**1. Introduction**

Antarctica has long been considered one of the last pristine and remote wilderness areas on Earth. However, in recent years, pollution by hydrocarbons such as polycyclic aromatic hydrocarbons, chlorinated biphenyls, and phenols has become an increasing concern in Antarctica [1–4]. A previous study on the impacts of lubricant oil on Antarctic infauna reported the presence of not readily biodegradable phenol additives in both "conventional" and "biodegradable" lubricants [5]. Hydrocarbons from oil, diesel, and lubricant spills tend to persist due to the harsh conditions in Antarctica [6–8]. Furthermore, aromatic petroleum components such as phenol and phenolic compounds (PCs) are harmful to Antarctic terrestrial and aquatic ecosystems [9,10]. In addition, persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs), are associated with human activities and are subject to long-range atmospheric transport [11]. The direct discharge of phenol into the environment causes severe damage and threats to terrestrial and aquatic ecosystems and, even at low concentrations, it is extremely toxic to aquatic life [10,12–14].

Phenol is a hazardous pollutant that must be removed from the environment [15–17]. Several methods have been published to treat phenol, including the application of physical, chemical, and biological processes. Many studies have shown that bioremediation is more

**Citation:** Lee, G.L.Y.; Zakaria, N.N.; Futamata, H.; Suzuki, K.; Zulkharnain, A.; Shaharuddin, N.A.; Convey, P.; Zahri, K.N.M.; Ahmad, S.A. Metabolic Pathway of Phenol Degradation of a Cold-Adapted Antarctic Bacteria, *Arthrobacter* sp. *Catalysts* **2022**, *12*, 1422. https:// doi.org/10.3390/catal12111422

Academic Editors: Zhilong Wang and Tao Pan

Received: 6 September 2022 Accepted: 7 November 2022 Published: 12 November 2022

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

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

effective in degrading toxic compounds compared to physicochemical treatments and leads to the complete degradation of these compounds [16,18]. Among the various microorganisms reported, phenol-degrading bacteria are the most extensively studied [1,10,19]. Several reports have indicated that members of the genera *Pseudomonas, Rhodococcus, Acinetobacter*, and *Arthrobacter* are capable of degrading phenol [20–22]. In cold environments, the bioremediation of polluted sites requires cold-adapted microorganisms [23–25]. A number of phenol-degrading microorganisms exhibiting cold-adapted features have been isolated from cold regions, such as the alpine areas and Antarctica. Certain bacteria are able to utilize phenol as a carbon and energy source under aerobic or anaerobic conditions [26]. The application of next-generation sequencing (NGS) has catalysed a revolution in biodegradation by predicting the genes involved in biodegradation and other metabolic processes of bacteria [27].

Bioremediation is increasingly viewed as an appropriate remediation technology for cleaning industrial wastewater and contaminated sites [3,28,29]. Aromatic pollutants, such as phenol, are recalcitrant and exhibit anti-microbial properties. The main objectives of this study were to identify the pathway(s) of phenol degradation using whole genome sequencing (WGS) and enzyme assays in two phenol-degrading strains of bacteria originally isolated from Antarctica.

#### **2. Results and Discussion**

#### *Detection of Phenol Degradative Genes via WGS*

The key structural features of the draft genomes of strains AQ5-05 and AQ5-06 are summarized in Table 1. The draft genome size of strain AQ5-05 was 4647352 base pairs (bp) in 116 contigs with an average G + C content of 65.7%, while strain AQ5-06 was 4,139,264 bp in 70 contigs with an average G + C content of 65.7%. The annotated draft genome of strain AQ5-05 contained a total of 4294 coding sequences (CDSs), 9 rRNAs, and 57 tRNAs and that of strain AQ5-06 contained a total of 3981 CDSs, 3 rRNAs, and 50 tRNAs. In comparison, the total CDSs of completely sequenced cold-adapted *Arthrobacter alpinus* ERGS4:06 and *Arthrobacter* sp. strain FB24 were 3538 and 3279, respectively [30,31]. Members of the genus *Arthrobacter* are known as high-GC-content Actinobacteria, consistent with the findings here and on the previously reported *Arthrobacter* spp. [32–35]. According to Kim et al. [36], members of the genus *Arthrobacter* typically have a DNA GC content of 59 to 66%, in agreement with the genome of strains AQ5-05 and AQ5-06. Similar to the genomic size of strain AQ5-06, a 4.12 Mb draft genome sequence for Arthrobacter sp. strain 7749 isolated from marine sediment samples from the Antarctic Peninsula was reported by Sastre et al. [35].

**Table 1.** Genomic features of *Arthrobacter* sp. strains AQ5-05 and AQ5-06.

Figure 1 displays the number of genes in each subsystem and the subsystem coverage. According to RAST annotation, the genome sequences for strain AQ5-05 are classified into 415 subsystems (Table 1). Based on Figure 1A, 48% of the total CDSs are classified into subsystems, while 68% of the CDSs are not included in the subsystems. The subsystem

of amino acid and its derivatives contained the highest number of coding sequences (495 counts), followed by 461 counts in the subsystem of carbohydrates and 301 counts in the subsystem of the cofactors, vitamins, prosthetic groups, and pigments. Figure 1B presents the overview of each subsystem feature and the subsystem coverage. Forty-two percent of a total of 3981 CDSs were categorised into subsystems, while fifty-eight percent of the CDSs were not categorised into subsystems. The subsystem with the highest number of counts was carbohydrate (507 counts), followed by the subsystem of amino acids and derivatives (453 counts) and 287 counts in the subsystem of cofactors, vitamins, prosthetic groups, and pigments that are essential for maintaining the life of bacterial cells [37]. (495 counts), followed by 461 counts in the subsystem of carbohydrates and 301 counts in the subsystem of the cofactors, vitamins, prosthetic groups, and pigments. Figure 1B presents the overview of each subsystem feature and the subsystem coverage. Forty-two percent of a total of 3981 CDSs were categorised into subsystems, while fifty-eight percent of the CDSs were not categorised into subsystems. The subsystem with the highest number of counts was carbohydrate (507 counts), followed by the subsystem of amino acids and derivatives (453 counts) and 287 counts in the subsystem of cofactors, vitamins, prosthetic groups, and pigments that are essential for maintaining the life of bacterial cells [37].

Number of tRNAs 57 50

Figure 1 displays the number of genes in each subsystem and the subsystem coverage. According to RAST annotation, the genome sequences for strain AQ5-05 are classified into 415 subsystems (Table 1). Based on Figure 1A, 48% of the total CDSs are classified into subsystems, while 68% of the CDSs are not included in the subsystems. The subsystem of amino acid and its derivatives contained the highest number of coding sequences

*Catalysts* **2022**, *12*, x FOR PEER REVIEW 3 of 17

<sup>(</sup>**B**)

**Figure 1.** Subsystem category distribution for *Arthrobacter* sp. Strains AQ5-06 (**A**) and AQ5-06 (**B**) were annotated using the RAST server. The red box marks the genes involved in the metabolism of aromatic compounds. **Figure 1.** Subsystem category distribution for *Arthrobacter* sp. Strains AQ5-06 (**A**) and AQ5-06 (**B**) were annotated using the RAST server. The red box marks the genes involved in the metabolism of aromatic compounds.

In order to survive in extremely cold environments, polar bacteria synthesise coldshock proteins (Csps) in response to rapid temperature declines [38]. In strain AQ5-05, 95 counts of genes were responsible for stress responses with three classified as Csps. Rapid temperature reduction decreases the cell membrane fluidity and enzyme activity, and reduces the efficiency of transcription and translation due to the stabilisation of nucleic acid secondary structures and the inhibition of protein folding and ribosome function [38,39]. The genome of strain AQ5-06 contained 109 counts of the genes responsible for stress responses, including five Csps. Of these, four were classified as cold-shock protein A (CspA) and one as cold-shock protein C (CspC). Many bacteria produce Csps, including psychrophiles, mesophiles, and even thermophiles [38–40]. Dsouza et al. [41] noted that Csps were identified in seven temperate and seven Antarctic *Arthrobacter* genomes examined. Previous studies have noted that the production of CspA is induced by cold shock [38,42]. For instance, the overexpression of CspA was reported in *Psychromonas arctica* KOPRI 22215 isolated from Arctic marine sediment [43]. CspC, responsible for the regulation of the growth and expression of the stress response proteins RpoS and UspA, has also been reported in previous studies [44–46].

Seventy-six counts were categorised in the subsystem for the metabolism of aromatic compounds, with seven counts involved in the central aromatic intermediate metabolism, such as catechol, benzoate, and phenol. Similar findings have been reported in the genomes of several *Arthrobacter* spp., such as *Arthrobacter* sp. Edens01 [32], *Arthrobacter* sp. W1 [47], and *A. antarcticus* strain W2 [48]. Genome analysis of *A. alpinus* R3.8 isolated from a soil sample obtained at Rothera Point, Adelaide Island revealed genes involved in the bioremediation of xenobiotic compounds, including naphthalene [49].

Two phenol biodegradation pathways initiated by the oxygenation of phenol followed by the oxidation of catechol at the 1,2-(ortho) or 2,3-(meta) positions have been reported as being typical aerobic phenol degradation pathways for bacteria [19,50]. According to the gene annotation using RAST, genes encoding phenol degradation genes, including phenol 2-monooxgenase, and enzymes involved in the ortho-cleavage of catechol were present in strain AQ5-05.

