted by the SDMs for the present, Figure S3: Plant species distribution over predicted by the SDM for present and future under RCP2.6 andRCP8.5 forcings, Figure S4: Number of plant species over the area of *Ara rubrogenys* computed with the DVM or the SDMs for present and future climate under RCP2.6 and RCP8.5 forcings.

**Author Contributions:** Conceptualization, methodology and investigation, A.H. and L.F.; investigations, E.R.L., S.d.H. and L.Z.Z.; data curation, L.Z.Z., writing—original draft preparation and editing, A.H.; review, L.F. 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.

**Data Availability Statement:** Information on data from publicly archived datasets used in this study is given in the Section 2.

**Acknowledgments:** We thank the reviewers for their comments and suggestions and their sharing of information on the red-fronted macaw which allowed us to improve our study.

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

### **References**


**4. Conclusions**

The Luo converter-based BLDC-driven PV pumping with ANFIS-FPA MPPT has been demonstrated under varying weather conditions using the dSPACE platform. The Luo converter has been proposed for desired GPP functions and is responsible for updating the duty ratio in each iteration using biotic/abiotic pollination. The PV-fed BLDC motor drive pumping system operates effectively under steady, dynamic states and soft-starting operating conditions, which have been validated through experimentally obtained responses. The enforcement of the ANFIS-FPA MPPT controller has been equated with the general P&O and ANFIS-PSO methods, which gives high tracking efficiency, fast design, and rapid convergence time under varying solar irradiance level. Performed experimental responses reveal that, compared to different bio-inspired, swarm-intelligence and classical MPPT techniques reviewed in literature, the ANFIS-FPA has superior power tracking ability, fast convergence velocity and accurate system response. The designed PV-based BLDC-driven pumping system provides the following functions viz. Luo converter-based MPPT tracking reducing switching losses based on electronic commutation/VSI switching, maintenance of the DC-link voltage, and BLDC motor speed regulation under low sun insolation, validated through practical responses using the dSPACE board.

**Author Contributions:** All authors contributed equally for the decimation of the research article in current form. **Conflicts of Interest:** The authors declare no conflict of interest.


**3.3 Polymer syntheses**

Based on the results of the model reactions, several polyimides (PIs) were prepared from **3** and commercially available aromatic dianhydrides via a one-pot solution imidization method, as shown in **Figure 7**. The polymerizations of diamine

**Figure 4.** *Model reaction of 3 with phthalic anhydride.*

*Synthesis and Properties of Fluorinated Polyimides from Rigid and Twisted… DOI: http://dx.doi.org/10.5772/intechopen.92010*

**Figure 5.** *(a) FTIR, (b) 1 H, and (c) 13C NMR spectra of model compound (4) (NMR: DMSO-*d*6, 25°C) [48].*

monomer **3** with stoichiometric amounts of five different aromatic dianhydride monomers, BPDA (**PI-1**), BTDA (**PI-2**), ODPA (**PI-3**), 6-FDA (**PI-4**), and PMDA (**PI-5**), were carried out in *m*-cresol with catalytic amounts of isoquinoline at a solid content of about 16 wt%. The ring-opening polyaddition at room temperature for

### **Figure 6.**

*(a) Top view and (b) side view of displacement ellipsoid (50%) representations of 4. All hydrogen atoms are omitted for clarity.*

**Figure 7.** *Polymerization of 3 with aromatic dianhydrides.*

4 h yielded poly(amic acid) solutions. After dilution of the solution to 8 wt%, subsequent cyclodehydration by heating at 190°C with the azeotropic distillation of chlorobenzene for 12 h gave fully imidized and homogeneous PI solutions except for that of PMDA (**PI-5**). When PMDA was used as the dianhydride, the solution became turbid with phase separation as soon as the temperature reached 190°C. This was likely due to the most rigid chain characteristic of **PI-5**. At the end of the reaction, pure solid polymers were obtained by precipitation of the corresponding polymer solutions into ethanol.

**Table 1** shows the inherent viscosities and GPC data of the PIs. The inherent viscosities of the organosoluble PIs were in the range of 0.69–2.30 dL/g, as measured in DMAc at 30°C. Additionally, the PIs soluble in THF exhibited weightaverage molecular weights (*M*ws) in the range of 7.32–8.81 104 relative to the polystyrene standard. The molecular weights of the PIs were high enough to obtain flexible and tough polymer films by casting from their DMAc solutions.