The physical maps of the gene clusters for both phenol 2-monooxygenase and catechol 1,2 dioxygenase are shown in Figure 2A, with the genes found in the phenol and catechol degradation gene clusters summarised in Table 2. The results from BLASTP searches revealed that the amino acid sequence of ORF6 shared a 90.1% identity with the phenol 2-monooxygenase of *Arthrobacter* sp. UCD-GKA (accession no. WP\_071214692) and 76.9% with the PheA/TfdB family putative FAD monooxygenase from *PaenArthrobacter aurescens* strain TC1 (accession no. WP\_011777158). According to Figure 2A, genes encoding the enzymes phosphoribosylformylglycinamidine synthase (ORF1-3), 3-oxoacyl-[acyl-carrier protein] (ORF7), and antibiotic biosynthesis monooxygenase (ORF8) were found within the gene cluster of the phenol 2-monooxygenase of strain AQ5-05. The enzyme of fatty acid biosynthesis, 3-oxoacyl-[acyl-carrier protein] catalyses the first of the two reduction steps in the elongation cycle of fatty acid synthesis, while phosphoribosylformylglycinamidine synthases are involved in the purine biosynthetic pathway [51,52]. The antibiotic biosynthesis monooxygenase of bacteria catalyses oxygen-dependent and cofactor-independent oxidations or monooxygenations involved in the biosynthetic pathways of polyketide antibiotics [53]. Antibiotic coding genes, such as antibiotic biosynthesis monooxygenase, are also present in the genome of *A. crystallopoietes* strain BAB-32, which may have industrial and medical applications [54]. A previous study has reported the presence of antibiotic activities for seven antibiotic-producing *Arthrobacter* spp. isolated from the Arctic Ocean under different culture conditions [55]. However, no other phenol catabolic genes were found within the gene cluster.

**Figure 2.** Physical map of gene cluster containing phenol 2-monooxygenase and catechol 1, 2-dioxygenase genes from *Arthrobacter* sp. strains AQ5-05 (**A**) and AQ5-06 (**B**). **Figure 2.** Physical map of gene cluster containing phenol 2-monooxygenase and catechol 1,2-dioxygenase genes from *Arthrobacter* sp. strains AQ5-05 (**A**) and AQ5-06 (**B**).

**Table 2.** The putative functions of the genes in phenol and catechol degradation gene clusters from *Arthrobacter* sp. strain AQ5-05. **Table 2.** The putative functions of the genes in phenol and catechol degradation gene clusters from *Arthrobacter* sp. strain AQ5-05.

In the gene-encoding for enzyme C12O, the catA of strain AQ5-05 was shown as ORF10, sharing a 96.5% amino acid sequence identity with the C12O of *Arthrobacter* sp. UCD-GKA (accession no. WP\_071214119). Apart from catA, benzoate catabolic genes, benABCD encoding for benzoate 1,2-dioxygenases and dihydroxycyclohexadiene carboxylate dehydrogenase, and benEK encoding for two benzoate transport proteins, were detected. Similar to the findings of previous studies, catA is chromosomally linked with the ben genes in strain AQ5-05 [56–58]. Two steps are required to convert benzoate to catechol. BenABC encodes for benzoate dioxygenase, which is thought to catalyse the In the gene-encoding for enzyme C12O, the catA of strain AQ5-05 was shown as ORF10, sharing a 96.5% amino acid sequence identity with the C12O of *Arthrobacter* sp. UCD-GKA (accession no. WP\_071214119). Apart from catA, benzoate catabolic genes, ben-ABCD encoding for benzoate 1,2-dioxygenases and dihydroxycyclohexadiene carboxylate dehydrogenase, and benEK encoding for two benzoate transport proteins, were detected. Similar to the findings of previous studies, catA is chromosomally linked with the ben genes in strain AQ5-05 [56–58]. Two steps are required to convert benzoate to catechol. BenABC encodes for benzoate dioxygenase, which is thought to catalyse the oxidation of benzoate to benzoate diol, followed by decarboxylation to catechol by 1,6-dihydroxycyclohexa-2,4diene-1-carboxylate dehydrogenase encoded by benD [59]. Genes encoding pimeloyl-ACP methyl ester carboxylesterase and flavin reductase were also detected within the gene cluster.

According to Figure 2A, both genes encoding putative LuxR family transcriptional regulators were detected in the gene clusters of phenol 2-monooxygenase and C12O. Previous studies have shown that LuxR family transcriptional regulators are activators of the expression of genes encoding enzymes associated with ortho-cleavage through the β-ketoadipate pathway in an aromatic-degrading *Corynebacterium glutamicum* [60,61]. Many transcriptional regulators, including GntR, LuxR, CRP, FNR, TetR, and the Fis family transcriptional regulator, have been identified to be potentially involved in phenol biodegradation in the phenol-degrading *Acinetobacter* sp. DW-1 [62].

Based on the gene annotations, genes encoding the phenol 2-monooxygenase and ortho cleavage enzyme for catechol were also detected in the genome of strain AQ5-06. Figure 2B displays the physical map of the gene cluster containing genes coding for phenol 2-monooxygenase, catechol 1,2 dioxygenase, muconolactone isomerase, and muconate cycloisomerase, and the putative functions of the genes are listed in Table 3.

**Table 3.** The putative functions of the genes in phenol and catechol catabolic gene clusters from *Arthrobacter* sp. strain AQ5-06.

Genetic annotation predicted that strain AQ5-06 is capable of degrading phenol only through the ortho pathway as genes encoding for meta-cleavage were absent in the genome. According to Figure 2B, a transcriptional regulator GntR family was detected within the gene cluster, shown as ORF1. Previous studies have reported the involvement of the GntR transcriptional regulator family in the degradation pathways of aromatic compounds [62–64]. For instance, Gu et al. [62] claimed that a GntR-type regulator was potentially involved in phenol degradation in *Acinetobacter* sp. DW-1. Moreover, Rucká et al. [65] reported that the GntR regulator is required besides the XylR regulator for efficient regulation of phenol degradation operons. Teramoto et al. [66] and Arai et al. [67] reported the presence of the GntR family of transcriptional regulators in the expression of the multicomponent phenol hydroxylase (mPH) of *Comamonas testosteroni* spp.

The gene encoding phenol 2-monooxygenase is shown as ORF3, located between two hypothetical proteins with unknown functions. According to the result from BLASTP searches, the amino acid sequence of phenol 2-monooxygenase from strain AQ5-06 shared the highest identity (88.1%) with phenol 2-monooxygenase from *Arthrobacter* sp. B6 (accession no. WP\_066285143). Phenol 2-monooxygenase serves as the first enzyme in the ring cleavage of phenol by catalysing the ortho-hydroxylation of phenol to catechol [68,69].

The phenol 2-monooxygenase gene cluster of strain AQ5-06 also harbours the genes coding for both the catechol and benzoate pathways. Catechol catabolic genes, catABC, were detected within the gene cluster and are shown as ORF6-8. Previous studies have demonstrated that several bacteria have catABC genes in one operon responsible for the

first three steps of β-ketoadipade, similar to the gene organisation in strain AQ5-06 [70–72]. The amino acid sequence of catA for strain AQ5-06 showed the highest (94.9%) identity with the catA amino acid sequence of *Arthrobacter* sp. AGTC131 (accession no. WP\_104138975.1). Contiguous to the strain AQ5-06 cat operon, benzoate catabolic genes, benABCD, and the benzoate transport protein, benE, were detected within the gene cluster, which is responsible for metabolising benzoate to catechol. Several studies have noted that cat genes are chromosomally linked with ben genes forming a cluster in bacteria [56,73,74]. Adjacent to the benABCD operon, a LuxR family transcriptional regulator was detected. Brinkrolf et al. [60] noted that LuxR family transcriptional regulators are the activators of the expression of genes encoding enzymes associated with ortho-cleavage through the β-ketoadipate pathway in an aromatic-degrading *Corynebacterium glutamicum*. 72]. The amino acid sequence of catA for strain AQ5-06 showed the highest (94.9%) identity with the catA amino acid sequence of *Arthrobacter* sp. AGTC131 (accession no. WP\_104138975.1). Contiguous to the strain AQ5-06 cat operon, benzoate catabolic genes, benABCD, and the benzoate transport protein, benE, were detected within the gene cluster, which is responsible for metabolising benzoate to catechol. Several studies have noted that cat genes are chromosomally linked with ben genes forming a cluster in bacteria [56,73,74]. Adjacent to the benABCD operon, a LuxR family transcriptional regulator was detected. Brinkrolf et al. [60] noted that LuxR family transcriptional regulators are the activators of the expression of genes encoding enzymes associated with ortho-cleavage through the β-ketoadipate pathway in an aromatic-degrading *Corynebacterium glutamicum*.