The formation and the structures of the polymers were verified by elemental analyses, FTIR, and 1 H NMR spectroscopy. The elemental analysis values of the PIs (listed in Experiments) were in good agreement with the calculated values of the proposed structures. The typical FTIR spectrum of **PI-1** is shown in **Figure 8**. All PIs exhibited characteristic imide group absorptions around 1780 and 1730 (typical of imide carbonyl asymmetrical and symmetrical stretching), 1370 (CdN stretching), and 730 cm1 (imide ring deformation), together with a number of strong absorption bands in the regions of 1120–1200 cm1 due to CdF stretching. The absence of amide and carboxyl bands indicates the virtually complete conversion of the poly (amic acid) precursors into PIs. The 1 H NMR spectra of the PIs are illustrated in **Figure 9**. All proton peaks were also assigned to the predicted structures without amide and acid protons, demonstrating the successful preparation of the PIs. Additionally, the proton peaks of the dianhydride units in the polymer chains were divided into different chemical shifts due to the unsymmetrical structure of the diamine unit in the PI main chains. The chemical shifts of protons closer to the CF3

*Synthesis and Properties of Fluorinated Polyimides from Rigid and Twisted… DOI: http://dx.doi.org/10.5772/intechopen.92010*

*a Measured in DMAc at a concentration of 0.5 g/dL at 30°C.*

*b Determined by GPC in THF at 35°C (relative to polystyrene standard).*

*c Insoluble.*

### **Table 1.**

*Inherent viscosities and elemental analyses results of the PIs.*

**Figure 8.** *FTIR spectrum of PI-1 (film).*

groups appeared further downfield in the NMR spectrum due to the deshielding effect of the electron-withdrawing CF3 groups. This result was consistent with the model reaction result.

### **3.4 Polymer properties**

The solubility of the synthesized PIs is summarized in **Table 2**. The synthesized polymers retained good solubility in organic solvents, although their rigidity increased compared to those synthesized from 4-(40 -aminophenoxy)-3,5-bis (trifluoromethyl)aniline [40]. All PIs except for **PI-5** exhibited good solubility in *N*-methyl-2-pyrrolidone (NMP), *N*,*N*-dimethylacetamide (DMAc), *m*-cresol, and anisole at room temperature. In addition, **PI-2**, **PI-3**, and **PI-4** showed good solubility in *N*,*N*-dimethylformamide, dimethyl sulfoxide, tetrahydrofuran, and ethyl acetate at room temperature. The good solubility of the PIs is attributed not only to the bulkiness of the two CF3 groups on the polymer chains but also to the unsymmetrical structure resulting from the diamine monomer. Given the increased chain flexibility, **PI-3** and **PI-4** were also soluble in chloroform and **PI-4** was soluble even

### **Figure 9.**

*1 H NMR spectrum of PI-1 (DMSO-*d*6, 100°C); 1 H NMR spectra of PI-2, PI-3, and PI-4 (DMSO-*d*6, 25°C).*

*Solubility: ++, soluble at room temperature; +, soluble on heating; +*�*, partially soluble;* �*, insoluble. Abbreviations: NMP,* N*-methyl-2-pyrrolidone; DMAc,* N*,*N*-dimethylacetamide; DMF,* N*,*N*-dimethylformamide; DMSO, dimethyl sulfoxide; THF, tetrahydrofuran; ODCB, 1,2-dichlorobenzene.*

### **Table 2.** *Solubility of the PIs.*

in acetone at room temperature. Upon a comparison of the solubility behavior with PIs prepared from symmetrical benzidine, 2,20 -bis(trifluoromethyl)benzidine [17–21], the PIs described here showed enhanced solubility. The improved solubility can be attributed to the unsymmetrical and more twisted chain structure, which inhibits close packing and reduces intermolecular interactions. Meanwhile, **PI-5** showed poor solubility in the organic solvents tested, although it was partially soluble in NMP and DMAc.