The phenol 2-monooxygenase gene cluster of strain AQ5-06 also harbours the genes coding for both the catechol and benzoate pathways. Catechol catabolic genes, catABC, were detected within the gene cluster and are shown as ORF6-8. Previous studies have demonstrated that several bacteria have catABC genes in one operon responsible for the first three steps of β-ketoadipade, similar to the gene organisation in strain AQ5-06 [70–

*Catalysts* **2022**, *12*, x FOR PEER REVIEW 7 of 17

Bioinformatics analysis revealed the presence of genes related to the phenol and catechol degradation pathways in strains AQ5-06 and AQ5-05 (Figure 3). Based on gene annotation (Figure 3A), the phenol degradation pathway of strain AQ5-05 is most likely to be the ortho-pathway. Detailed information, including the gene name, gene products, locus tags, length of amino acid sequence, KEGG, COG, and Enzyme Commission number (EC No.), is given in Table 4 for strain AQ5-05 and in Table 5 for strain AQ5-06. The PheA/tfdB family gene in strains AQ5-05 and AQ5-06 were predicted to encode phenol 2-monooxygenase, containing 634 amino acid residues that catalysed the oxidation of phenol to catechol. Bioinformatics analysis revealed the presence of genes related to the phenol and catechol degradation pathways in strains AQ5-06 and AQ5-05 (Figure 3). Based on gene annotation (Figure 3A), the phenol degradation pathway of strain AQ5-05 is most likely to be the ortho-pathway. Detailed information, including the gene name, gene products, locus tags, length of amino acid sequence, KEGG, COG, and Enzyme Commission number (EC No.), is given in Table 4 for strain AQ5-05 and in Table 5 for strain AQ5-06. The PheA/tfdB family gene in strains AQ5-05 and AQ5-06 were predicted to encode phenol 2 monooxygenase, containing 634 amino acid residues that catalysed the oxidation of phenol to catechol.

**Figure 3.** Predicted pathways for the degradation of phenol in *Arthrobacter* sp. strains AQ5-05 (**A**) and AQ5-06 (**B**) based on the presence of gene sequences shown in red fonts. **Figure 3.** Predicted pathways for the degradation of phenol in *Arthrobacter* sp. strains AQ5-05 (**A**) and AQ5-06 (**B**) based on the presence of gene sequences shown in red fonts.

**Table 4.** Characteristics of phenol catabolic genes detected in the genome of *Arthrobacter* sp. strain

AQ5-05.

**Table 4.** Characteristics of phenol catabolic genes detected in the genome of *Arthrobacter* sp. strain AQ5-05.

**Table 5.** Characteristics of phenol catabolic genes found in the genome of *Arthrobacter* sp. strain AQ5-06.

Phenol 2-monooxygenase, also known as phenol hydroxylase, is the first enzyme in phenol degradation that catalyses the hydroxylation of phenol to catechol [50]. The presence of genes coding for phenol 2-monooxygenase or phenol hydroxylase in several *Arthrobacter* spp. has been reported in previous studies [32,75,76]. For example, Nordin et al. [75] revealed that the gene-encoding single-component phenol 2-monooxygenase of 4-chlorophenol-degrading *Arthrobacter chlorophenolicus* A6 shared the highest identity with the PheA gene from the *Pseudomonas* sp. strain EST1001. Qu et al. [76] described a phenol hydroxylase gene cluster (4606 bp), containing six components in the order of KLMNOP from *Arthrobacter* sp. W1. The phenol 2-monooxygenase from strains AQ5-05 and AQ5-06 are highly similar to each other, with 490 identical residues and 53 similar residues within 639 aligned residues (83.8% similarity). A domain for FAD-binding was detected within both proteins, between residue 32 and residue 405. This domain is known for binding to flavin-adenine dinucleotide, which is the cofactor for various flavoprotein oxidoreductase enzymes, including aromatic hydrolases such as salicylate hydrolase, *p*-hydroxybenzoate, and 2,4-dichlorophenol hydroxylase [77]. A conserved domain for the phenol hydroxylase

C-terminal dimerization (thioredoxin-like fold) domain and a highly conserved sequence (DGxxSxxR) for identifying the FAD/NAD(P)H-binding flavoprotein [78] were also identified using Uniprot peptide search utility. These findings suggested that both strains employ class A single-component flavoprotein monooxygenase for phenol hydroxylation. Class A single-component flavoprotein monooxygenases contain a FAD cofactor and depend on NAD(P)H as the external electron donor. Unlike the two-component flavoprotein monooxygenase, single-component flavoprotein monooxygenases can utilize NAD(P)H directly as flavin cofactors.

Catechol is often produced as the intermediate from a wide range of aromatic catabolic pathways [50]. Subsequent to phenol hydroxylation, the catechol intermediate formed is cleaved by C12O encoded by the catA gene in the ortho-pathway. C12O (EC <IP_ADDRESS>) is an intradiol-cleaving enzyme with an Fe3+ prosthetic group, whereas C23O (EC <IP_ADDRESS>) has a catalytic iron ion (Fe2+) in each of its four identical subunits and performs extradiol cleavage [79]. As shown in Figure 3, C12O is the key enzyme in the catechol ortho-pathway, initiating an intradiol cleavage reaction of catechol to form cis,cis-muconate (CCMA), with further processing as described earlier. Similar to the catechol pathway of strains AQ5-05 and AQ5-06, Hong et al. [80] suggested the ortho-pathway for the degradation of catechol in hydrocarbon-degrading *Achromabacter* sp. HZ01 isolated from oil-polluted seawater in the South China Sea, which was predicted using genome sequencing. Moreover, Li et al. [81] confirmed the predicted ortho-pathway of phenol degradation for *Arthrobacter sulfureus* strain XSH(1) based on proteomic analysis.

To validate the pathway of phenol degradation in strain AQ5-06 experimentally, the two routes for the degradation of phenol via the ortho- and meta-pathways were tested by quantifying the resulting products. Prior to the enzyme assay, the bacterium was cultured in the presence of phenol to induce the production of catechol dioxygenases. The catechol degradation pathways of strains AQ5-05 and AQ5-06 were experimentally confirmed through enzyme assays of C12O and C23O by measuring the amount of CCMA and 2-HMSA produced, using colorimetric methods [82–84]. The amounts of CCMA and 2-HMSA formed were determined spectrophotometrically at 260 nm and 370 nm, respectively (Figure 4). The data obtained revealed that only CCMA was produced in the presence of catechol, consistent with the activity of only the C12O in both strains. The average rates of CCMA production over 1 h were 0.52 and 0.67 µmol/min, with a maximum specific activity of the C120 of 24 and 20 U/mg for strains AQ5-05 and AQ5-06, respectively.

The results from both the bioinformatics analysis and enzyme assays confirmed that both studied strains can degrade phenol via the ortho-pathway. A recent study concluded that C12O was detected in all of the fifteen cold-adapted phenol-degrading strains isolated and examined from cold regions, while only eight strains produced C23O [85]. Previous studies have reported several C12O enzymes produced by members of the genera *Arthrobacter*, *Acinetobacter*, *Pseudomonas*, and *Rhodococcus* [83,86–88].

**Figure 4.** Formation of cis,cis-muconate and 2-hydroxymuconic semialdehyde by catechol dioxygenases of *Arthrobacter* sp. strains AQ5-05 (**A**) and AQ5-06 (**B**). Error bars represent mean ± standard deviation for three replicates. **Figure 4.** Formation of cis,cis-muconate and 2-hydroxymuconic semialdehyde by catechol dioxygenases of *Arthrobacter* sp. strains AQ5-05 (**A**) and AQ5-06 (**B**). Error bars represent mean ± standard deviation for three replicates.

#### The results from both the bioinformatics analysis and enzyme assays confirmed that **3. Materials and Methods**

#### both studied strains can degrade phenol via the ortho-pathway. A recent study concluded *3.1. Bacterial Culture and Medium*

that C12O was detected in all of the fifteen cold-adapted phenol-degrading strains isolated and examined from cold regions, while only eight strains produced C23O [85]. Previous studies have reported several C12O enzymes produced by members of the genera *Arthrobacter*, *Acinetobacter*, *Pseudomonas*, and *Rhodococcus* [83,86–88]. **3. Materials and Methods** *3.1. Bacterial Culture and Medium* Phenol-degrading *Arthrobacter* sp. strains AQ5-05 and AQ5-06 were originally isolated from soil obtained on King George Island, South Shetland Islands (maritime Antarctic) [89]. A phenol medium (0.5 g/L) was prepared in 1 L of volume by adding 0.4 g KH2PO4, 0.2 g K2HPO4, 0.1 g MgSO4, 0.1 g NaCl, 0.01 g MnSO4.H2O, 0.01 g Fe2(SO4)3.H2O, 0.01 g Na2MoO4.H2O, and 0.4 g (NH4)2SO<sup>4</sup> to distilled water at pH 7.2. The medium was autoclaved for 15 min at 121 ◦C. The sterilised medium was then augmented with 0.5 g of crystalline phenol.

#### *3.2. Whole Genome Sequencing*

#### 3.2.1. Extraction of Genomic DNA

Strains AQ5-05 and AQ5-06 were each cultured in 50 mL of NB in a shaking incubator at 15 ◦C for 48 h. The cultured broth (1.5 mL) was transferred to an Eppendorf tube and centrifuged at 15,000× *g* for 2 min in order to remove the supernatant. Genomic DNA was extracted using a GeneJET Genomic DNA Extraction Kit (Thermo Scientific, Waltham, MA, USA) and following the manufacturer's protocol. The extracted DNA was examined using 1.0% (*w*/*v*) agarose gel electrophoresis stained with 0.5 µg/mL ethidium bromide (Vivantis, Oceanside, CA, USA) at 100 V for 40 min. Gel electrophoresis was performed using a Lambda/Hind III marker (Vivantis, Shah Alam, Selangor, Malaysia) as the ladder to examine the size of the extracted genomic DNA prior to observation of the gel under UV light. Subsequently, the purity and concentration of extracted genomic DNA were assessed using a Nanodrop spectrophotometer (Bio-Rad, Hercules, CA, USA). The extracted genomic DNA of both strains was sent to Japan's Shizuoka University for whole genome sequencing.

#### 3.2.2. Genome Sequencing and Assembly

The whole genomes of both strains were sequenced using an Illumina MiSeq platform (301 base paired-end reads) at the Instrumental Research Support Office, Research Institute of Green Science and Technology, Shizuoka University, Japan, and the high-quality reads were then assembled using SPAdes version 3.6.2 [90].

#### 3.2.3. Gene Prediction and Annotation

Gene predictions of the genomes of both strains were performed using Prokka 1.12-beta software [91] and the Expert Review version of the Integrated Microbial Genomes (IMG/ER) [92]. Interspersed repetitive sequences were predicted using RepeatMasker [93]. Tandem repeats were analysed by a TRF (Tandem repeats finder) [94]. Transfer RNA (tRNA) genes were predicted by tRNAscan-SE [95]. Ribosomal RNA (rRNA) genes were analysed by rRNAmmer [96]. Small nuclear RNAs (snRNA) were predicted by BLAST against the Rfam database [97]. Annotations of the whole genome sequences were performed using the automated web-based tool, Rapid Annotations using subsystems Technology (RAST) server, with the SEED database [98]. To screen for genes involved in phenol degradation, the amino acid sequences were searched against the protein sequence database from the Basic Local Alignment Search Tool (BLAST) [99], Uniprot [100], the Kyoto Encyclopedia of Genes and Genomes (KEGG) [101], and Clusters of Orthologous Groups (COG) databases [102].

## *3.3. Preparation of Cell Extracts*

Phenol-degrading bacterial strains were cultured in 10 mL of MSM containing 0.7 g/L phenol on a shaking incubator at 150 rpm at 15 ◦C for 5 d. Cells (2 mL) were then harvested by centrifugation at 4500× *g* for 15 min. This was followed by washing with a 50 mM phosphate buffer at pH 7.5 and resuspension in 2 mL of the same buffer. Eventually, cells were disrupted by sonication treatment with 30-s intervals of sonication and 30-s intervals of interruption for a total of 6 min in an ice-cooled bath [83]. Pellets were removed by centrifugation at 9000× *g* for 30 min at 4 ◦C. The collected supernatants were used in the subsequent enzyme assays [82,86].

#### *3.4. Enzyme Assay of Catechol 1,2 Dioxygenase (C12O) and Catechol 2,3 Dioxygenase (C23O)*

Enzyme activity of C12O was determined by adding the cell-free extract (20 µL) to a 50 mM phosphate buffer (pH 7.5) containing 20 mM Na2EDTA and 50 mM catechol to give a final volume of 1 mL, according to the protocols previously reported [82–84]. The enzyme activity of C23O was determined spectrophotometrically at 370 nm, based on the formation of 2-hydroxymuconic semialdehyde (2-HMS). Product formation was measured spectrophotometrically for 1 h at 10-min intervals in an ice bath.


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### IntechOpen Book Series Agricultural Sciences Volume 6

### Aims and Scope of the Series

The importance of agriculture cannot be overstated. It helps sustain life, as it gives us the food we need to survive and provides opportunities for economic well-being. Agriculture helps people prosper around the world and combines the creativity, imagination, and skill involved in planting crops and raising animals with modern production methods and new technologies. This series includes two main topics: Agronomy and Horticulture, and Animal Farming. This series will help readers better understand the intricacies of production agriculture and provide the new knowledge that is required to be successful. The success of a farmer in modern agriculture requires knowledge of events happening locally as well as globally that impact input decisions and ultimately determine net profit.

## Meet the Series Editor

W. James Grichar has been employed with Texas A&M AgriLife Research for over 45 years with an emphasis on research in agronomy, plant pathology, and weed science. He obtained his BS from Texas A&M in 1972 and his Masters of Plant Protection in 1975. He has published 195 journal articles, over 330 research reports and briefs, 11 book chapters, and over 300 abstracts of profession meetings. He also directs research in many crops including

corn, grain sorghum, peanuts, and sesame. He has held various positions in different professional societies including the American Peanut Research and Education Society, Southern Weed Science Society, and Texas Plant Protection Conference in addition to being Associate Editor for Peanut Science and Weed Technology. Significant accomplishments have included spearheading efforts to determine the optimum planting time for soybean production along the upper Texas Gulf Coast. These efforts have shown growers that soybean yields can be improved by 10 to 20% by following a late March to early April plant date. He also has been instrumental in developing a herbicide program for peanut production in the south Texas growing region. Through the development and use of herbicides that are effective against major weed problems in the south Texas region, peanut yields have increased by 25 to 30%.

## Meet the Volume Editors

Dr. Muhammad Sultan is an accomplished Associate Professor of Agricultural Engineering at Bahauddin Zakariya University, Pakistan. Holding a BSc and MSc in Agricultural Engineering with honors from the University of Agriculture Faisalabad, Pakistan, he earned his Ph.D. from Kyushu University, Japan as a recipient of an MEXT fellowship from the Government of Japan. He also conducted postdoctoral research at Kyushu University, Japan, and Simon

Fraser University, Canada as a recipient of Japan Student Services Organization (JAS-SO) and Canadian Queen Elizabeth fellowships. Recognized among the world's top 2% Scientists by Stanford University, USA and Elsevier, Dr. Sultan is the leading scientist in Pakistan for agricultural engineering, according to the AD Scientific Index Ranking. With a vast publication record, he serves as editor for various high-impact journals, contributing significantly to the field of agricultural engineering. Engaged in multiple research projects and revered for supervising theses, Dr. Sultan continues to shape the landscape of energy-efficient agricultural technologies.

Dr. Muhammad Imran is an associate professor at Aston University, UK, and a distinguished researcher renowned for his expertise in sustainable thermal energy systems, specializing in heating, cooling, and distributed energy applications. Dr. Imran's recent leadership in two innovative projects focused on novel and smart irrigation systems powered by hybrid energy solutions showcases a commitment to advancing sustainable practices in agriculture. As

the principal investigator for projects in Ethiopia and Kenya, Dr. Imran is spearheading efforts to integrate cutting-edge cooling technologies into agricultural practices, addressing post-harvest losses in developing countries. Beyond research, Dr. Imran holds leadership roles at Aston University, actively engages with industry, and contributes to the academic community through publications and supervisory roles.

Dr. Fiaz Ahmad is an associate professor in the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan. He obtained his Ph.D. in Agricultural Bioenvironmental and Energy Engineering from Nanjing Agriculture University, China, in 2015, and a postdoctorate degree in Agricultural Engineering from Jiangsu University, Zhenjiang, China, in 2020. He earned a BSc and MSc in Agricultural Engineering from the

University of Agriculture, Faisalabad, Pakistan, in 2001 and 2007, respectively. Dr. Ahmad is the author of more than seventy journal and conference articles. He has supervised more than ten master's students to date. In addition, he is currently supervising five master's and two doctoral students. He completed five research projects and is working on three others. His research interests include various aspects of agricultural and farm mechanization.

### Contents

#### **Chapter 7 123**

Revolutionizing Rice Farming: Maximizing Yield with Minimal Water to Sustain the Hungry Planet *by Shanmugam Vijayakumar, Narayanaswamy Nithya, Pasoubady Saravanane, Arulanandam Mariadoss and Elangovan Subramanian*

#### **Chapter 8 149**

Application of Geospatial Techniques in Agricultural Resource Management *by Syeda Mishal Zahra, Muhammad Adnan Shahid, Zahid Maqbool, Rehan Mahmood Sabir, Muhammad Safdar, Muhammad Danish Majeed and Aneela Sarwar*


**3.1.2 Hardware design**

244 Wireless Communications and Networks – Recent Advances

Here we only focus on the indoor wireless MIMO channel for WLAN like devices. The measurement system supports 20/40MHz bandwidth by suitable RFIC [16][17]. The system

Sampling frequency s*f* 60MHz Symbol rate of PN sequence *R*symb 20M Symbol/s

Period of PN sequence, express in units of *T*symb *N*PN 127 Length of CIR, express in units of *T*symb *N*CIR 127 Sampling interval *T*s s 1 *f* Interpolated sample interval *T*i symb *T* 2 Symbol interval of PN sequence *T*symb symb 1 *R*

Symbol timing synchronizer is a critical module of the digital receiver design of the channel sounder based on sliding correlation channel measurement. Gardner's symbol timing recovery method is used in this system [18][19]. The structure of the symbol timing synchronizer is shown in Fig. 4. All the processing of this synchronizer is done in digital domain. No interaction between analog and digital part of the system is needed. This synchronizer is capable of compensating sampling phase and frequency offset and is

Interpolator Timing error

*yk*  (*T*i)

detector

*un*

(*T*symb)

*yk*  (*T*symb)

Loop filter

The sampled data *ym* , which is filtered by matching filter, is then feed into the interpolator to compute the desired sampled strobe *k y* . This is done by digital interpolation, controlled

*wk*  (T*symb*)

the NCO is / *TT K i symb* , where *K* is an integer. The loop consisting of timing error

detector, loop filter and NCO function just like a DPLL, where *un* , *wk* and

timing error signal, NCO control word and NCO register content respectively.

*k* . Ideally, the period of

*k* represents

by NCO (Numerical Controlled Oscillator) and fraction interval

NCO

*ηk*

*μk*

Compute *μ*

(*T*i)

(*T*i)

**Name Symbol Value**

parameters of baseband are listed in Table 1.

**<IP_ADDRESS> Symbol timing synchronizing algorithm**

Table 1. System Parameters.

independent of carrier phase [20].

*ym*  (*T*s)

underflo

Fig. 4. Symbol timing synchronizer.