*Synthesis and Properties of Fluorinated Polyimides from Rigid and Twisted… DOI: http://dx.doi.org/10.5772/intechopen.92010*

To clarify the cause of the poor solubility of **PI-5**, wide-angle X-ray diffraction (WAXD) studies were performed because a crystalline domain can influence the solubility of the polymer [31]. The X-ray diffractograms of **PI-1** and **PI-3** showed broad diffraction curves without obvious peak features, indicating that the PIs have an amorphous morphology in principle (**Figure 10**). On the other hand, the X-ray diffraction curve of **PI-5** exhibited a relatively unambiguous peak around 17°, which indicated that **PI-5** has a more ordered phase compared to the other soluble PIs. This is likely related to the highly linear and rigid chain structure of **PI-5**, which induced high intermolecular interactions, resulting in a decrease of the solubility.

The soluble PIs of **PI-1**, **PI-2**, **PI-3**, and **PI-4** could be processed into flexible and tough films conveniently by casting from the DMAc polymer solutions. While **PI-2**, **PI-3**, and **PI-4** produced transparent films, **PI-1** generated a turbid film which appeared as the solvent was evaporated. **Table 3** shows the mechanical properties of the PI films. The PI films had tensile strength levels, elongation at break values, and a Young's modulus in the ranges of 92–145 MPa, 26–55%, and 2.1–3.2 GPa, respectively. These values were comparable to the mechanical strength of the PIs prepared from 2,20 -bis(trifluoromethyl)benzidine [22] and also indicated that the PI films are strong enough for use.

The thermal properties of the PIs were evaluated by TGA, DSC, and TMA, and these results are summarized in **Table 4**. The dynamic TGA result showed high thermal stability in which 5% weight loss occurred for the PIs in the range of

**Figure 10.** *Wide-angle X-ray diffractograms of PI-1 (film), PI-3 (film), and PI-5 (powder).*

**Table 3.** *Mechanical properties of the PI films.*

*a 5% weight loss temperature measured by TGA at a heating rate of 10°C/min.*

*b Measured by DSC (the second scan) in N2 at a heating rate 10°C/min. c*

*A TMA analysis was conducted three times for each sample at a heating rate of 5°C/min in a heating range up to 300° C. Each CTE value was calculated from the mean coefficient of the linear thermal expansion over a specific*

*temperature range between 50 and 250°C in the second and third runs, respectively. d Measured by TMA at a heating rate of 5°C/min.*

*e Not detected.*

*f Not measured.*

### **Table 4.**

*Thermal and optical properties of the PIs.*

535–605°C in nitrogen and 523–594°C in air (**Figure 11a** and **b**, respectively). DSC experiments were conducted at a heating rate of 10°C/min in nitrogen (**Figure 11c**). A survey of all of the PIs by DSC revealed that no endothermic peaks associated with melting were observed up to the temperature region investigated here. Moreover, while the glass-transition temperatures (*T*g) of **PI-2**, **PI-3**, and **PI-4** were clearly detected, **PI-1** and **PI-5** did not show any discernible glass transition on the DSC thermograms. Therefore, the *T*g of **PI-1** was measured by the TMA method after preparation of the polymer film (**Figure 11d**). All PIs exhibited high *T*g values above 340°C which depended on the chemical structure of the aromatic dianhydride component. **PI-1** obtained from BPDA showed the highest *T*g value (366°C) among the soluble PIs owing to the absence of a flexible linkage between the phthalimide units. Compared with PIs derived from 4-(40 -aminophenoxy)-3,5 bis(trifluoromethyl)aniline [40], the PIs based on 2,6-bis(trifluoromethyl)benzidine possess higher *T*g values due to their greater chain rigidity. Even when compared to the PIs derived from 2,20 -bis(trifluoromethyl)benzidine [18–22, 25], the PIs synthesized here showed similar or higher *T*g values. For example, the PIs based on BPDA and 6-FDA with 2,20 -bis(trifluoromethyl)benzidine exhibited *T*g values of 287–373°C and 335°C, respectively. This result could be attributed to the unsymmetrical introduction of the two CF3 groups, which further increases the rotational barrier of the polymer chains compared to the symmetrically introduced case.