The whole measurement system hardware consists of several modules: antennas module, multi-channel AD/DA module, baseband processing FPGA board, USB access module and a computer Graphical User Interface (GUI) module. The architecture of hardware is showed in Fig. 5. The RF board is based on MAX2829, which can support MIMO operation. We choose the FFP board (IAF GmbH) as FPGA prototyping platform for baseband signal processing, RF control and interface to PC. The interface between the FFP board and the PC is an USB2.0 port. The GUI program runs on the computer for user to control the channel measurement functions and demonstrate the real time test results. Because the most effort is on the development of FPGA, here we focus on the design of baseband transceiver.

Fig. 5. Hardware architecture of the channel measurement system.

The baseband transceiver module performs the baseband signal processing of a 2x2 MIMO channel sounder. This module generates the baseband probing signal, i.e. a BPSK modulated PN sequence, and delivers the CIR extracted from the received signal to the upper-level module.

The block diagram of this module is shown in Fig. 6. The module can be divided into three parts. The first part is the transmitter, which includes signal generator and transmit multiplexer. The second part is the receiver, which includes receive buffers, signal processor, and data buffer. The last part is the control logic of the module.

The functions of the sub-modules are as follows:

 Signal processor performs the signal processing, i.e. filtering, symbol timing, and sliding correlation, to extract the CIR from received signal.

Fig. 6. Baseband transceivers.


**6. Policy and practice implications for strategic communication**

What policies and best practices are needed to guide effective communication efforts about cancer prevention and control to vulnerable populations? First and foremost, communication interventions to educate vulnerable populations need to be strategic and evidence-based. Cancer communication is too complex a process to be handled without careful planning

elated health information to different audiences. Just as with the use of strategic messages, it is a good idea to pre-test the use of different information sources and different communication channels with targeted audiences.39 Message testing research that examines the impact of different communication sources on targeted audience members can help strategic communicators make good decisions about the best representatives to employ to

Evaluation research should be a basic process that is built in to all cancer prevention and control communication efforts.50 Cancer communication efforts should always begin with careful needs analysis and audience analysis to identify the best goals, targets, and strategies for communication interventions. The messages designed and channels identified for delivering cancer prevention and control messages should be carefully tested with representatives from targeted groups to make sure they communicate effectively to these groups and provide them with the intended information. Message testing can also be used to refine and improve message strategies as a form of formative research. Usability testing is also a useful formative research strategy for testing consumer access to, comfort with, and

A critical juncture in communicating risk and benefit information to vulnerable audiences is evaluating how well different communication strategies work to educate targeted audiences about important health issues.50 It is important to assess how well consumers really understand the risks and benefits that are being communicated and what differences communication programs are making in promoting informed consumer decision-making. A first step in evaluating the outcomes of communication efforts is to establish clear baseline measures of consumer understanding before introducing new health education programs. These baseline measures can be used as a starting point for tracking the influences of communication efforts on consumer attitudes, beliefs, and behaviors concerning cancer prevention and control.45 Feedback mechanisms, such as consumer surveys, focus groups, hotlines, help-desks, and comment cards, can be introduced as integral parts of communication interventions for tracking and evaluating consumer understanding of health messages, as well as their reaction to campaign strategies. The data gathered through these feedback mechanisms can be used to refine health communication programs and track progress in health education. Evaluation research can track the influences of communication efforts on consumer beliefs, behaviors, and even their physiological outcomes. Furthermore, it is important to conduct cost-benefit analyses to determine whether cancer prevention and control communication interventions are cost-effective. (See Figure 1 for a summary of the different forms of evaluation research that should be used to inform strategic cancer

**6. Policy and practice implications for strategic communication**

What policies and best practices are needed to guide effective communication efforts about cancer prevention and control to vulnerable populations? First and foremost, communication interventions to educate vulnerable populations need to be strategic and evidence-based. Cancer communication is too complex a process to be handled without careful planning

deliver health information to different audiences.

**5. Evaluating health communication interventions**

ability to effectively use communication channels and tools.

communication interventions).

Fig. 1. The Functions of Evaluation Research for Informing Strategic Cancer Communication Interventions.

and data. It is also critical for health educators to adopt culturally sensitive communication practices to reach and influence vulnerable populations. Community participative communication interventions are a valuable strategy for integrating consumers' perspectives into cancer education efforts and building community commitment to health communication interventions.41,42 It is a good idea to consider introduction of relevant communication technologies, such as tailored information systems, to support health

Strategic Communication for Cancer Prevention

**8. Acknowledgment**

**9. References**

216

193-211

and Control: Reaching and Influencing Vulnerable Audiences 385

I acknowledge the support of my current colleagues at George Mason University's Center for Health and Risk Communication and my former colleagues in the Health Communication and Informatics Research Branch at the National Cancer Institute for their

[1] Kreps, G.L., & Sivaram, R. *(*2008). The central role of strategic health communication in

[2] Kreps, G.L. *(*2008*).* Strategic use of communication to market cancer prevention and

[3] Kreps, G.L. (2006). Communication and racial inequities in health care. *American*

[4] Kreps, G.L., & Sparks, L. (2008). Meeting the health literacy needs of vulnerable

[5] Neuhauser, L., & Kreps, G.L. (2008). Online cancer communication interventions:

[6] Singh, G.K. & Hiatt, R.A. (2006). Trends and disparities in socioeconomic and

[7] Kunitz, S.J. & Pesis-Katz, I. (2005). Mortality of white Americans, African Americans, and

[8] Haider, M. (2005). *Global public health communications: Challenges, perspectives, and strategies*. Jones & Bartlett Publishers, ISBN-13:<PHONE_NUMBER>763, Sudbury, MA. [9] Kreps, G.L. (2003). The impact of communication on cancer risk, incidence, morbidity, mortality, and quality of life. Health Communication. Vol.15, pp. 163-171 [10] Morris L.A., & Aikin, K.J. (2001). The "Pharmokinetics" of patient communications.

[11] Patel, V.L., Branch, T., & Arocha, J.F. (2002). Errors in interpreting quantities as

[12] Chandra, A., Malcolm, N., & Fetters, M. (2003). Practicing health promotion through

[13] Moisan, J., Gaudet, M., Grégoire, J.P., & Bouchard, R. (2002). Non-compliance with drug

[14] Kreps, G.L. (2005). Communication and racial inequities in health care. American

pharmacy counseling. Health Promotion Practice. Vol.4, pp. 64-71

procedures: The case of pharmaceutical labels. Int J Med Informatics. Vol.65, pp.

treatment and reading difficulties with regard to prescription labelling among

populations. *Patient Education and Counseling. Vol. 71(3),*pp. 328-332

enhancing breast cancer outcomes across the continuum of care in limited-resource

control to vulnerable populations. *Health Marketing Quarterly.* Vol. *25(1/2), pp.* 204-

Meeting the literacy, linguistic, and cultural needs of diverse audiences. *Patient*

behavioural characteristics, life expectancy, and cause-specific mortality of nativeborn and foreign-born populations in the United States, 1979–2003*. International*

Canadians: The causes and consequences for health of welfare state institutions and

input and encouragement in helping me refine the ideas presented in this chapter.

countries. *Cancer,* Vol. *113(S8),* pp. *2331-2337*

*Behavioral Scientist,* Vol. *49(6). pp. 760-774*

*Education and Counseling*, Vol. *71(3),* pp. 365-377

*Journal of Epidemiology. Vol.*35, pp. 903-919

policies. *Milbank Quarterly*. *Vol*.83, pp. 5-39

Drug Informatics Journal. Vol.35, pp. 509–527

seniors. Gerontology. Vol.48, pp. 44-51

Behavioral Scientist. Vol49, pp. 1-15

education efforts.19 It is also a good idea to incorporate health communication training for both health care providers (educators) and consumers to enhance the quality of crosscultural communication efforts.20,51

Strategic communication efforts can promote cancer prevention and control for at-risk populations. To achieve these goals efforts must be taken to develop evidence-based communication campaigns to increase awareness about prevention. These campaigns must be designed to persuade members of at-risk groups to adopt prevention recommendations, change negative health habits, and adopt healthy lifestyles. Communication campaigns should be designed not only to initiate these healthy behavior changes, but to also reinforce and sustain behavior changes over time. Campaigns should be designed to increase awareness about the importance of developing healthy lifestyles and engaging in regular recommended cancer screening and early detection activities. Efforts should be taken to implement and promote easily accessible and affordable screening programs. Screening practices should be monitored to refine and improve screening programs and promotion activities.


*2.4. Characterizations of MIOs and MNs*

The morphology of MIOs are analyzed by field emission scanning electron microscope (JEOL Ltd., Tokyo, Japan), and MIOs were dried up on silicon wafers. The wafers were anchored to SEM specimen mounts using double-sided carbon tape, and sputter deposited with platinum in 10 mA for 120 s. An X-ray diffractometer (D8 Advance X-ray Diffractometer, Bruker, Billerica, MA, USA) was used for identification of crystal dimensions. A thermogravimetric analyzer (Seiko Instruments Inc., Tokyo, Japan) provided a proportion of each compositions based on differential melting temperature with physical and chemical properties. Dynamics laser scattering (DLS) analysis, by using a particle sizer (Nano-ZS, Malvern, Worcestershire, UK), determined the average size and size distribution of MIOs diluted with deionized water. Field-dependent magnetization curves were evaluated by a superconducting quantum interference device (San Diego, CA, USA) from −10,000 to 10,000 Oe.