The coefficients of thermal expansion (CTEs) of the PI films were found in the range of 6.8–63.1 ppm/°C. In general, polymers consisting of rod-like backbone structures together with a high chain alignment toward the direction parallel to the film plane have shown relatively low CTE values [24–28]. The relationship between the chain rigidity/degree of in-plane orientation and the CTE value can be applied to this study. The CTE value of the PIs decreased from 63.1 to 6.8 ppm/°C with an increase in the chain rigidity and the degree of in-plane orientation, as identified through the birefringence value (**Table 5**). Although the birefringence value of **PI-1** could not be measured due to the low transparency in this case, it can be speculated that **PI-1** has the highest degree of in-plane orientation because **PI-1** possesses the most rigid chain structure among the soluble PIs [18, 19, 24, 25, 28–30]. Therefore, **PI-1** gave the lowest CTE value (6.8 ppm/°C), displaying good dimensional stability.

*Synthesis and Properties of Fluorinated Polyimides from Rigid and Twisted… DOI: http://dx.doi.org/10.5772/intechopen.92010*

### **Figure 11.**

*TGA curves of the PIs in (a) nitrogen and (b) air at a heating rate of 10°C/min, (c) DSC curves of the PIs (the second heating run ranging from 0 to 400°C at a heating rate of 10°C/min in N2) and (d) TMA CURVE Of PI-1 (measured at a heating rate of 5°C/min).*

*a Measured at a wavelength of 633 nm at room temperature.*

*b* n*TE: the in-plane refractive index. c*

n*TM: the out-of plane refractive index. d*

n*av: the average refractive index (*n*av = (2*n*TE +* n*TM)/3). e*

*Δ*n*: birefringence (*n*TE* n*TM).*

*f Dielectric constant estimated from the refractive index: ε 1.10*n*av2 . g*

*Film thickness for the refractive index measured.*

### **Table 5.**

*Refractive indices of the PIs.*

The corresponding UV-vis spectra of the PI films with a thickness of about 60–80 μm are shown in **Figure 12a**. While the light transparencies of **PI-2**, **PI-3**, and **PI-4** were as high as 87–90% at a wavelength of 550 nm, **PI-1** film exhibited extremely low transparency of 34%. This outcome is attributable to the high degree of in-plane orientation caused by the most rigid chain structure compared to other PIs. The cutoff wavelengths (λo) of the PI films listed in **Table 4** range from 354 to 398 nm, indicating that they are light-colored films. The color of the PI films decreased in the following order: **PI-1** > **PI-2** > **PI-3** > **PI-4**. As shown in **Figure 12b**, **PI-3** and **PI-4** produced fairly transparent and almost colorless PI films compared to the other PIs. The good optical properties of **PI-3** and **PI-4** were attributed to the poor CTC formation ability caused by the ether chain of ODPA and the bulky hexafluoroisopropylidene group of

**Figure 12.** *(a) Transmittance UV-vis spectra and (b) photographs of the PI films.*

6-FDA, respectively. The increased yellowness of **PI-2** resulted from the presence of the carbonyl group, attracting electrons in the dianhydride units.

The refractive indices and birefringence values of the **PI-2**, **PI-3**, and **PI-4** were measured using a prism-coupling method with a laser beam having a wavelength of 633 nm. The measurement of the **PI-1** film could not be performed due to the low transparency of the film. As shown in **Table 5**, the PIs showed low refractive indices (*n*av) in the range of 1.534–1.614 due to the incorporation effect of the two CF3 groups, which led to low molecular polarizability and density levels. The birefringence (Δ*n*) values of the PIs were determined as the difference between *n*TE and *n*TM and were in the range of 0.042–0.072. The birefringence values among the PIs tested increased with an increase of the chain rigidity because the high chain rigidity led to high chain alignment in the direction parallel to the film plane [28–30]. **PI-4** showed the lowest birefringence value, indicating that the linear polarizability and segmental orientation of **PI-4** are the most isotropic among the PIs. The dielectric constant (ε) can be estimated from the refractive index *n* according to Maxwell's equation, ε ≈ *n*2 . The ε value at 1 MHz was determined to be ε ≈ 1.10 *n*av2 , including an additional contribution of approximately 10% due to infrared absorption [49]. The ε values of the PI films estimated from the average refractive indices ranged from 2.59 to 2.87. The low dielectric constants are also attributed to the existence of two CF3 groups in the main chain.

In our previous work, the thermo-responsive behavior of PIs containing CF3 groups was observed in acetyl-containing solvents [41]. Likewise, **PI-2** and **PI-3** also showed LCST behavior in the solvents used here. The transmittance of the ethyl acetate solutions of **PI-2** and **PI-3** was followed as a function of the temperature at a heating/cooling rate of 0.5°C/min. With an increase of the solution temperature, the solutions clearly turned turbid and opaque, indicating a phase transition. The clouding point temperatures (*T*cp) of the **PI-2** and **PI-3** solutions were 44 and 48°C, respectively, at a concentration of 0.1 wt%, and the *T*cp values decreased with an increase of the polymer concentrations. Compared to the polymers synthesized from 4-(40 -aminophenoxy)-3,5-bis(trifluoromethyl) aniline, **PI-2** and **PI-3** have lower *T*cp values, presumably due to lower solubility originating from the increased chain rigidity. The solutions became clear and transparent again when they were cooled.

## **4. Conclusion**

New fluorinated PIs were prepared from the benzidine monomer containing two trifluoromethyl groups on one aromatic ring, 2,6-bis(trifluoromethyl) benzidine.

*Synthesis and Properties of Fluorinated Polyimides from Rigid and Twisted… DOI: http://dx.doi.org/10.5772/intechopen.92010*

Due to the rigid and twisted structure of the diamine monomer, the resulting PIs showed good solubility together with high thermal stability and excellent mechanical properties. The PIs also possessed low refractive indices and low dielectric constants due to the high fluorine contents. These PIs can be considered as promising processable high-temperature materials that can find applications in flexible electronics including substrates of flexible and rollable AMOLED displays and lowk dielectrics for microelectronics.


**1. Introduction**

Liver biopsy remains a golden standard in the evaluation of various liver diseases. It is one of the most specific test allowing to assess the severity of various liver diseases. Clinical evaluation may be inadequate as chronic liver diseases could be asymptomatic for a long pe‐ riod of time. The routinely used laboratory test may be irrelevant, as diffuse changes may possibly be present in the liver in spite of liver function test being within reference values. Percutaneous biopsy allows to obtain a tissue specimen suitable for pathological assessment. Liver biopsy is an important procedure in diagnosing liver diseases in infants and children as it often provides diagnostic information not possible to obtain by other methods. There‐ fore, liver biopsy is considered to be a golden standard in the diagnostics and follow-up of the patients with chronic diffuse hepatopathies. The role of the liver biopsy is to confirm the diagnosis of chronic hepatitis, assess the necroinflamatory activity (grading) and the severi‐ ty of fibrosis (staging), confirm the presence of cirrhosis. Other hepatopathies may be ex‐ cluded as well as associated diseases using this method [1].

The size of liver sample varies from 1 to 4 cm in length and 1.2 to 1.8 mm in diameter. Biop‐ sy specimen represents 1/50,000 of the total mass of the liver, therefore the procedure carries the risk of sampling error. The specimen should be sufficient in length (2-2.5 cm) and num‐ ber of portal spaces (at least 11). The fragmentation of the specimen should be avoided [2]. Liver assessment is also affected by an interpretative error and intraobserver variability of histological interpretation. Moreover, liver biopsy is an invasive procedure carrying the risk

© 2012 Mania et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

of certain complications including pain, bleeding, pneumothorax, puncture of bile ducts or the gall bladder.

bladder, the lung, right kidney and large vessels. Immediately after the procedure ultra‐ sound examination was performed searching for potential complications such as accidental puncture or bleeding. In the case of blind biopsied ultrasound examination was performed by radiologists in situations where complications were suspected basing on clinical symp‐ toms. All patients were monitored 24 hours after the procedure in the department for vital

Safety and Reliability Percutaneous Liver Biopsy Procedure in Children with Chronic Liver Diseases

http://dx.doi.org/10.5772/52619

77

Histological evaluation was performed using Ishak scoring system for grading and staging.