*5.2. The Influence of Perceived Benefits on Use Decisions*

The test results in this study prove that the perceived benefits affect the decision to use. This means that usefulness in information technology or finance is the benefits obtained and expected by users in carrying out their activities [27]. Perceived usefulness is defined as where someone believes that using a system can improve their performance. Interpreting the perception of usefulness is the subjective profitability of potential users who use a particular application to facilitate their activities. Research by Kim, Mirusmonov, and Lee [10], and Wardhani [35] and Priyono [3], show that the perception of benefits has a positive and significant effect on the use of technology electronic payment. Perception of benefit is a subjective probability of potential users using a particular application to facilitate the performance of their work. Perception of benefits shows that respondents rate electronic money as providing high benefits, meaning that consumers, in this case users, feel that electronic money provides various advantages in the form of high benefits, such as providing speed and accuracy in transactions, the ability to be used for all forms of transactions of small value or value, used with a high frequency, being practical and easy to use for transactions, and more efficient than cash, thereby having a positive effect on interest in electronic money.


**8. Conclusion**

BC continues to be cancer with the highest number of cases and deaths worldwide. While the TNBC subtype is characterized by a worse prognosis, due to high rates of recurrences and metastasis, there is no standardized treatment so far, reasons that call the attention of researchers to find new therapeutic proposals. Thus, noting that several integrins are overexpressed during the progression of BC; disintegrins have pharmacological potential as a new alternative in cancer.


**2. Main driving forces of the Amazon river mouth discharge**

Tidal propagation in estuaries is mainly affected by friction and freshwater discharge, together with changes in channel depth and morphology, which implies damping, tidal wave asymmetry and variations in mean water level. Tidal asymmetry can be important as a mechanism for sediment accumulation while mean water level changes can greatly affect navigation depths. These tidal distortions are expressed by shallow water harmonics, overtides and compound tides (Gallo, 2004). The Amazon estuary presents semidiurnal overtides, where the most important astronomic components are the M2 (lunar component) and S2 (solar component), consequently, the most common overtide is the M4 (M2 + M2) and the main compound tide is the Msf (relative to fluvial flow). Amplitude characteristics of the mouth of the Amazon River is represented by tidal components M2 and S2, of 1,5m and 0,3m, respectively, corresponding to North Station Bar, Amapá State (Galo, 2004; Rosman, 2007).

Form factor (F) expressing the importance of scale on components of the diurnal and semidiurnal tides, the Amazon estuary can be classified as a typical semi-diurnal tide (0 < F < 0.25). However, this classification does not considers the effects of river discharge. River discharge certainly contribut to friction and to balance the effect of convergence in the lower estuary and also to what happens between the platform edge of the ocean station and the the mouth of the Amazon River.

There is evidence of nonlinearity in tidal propagation, which can be observed by the gradual redistribution of power between M2 and its first harmonic M4. Considering tides as the sum of discrete sinusoids, the asymmetry can be interpreted through the generation of harmonics in the upper estuary (Galo, 2004; Rosman, 2007). In the case of a semi-diurnal tide, with its

interaction. They drive biogeochemical processes (carbon and nutrient flows), variations in water quality (physical-chemical and microbiological). They drive biogeochemical processes (river bottom and suspended sediments) (Richey et al., 1990; Van Maren & Hoekstra, 2004, Shen et al. 2010; Hu & Geng, 2011). The understanding of the Amazon River mouth flows is an important and opened question to be investigated in the context of the river-ocean

In Brazil, the National Water Agency (ANA) monitors water flows at numerous locations throughout the Amazon basin (Abdo et al. 1996; Guennec & Strasser, 2009). However, the last monitoring station located on the Amazon River and nearest to the ocean is *Obidos* (1°54'7.36"S, 55°31'10.43"W). There are no systematically recorded data available in downriver locations towards the mouth. The Amapá State coast is, *geographically*, an ideal site for such future systematic experimental flow measurements, since about 80% of the net discharge of the Amazon River flows in the North Channel located in front of the city of Macapá (0° 1'51.41"N, 51° 2'56.88"W) (ANA, 2008). The fact that this flow is not continuous and varies with ocean tides, creating an area of inflow-outflow transition makes this region

This research focus on two main issues: a) to establish an overview of physical aspects over transect T2 in the North Channel of the Amazon River, where measurements were performed for quantification of liquid discharge and additional sampling procedures for assessing water quality and quantify concentration of CO2 in the air and water; b) to evaluate typical local effects of river flow interacting with the shore and small rivers, based

Tidal propagation in estuaries is mainly affected by friction and freshwater discharge, together with changes in channel depth and morphology, which implies damping, tidal wave asymmetry and variations in mean water level. Tidal asymmetry can be important as a mechanism for sediment accumulation while mean water level changes can greatly affect navigation depths. These tidal distortions are expressed by shallow water harmonics, overtides and compound tides (Gallo, 2004). The Amazon estuary presents semidiurnal overtides, where the most important astronomic components are the M2 (lunar component) and S2 (solar component), consequently, the most common overtide is the M4 (M2 + M2) and the main compound tide is the Msf (relative to fluvial flow). Amplitude characteristics of the mouth of the Amazon River is represented by tidal components M2 and S2, of 1,5m and 0,3m, respectively, corresponding to North Station Bar, Amapá State (Galo, 2004;

Form factor (F) expressing the importance of scale on components of the diurnal and semidiurnal tides, the Amazon estuary can be classified as a typical semi-diurnal tide (0 < F < 0.25). However, this classification does not considers the effects of river discharge. River discharge certainly contribut to friction and to balance the effect of convergence in the lower estuary and also to what happens between the platform edge of the ocean station and the

There is evidence of nonlinearity in tidal propagation, which can be observed by the gradual redistribution of power between M2 and its first harmonic M4. Considering tides as the sum of discrete sinusoids, the asymmetry can be interpreted through the generation of harmonics in the upper estuary (Galo, 2004; Rosman, 2007). In the case of a semi-diurnal tide, with its

integrated system.

Rosman, 2007).

the mouth of the Amazon River.

a challenging subject for water research.

on turbulent fluid flow modeling and simulation.

**2. Main driving forces of the Amazon river mouth discharge**

main components M2 and M4 first harmonic, the phase of high frequency harmonic wave on the original controls the shape of the curve and therefore the asymmetry.

Three major effects characterize the amount and behaviour of flow it the mouth of the Amazon River: (a) relative discharge contributions from sub-basins of the main channel; b) tidal cycles and; (c) regional climate dynamics.

According to Gallo (2004) the Amazon River brings to the Atlantic Ocean the largest flow of freshwater in the world. Based on *Óbidos* records, there is an average flow of approximately 1.7x105 m3/s, with a maximum of approximately 2.7x105 m3/s and a minimum of 0.6x105 m3/s. According to ANA (2008), the flow reaches a net value of approximately 249,000 m3/s, with a maximum daily difference of 629,880 m3/s (ebb) and a minimum of -307,693 m3/s (flood). The most important contributions come from the *Tapajós* River with an average flow of approximately 1.1x104 m3/s, the *Xingu* River with an average of approximately 0.9x104 m3/s and *Tocantins* River, at the southern end of the platform, with an approximate average flow of 1.1x104 m3/s.

Penetration of a tidal estuary is result of interaction between river flow and oscillating motion generated by the tide at the mouth river, where long tidal waves are damped and progressively distorted by the forces generated by friction on river bed, turbulent flow characteristics of river and channel geometry. Gallo (2004) describes that propagation of the tide in estuaries is affected mainly by friction with river bed and river flow, as well as changes in channel geometry, generating damping asymmetry in the wave and modulation of mean levels. Such distortions can be represented as components of shallow water, overtides and harmonic components. The Amazon River estuary can be classified as macrotidal, typically semi-diurnal, whose most important astronomical components are M2 (principal lunar semidiurnal) and S2 (Principal solar semidiurnal) and therefore the main harmonics generated are high frequency, M4 (lunar month) and the harmonic compound, Msf (interaction between lunar and solar waves) (Bastos, 2010; Rosman, 2007).

In the Amazon the most important climatic variables are convective activity (formation of clouds) and precipitation. The precipitation regime of the Amazon displays pronounced annual peaks during the austral summer (December, January and February - DJF) and autumn (March, April and May - MAM), with annual minima occurring during the austral winter months (June, July and August - JJA) and spring (September, October and November - SON). The rainy season in Amapá occurs during the periods of DJF and MAM (Souza, 2009; Souza & Cunha, 2010).

The variability of rainfall during the rainy season is directly dependent on the large-scale climatic mechanisms that take place both in the Pacific and the Atlantic Oceans (Souza, 2009). In the Pacific Ocean, the dominant mechanism is the well-known climatic phenomenon El Niño / Southern Oscillation (ENSO), which has two extreme phases: El Niño and La Niña. The conditions of El Niño (La Niña) are associated with warming (cooling) anomalies in ocean waters of the tropical Pacific, lasting for at least five months between the summer and autumn. In the Atlantic Ocean, the main climatic mechanism is called the Standard Dipole or gradient anomalies of Sea Surface Temperature (SST) in the intertropical Atlantic (Souza & Cunha, 2010).