Categorical variables were compared using Fisher's exact test or chi-square test were appro‐

**Figure 1.** Number of liver biopsy performed in the Department of Infectious Diseases and Child Neurology due to vari‐ ous reasons in years 2005-2012 (until July) CHC- chronic hepatitis C, CHB – chronic hepatitis B, NAFLD – non-alcoholic

Liver samples were obtained in all children. Adequate sample size was not obtained in the case of 5 children - 2 samples were to short and did not contain the adequate number of por‐ tal spaces, one sample was fragmented. Four inadequate samples resulted from the blind liver biopsy and 1 was obtained by the ultrasound guided procedure (p=0.21). No significant adverse events were observed. No clinical signs of hemorrhage, no cases of pneumothorax, puncture of the gallbladder nor severe infections were observed. Larger bile ducts were punctured in 4 cases – all undergoing blind procedure (p=0.07). 12 patient were complaining on pain in the right upper quadrant of the abdomen following the procedure that required more intensive analgesics – 3 undergoing ultrasound guided procedure, 9 having blind liver

fatty liver disease, HUO – hepatitis/hepatomegaly of unknown origin, AIH – autoimmune hepatitis

priate. Result with p value <0.05 were considered statistically significant.

signs, pain and other consequences.

**3. Results**

Repeating samples in different time intervals are useful in monitoring the efficacy of treat‐ ments. Many patients are, however, reluctant to experience repeated biopsies, which limits the ability to monitor disease progression and treatment effects. [3].

Due to the limitations of the procedure many non-invasive techniques were developed such as single serological markers, panels of different markers, imaging techniques and elastogra‐ phy [4]. None of the non-invasive methods is suitable and reliable enough to entirely substi‐ tute the liver biopsy. Non-invasive techniques are very helpful in the detection of severe lesions. However, results obtained from patients with intermediate lesions very often over‐ lap between different categories of staging. Nevertheless non-invasive methods are useful in situations where contraindications to liver biopsy do not allow to perform the procedure.

Liver biopsy can be percutaneous, transjugular or laparoscopic. Percutaneous liver biopsy can be blind, ultrasound- guided or ultrasound assisted. Various approaches differ in the number of potential complications and require various equipment. Ultrasound guidance al‐ lows safer intercostal approach and may be useful in the evaluation of relative position of the liver, gall bladder, kidneys and lungs. The technique reduces the risk of hemothorax and pneumothorax and puncture of the gall bladder.

The aim of this study was to evaluate safety and reliability of the liver biopsy in children in relation to obtained results and potential complications.


**2. Application of electron beam technology**

The commercial use of irradiation to disinfect sludge started in 1973 when an industrial gamma ray facility from Geiselbullach near Munich (Germany) used Co-60 and Cs-137 sources [1]. The facility used 90,000 Ci of Co-60 and 570,000 Ci of Cs-137 and treated up to 180 m3 /day of sludge. More recently, a new technology using electron beam accelerators was developed in Miami, Florida where a 1.5 MeV 50 mA accelerator with a throughput of 645 m3 /day, and in Brazil where a 1.5 MeV 37.5 kW accelerator, with a maximum throughput of 45 l/min were described [2]. However, these two studies only addressed the engineering aspects of the facilities and the possibilities to use electron beam irradiation for environmental applications. The radiation effects on the bacterial load and removal of noxious chemical compounds have been performed mostly in small samples of sludge irradiated under laboratory conditions and mainly address either only the microbiological or the chemical effect of radiation in a sample of sludge [3–5]. Processing and disposal of wastewater sludge are a critical problem worldwide [5]; therefore, new technologies to solve this problem are constantly being sought.

Sludge is commonly used as a soil amendment and fertilizer but must be treated in order to remove various bacteria, toxic compounds, parasites, and viruses. Many researchers have shown that exposing sludge to high-energy radiation successfully removes all the bacteria and other organisms from the sludge. Thus, the right dose of radiation will ensure proper sludge disinfection. It has been shown that even a small dose of radiation will remove 99.9% of all bacteria in sludge [6]. In addition to disinfection, irradiation of sludge often accelerates sedimentation and filtration, which helps facilitate removal of water from the sludge. Even while changing the physical makeup of sludge, this does not affect the ability of using sludge as a good fertilizer.

The generation of offensive odors from sewage sludge is also a concern in the subsequent disposal and/or use of sludge. Volatile sulfur-containing compounds (carbon disulfide CS2, dimethylsulfide [(CH3)2S], dimethyldisulfide [(CH3S)2] and volatile carboxylic acids (acetic acid, propanoic acid, butanoic acid) have been identified as odor causing compounds in sewage sludge [7].