This climate is characterized by a simultaneous expression of SST anomalies spatially configured with opposite signs on the North and South Basins of the tropical Atlantic. This inverse thermal pattern generates a thermal gradient (inter-hemispheric and meridian) in the tropics, with two opposite phases: the positive and negative dipole. The positive phase of the dipole is characterized by the simultaneous presence of positive / negative SST

Challenges and Solutions for Hydrodynamic and Water Quality in Rivers in the Amazon Basin 71

2) interpolate the temporal evolution of flow and velocity from these measurements; 3) integrate the values with the tidal cycle to obtain the average flow rate (or velocity); 4) analyze the maximum and minimum flow, and the relationship between flow/velocity and

Fig. 1 shows the location of Transect T2 (blue line) of the North Channel and Matapi River, both studied by Brito (2010) and Cunha (2008) nearly to city of *Macapá*, respectively .The requirement for local knowledge of the river bathymetry is demonstrated by the geometric complexity of the channels and variations in the average depths of the channel. Cunha (2008) observed depths ranging from 3 m (minimum) to approximately 77 m (maximum) in

Brito (2010) has studied the water quality sampled water quality and participated in the quantification of the measurements of liquid discharge in the North Channel. The width of the North Channel is approximately 12.0 kilometres (30/11/2010). The width of the South

Fig. 1. Features in river sections close to Transect T2 located in the North Channel of the

Muste & Merwade (2010) describe recent advances in the instrumentation used for investigations of river hydrodynamics and morphology including acoustic methods and remote sensing. These methods are revolutionizing the understanding, description and

**3.1 Methodological approach for discharge measurement in large rivers**

level, as described by ANA (2008), Cunha et al. (2006) and Silva & Kosuth (2001).

Channel was approximately 13.0 kilometres (12/02/2010).

Amazon River – Amapá State (S0 03 32.2 W51 03 47.7)

modelling of flows in natural rivers.

the section indicated.

anomalies, setting the north / south basins of the tropical Atlantic Ocean. The dipole negative phase of the configuration is essentially opposed. Several observational studies showed that the phase of the dipole directly interferes with north-south migration of the Intertropical Convergence Zone (ITCZ). The ITCZ is the main inducer of the rain weather system in the eastern Amazon, especially in the states of Amapá and Pará, at its southernmost position defines climatologically the quality of the rainy season in these states (Souza & Cunha, 2010). The behavior of the climate is important because it significantly influences the hydrological cycle and, therefore, the hydrodynamic and mixing processes in the water.

According to Van Maren & Hoesktra (2004) the mechanisms of intra-tidal mixing depend strongly on seasonally varying discharge (climate) and therefore hydrodynamics. In this case, during the dry season, there is a breakdown of stratification during the tidal flood that occurs in combination with the movements of tides and advective processes. Intra-tidal mixing is probably greater in semi-diurnal than in diurnal tides, because the semi-diurnal flow velocity presents a non-linear relationship with the mixture generated in the river bed and the mean velocity.

A second, Hu & Geng (2011), studying water quality in the Pearl River Delta (PRD) in China, found that coupling models of physical transport and sediments could be used to study the mass balance of water bodies. Thus, most of the flows of water and sediment occur in wet season, with approximately 74% of rainfall, 94% water flow and 87% of suspended sediment flow. Moreover, although water flow and sediment transport are governed primarily by river flow, tidal cycle is also an important factor, especially in the regulation of seasonal structures of deposits in river networks (deposition during the wet season and erosion in the dry season). As well as net discharge there are several types of physical forces involved in these processes, including: monsoon winds, tides, coastal currents and movements associated with gravitational density gradients. Together these forces seem to jointly influence the control of water flow and sediment transport of that estuary.

A third example, according to Guennec & Strasser (2009), hydrodynamic modeling along a stretch of the Manacapuru-Óbidos river in the upper Amazon a stretch of the *Manacapuru-Óbidos* river in the upper Amazon revealed that the ratio of liquid flow that passes through the floodway changes from 100% during the low water period to 76% (on average) during the high water period. Expressed in volume, this means that about 88% of the total volume available during a hydrological cycle moves through the floodway of the river, and only 12% moves through the mid portion. The volume that reaches the fringe of the flood plain is approximately 4% and appears to be temporarily stored.

Based on the climatic characteristics of the State of Amapá, one of the main challenges for both hydrological and hydrodynamic studies is to integrate meteorological information from the Amazon Basin and include these forces when evaluating the responses of aquatic ecosystems in the Lower Amazon River estuary (Brito, 2010; Bastos, 2010; Cunha et al., 2006; Rickey et al., (1986), Rosman (2007), Gallo (2004), ANA (2008) and Nickiema et al., (2007).


**4. Discussion**

Oasis is an important area that guarantees the normal life and production of residents in arid region. Its climate effects are vital important to the sustainable development of the oasis shelterbelts [30]. The stability of the microclimate was maintained and natural disasters were reduced by oasis shelterbelts [31]. Our results showed that under the influence of a large-scale oasis shelterbelts, air temperature, ground temperature and evaporation decreased 5.13% ~ 24.74%, 2.38% ~ 20.09% and 7.06% ~ 17.68%, respectively. However, relative humidity and precipitation increased 6.93% ~ 25.53% and 4.30% ~ 50.15%, respectively. The cold-humid effect of the atmosphere inside shelterbelt formed a cold-humid column. The warm-dry effect of the desert atmosphere outside shelterbelt caused a warm-dry air current. The interaction of the cold-humid effect and the warm-dry effect formed a local circulation. The warm air outside shelterbelt was transported to the sky above the shelterbelt, thus formed a stable thermal inversion layer. Therefore, the cold and humid air inside shelterbelt was maintained, which was beneficial to crop growth in oasis [32].

The microclimate of oasis shelterbelts was conducive to the overwintering of plants and kept them from the damage of high temperature in summer. Therefore, it played a vital role in plant growth, nutrient accumulation and quality improvement [33]. The relative humidity was increased 0.5% ~ 18.6%, whereas the evaporation was decreased 18.4 ~ 1282.8 mm by oasis shelterbelts in the northeastern edge of Ulan Buh Desert. This played a positive role in increasing soil moisture and inhibiting crop transpiration, thereby increasing crop yields and improving the soil quality in long time [33]. The oasis shelterbelts in the northeastern edge of Ulan Buh Desert also had the effect of cooling in summer and heat preservation in winter. It decreased air temperature in spring, summer and autumn, whereas increased air temperature in winter inside shelterbelt. This was similar to the microclimate effect of the shelterbelt in Heihe River Basin [34]. The air heat transfer was affected by the barrier of shelterbelt, caused the temperature dynamic lag between inside and outside shelterbelt [33]. The attraction and reflection of plants inside shelterbelt to the solar radiation energy reduced the solar radiation energy absorbed by the air. Meanwhile, the growth and transpiration of plants consumed a lot of heat energy. In addition, the shading effect of the shelterbelt also resulted in the cooling effect especially in spring, summer and autumn. On the other side, it had a heat preservation effect in winter [35]. During the growing season of crop, the blocking effect on airflow was enhanced by the shelterbelt in the leafy season. The wind speed was reduced by the weakening of turbulence exchange, whereas the wind speed was increased inside shelterbelt in the leafless season [35]. Saturated water vapor was formed when the temperature inside shelterbelt was lower than that outside shelterbelt. The canopy blocked the exchange of airflow between inside and outside shelterbelt. In addition, the water vapor diffusion from inside to outside shelterbelt was reduced by the decrease of wind speed, which resulted in a higher relative humidity inside than outside shelterbelt [36].

#### *Ecological Effects of Oasis Shelterbelts in Ulan Buh Desert DOI: http://dx.doi.org/10.5772/intechopen.98679*

Wind speed increased gradually with the increase of height inside and outside shelterbelt with the significant relationship of a power function. There were obvious differences in wind speed, wind direction, and eolian sediment flux inside and outside shelterbelt in the northeastern edge of Ulan Buh Desert. The results were consistent with the wind speed distribution characteristics of Minqin Oasis and Badain Jaran Desert on the oasis and desert-oasis region, which increased with the height from 0 to 50 m [37]. During the occurrence of sandstorms, wind speed profile and the migration process of sand and dust particles were affected by the type of underlying surface, thereby affecting the distribution of aeolian sediment flux [38–40]. For example, the relationships between the near-surface aeolian sediment flux and height were expressed as an exponential function in Tengger Desert [40–42], a power function in Minqin Oasis [38], whereas both functions in Taklimakan Desert [43]. Wind speed and the aeolian sediment flux through the shelterbelt were reduced significantly by oasis shelterbelts. The microclimate was improved by oasis shelterbelts, together with weakening turbulent exchange inside shelterbelt, and inhibiting vertical transportation of aeolian sediment. Therefore, the quality of aeolian sediment was less inside than outside shelterbelt. The dust is transported under the action of wind, and the sediment flux varies with height during the transport process.

The difference of sand driving wind speed inside and outside shelterbelt was different in wind speed. The frequency of strong sand driving wind exceeding 11 m·s−1 was decreased significantly inside than that outside shelterbelt in the northeastern edge of Ulan Buh Desert. However, the frequency of sand driving wind between with 5 m·s−1 - 7 m·s−1 and between 7 m·s−1 and 9 m·s−1 increased inside than outside shelterbelt. When sandstorm passed through the oasis shelterbelts, which acted as a tall rough element, and part of the airflow was lifted up, a relatively high speed free-stream was formed above the shelterbelt canopy. After crossing the shelterbelt, it formed a sinking airflow, which spread in all directions at a certain distance in the leeward zone. Another part of the airflow entered into the shelterbelt. Due to the block and friction of the trees, the airflow consumed a large amount of energy during dispersion, thus a low speed bound-stream was formed under the shelterbelt canopy. Therefore, the frequency of sand driving wind with high speed was decreased, whereas the frequency of sand driving wind with low wind speed was relatively increased inside oasis shelterbelts [34]. Because there was spatial and temporal heterogeneity in the shelterbelt distribution in a specific space with the form of grids [38], our methods on the shelterbelt scale cannot be applied to the regional scale [44]. Therefore, the comprehensive evaluation index system of the environmental benefits of shelterbelts needs to be studied urgently on regional scale. Moreover, it was suggested to focus on deducing the results from shelterbelt scale to multiple shelterbelts or even landscape scales in the future, using interpolation methods to establish models on the time scale [45], and integrating vegetation, soil, climate and other factors, in order to obtain simple and effective results.


*3.1. Callus Proliferation*

Callus of *F. cirrhosa* cultured in Murashige and Skoog's liquid medium with 2,4-D, under agitated conditions in dark incubation for six weeks, proliferated readily. Callus not only grew in volume but also became embryogenic since early stages of embryos were observed (Figure 4). There are several reports from our laboratory where various secondary metabolites have been obtained from different culture systems [25], including callus cultures of several medicinally important plant species, e.g., *Salvia miltiorrhiza* Bunge [26] and *Saussurea involucrata* Kar. et Kir. [27]. These and numerous other reports demonstrate that callus cultures have tremendous potential for the sustainable and large-scale production of secondary metabolites used in pharmaceuticals. Biotechnological applications of plant callus cultures have been recently reviewed [28], and, according to the author, the full potential of callus plant culture technology has not yet been exploited. Callus cultures and suspension cell cultures offer a wide range of applications in agriculture and horticulture, including for Chinese medicinal plants. Genetically transformed callus cultures cannot only be used for the synthesis of bioactive compounds but also for the development of plants with traits [28].

of plants with traits [28].

according to the author, the full potential of callus plant culture technology has not yet been exploited. Callus cultures and suspension cell cultures offer a wide range of applications in agriculture and horticulture, including for Chinese medicinal plants. Genetically transformed callus

**Figure 4.** Embryogenic calli (EC) of *F. cirrhosa* D. Don growing in Murashige and Skoog's liquid basal medium supplemented with 2,4-D (0.5 mg/mL) and 2% sucrose. The image is a photograph taken by a scanner of the bottom of the culture flask after six weeks of incubation (bar = 1.2 cm). **Figure 4.** Embryogenic calli (EC) of *F. cirrhosa* D. Don growing in Murashige and Skoog's liquid basal medium supplemented with 2,4-D (0.5 mg/mL) and 2% sucrose. The image is a photograph taken by a scanner of the bottom of the culture flask after six weeks of incubation (bar = 1.2 cm).

#### *3.2. Influence of LED Lights on Morphogenesis of Embryogenic Calli (EC) of F. cirrhosa 3.2. Influence of LED Lights on Morphogenesis of Embryogenic Calli (EC) of F. cirrhosa*

The eight LED lights had significant effects on the growth and development of embryogenic calli (EC) of *F. cirrhosa* (Table 2). Although the development of somatic embryos (SEs) in embryogenic calli were recorded under all the light treatments, the maximum number of SEs was recorded under red (9R, 223.7), infrared (9IR, 231.3), and a combination of red+blue+infrared light (3R3B3IR, 230.7), respectively. Among the red, infrared, and a combination of red+blue+infrared LED lights, there was a low significant difference concerning the number of SEs and the number of SEs with cotyledonary leaves (Table 2). LED lights also influenced the number of embryos with the development of cotyledonary leaves. Calli exposed to white light (WW-2700K) developed a maximum number of SEs with cotyledonary leaves (22.3), followed by 8R1B (16.7) and CW-5000K (12.0), respectively. SEs under LED lights 9B, 9R, 9IR, and 3R3B3IR developed a reduced number of cotyledonary leaves (in the range of 3.7 to 5.3). There was a low significant difference in the total fresh weight of EC among different LED light treatments. The maximum total fresh weights (FW) of EC biomass, i.e., 17.92 g and 16.67 g, were recorded with treatments of red (9R) and a combination of red+blue+infrared light (3R3B3IR), respectively. Eight LED lights also influenced the growth and development, and morphological features of somatic embryos. Embryogenic calli exposed to different LED lights developed somatic embryos in different developmental stages from early globular to mature SEs with cotyledonary leaves (Figure 5a–i). However, the average number of SEs varied depending upon the LED light. LED light WW-2700K developed the maximum number of SEs with cotyledonary leaves (22.3), followed by 8R1B (16.7) (Table 2). Different LED lights also affected the overall color of the embryogenic calli mass (Figure 5a–h). Under red (9R) and infrared (9IR) treatments, cultures turned white (Figure 5f,g), while under red+blue+infrared (3R3B3IR), these were dark green (Figure 5h). The eight LED lights had significant effects on the growth and development of embryogenic calli (EC) of *F. cirrhosa* (Table 2). Although the development of somatic embryos (SEs) in embryogenic calli were recorded under all the light treatments, the maximum number of SEs was recorded under red (9R, 223.7), infrared (9IR, 231.3), and a combination of red+blue+infrared light (3R3B3IR, 230.7), respectively. Among the red, infrared, and a combination of red+blue+infrared LED lights, there was a low significant difference concerning the number of SEs and the number of SEs with cotyledonary leaves (Table 2). LED lights also influenced the number of embryos with the development of cotyledonary leaves. Calli exposed to white light (WW-2700K) developed a maximum number of SEs with cotyledonary leaves (22.3), followed by 8R1B (16.7) and CW-5000K (12.0), respectively. SEs under LED lights 9B, 9R, 9IR, and 3R3B3IR developed a reduced number of cotyledonary leaves (in the range of 3.7 to 5.3). There was a low significant difference in the total fresh weight of EC among different LED light treatments. The maximum total fresh weights (FW) of EC biomass, i.e., 17.92 g and 16.67 g, were recorded with treatments of red (9R) and a combination of red+blue+infrared light (3R3B3IR), respectively. Eight LED lights also influenced the growth and development, and morphological features of somatic embryos. Embryogenic calli exposed to different LED lights developed somatic embryos in different developmental stages from early globular to mature SEs with cotyledonary leaves (Figure 5a–i). However, the average number of SEs varied depending upon the LED light. LED light WW-2700K developed the maximum number of SEs with cotyledonary leaves (22.3), followed by 8R1B (16.7) (Table 2). Different LED lights also affected the overall color of the embryogenic calli mass (Figure 5a–h). Under red (9R) and infrared (9IR) treatments, cultures turned white (Figure 5f,g), while under red+blue+infrared (3R3B3IR), these were dark green (Figure 5h).

Light is one of the essential components required by plants for photosynthesis. However, its quantity and duration (photoperiod) drastically affect plant growth and development [29]. Fluorescent tubes are the most common lighting source in a culture room in a typical tissue culture set up. However, in the recent past, due to several advantages, more advanced light-emitting diodes (LEDs) have been used as a source of light. LEDs are relatively cool, emit light of specific wavelengths (spectra), are much smaller in size, and are more durable compared to conventional ones [30]. Due to their efficiency in growth and development, LED lights have been used for micropropagation of many horticultural and agricultural crops [31–33]. Depending on requirements, different types of growth chambers equipped with LED lights can be designed. Since the supply of specific light spectra can be controlled in plant tissue culture systems via LED lights, the effects of individual or combinations of light spectra on plant growth and development can be investigated appropriately as in the present study. Several studies on the influence of LED lighting on plant growth, physiology, and

secondary metabolism have been reviewed [11,34]. Recently, Pedmale and co-workers reported that the quality of light affects plant growth and development by the regulation of different mechanisms, including the selective activation of light receptors, such as phytochromes by red and far-red light, cryptochromes and phototropins by blue light, and UV-B receptors by ultraviolet light [35]. In a previous study in our laboratory, LED lights affected the development of somatic embryos and callus proliferation (fresh weight) in *Peucedanum japonicum* Thunb [22]. Several studies have demonstrated that the quality, duration, and intensity of red, infrared, blue, and ultraviolet light can have a profound influence on plants by activating or deactivating physiological reactions and controlling their growth and development [36–38]. These studies confirm with many other reports that LED lights are more efficient in plant growth compared to fluorescent lamps. Similar to our results, the beneficial effects of some LED light sources on the induction of embryogenesis in *Oncidium* have been reported [39]. Due to these beneficial effects, LED light systems are being increasingly used to boost the horticulture industry in Taiwan and several other countries [40–42]. *Plants* **2020**, *9*, x FOR PEER REVIEW 10 of 15

**Figure 5.** Influence of different LED lights on the growth and development of embryogenic calli of *F. cirrhosa* D. Don: (**a**) CW-5000K; (**b**) WW-2700K; (**c**) 7R1G1B; (**d**) 8R1B; (**e**) 9B; (**f**) 9R; (**g**) 9IR; (**h**) 3R3B3IR; (**i**) Different stages of somatic embryos. For microphotographs, cultures are taken out of the bottles and transferred to sterilized Petri dishes. g: globular embryos; m: mature embryos; c: cotyledonary leaf; ec: embryogenic callus. (a–f, h, bar = 0.57 cm; g, bar = 1.4 cm). Culture medium is Murashige and Skoog's basal medium supplemented with 2% sucrose and 0.4% GPP. Culture vessels are 650 mL glass bottles, each containing 100 mL medium. Observations recorded after three months of incubation in a specially designed LED light growth chamber. **Figure 5.** Influence of different LED lights on the growth and development of embryogenic calli of *F. cirrhosa* D. Don: (**a**) CW-5000K; (**b**) WW-2700K; (**c**) 7R1G1B; (**d**) 8R1B; (**e**) 9B; (**f**) 9R; (**g**) 9IR; (**h**) 3R3B3IR; (**i**) Different stages of somatic embryos. For microphotographs, cultures are taken out of the bottles and transferred to sterilized Petri dishes. g: globular embryos; m: mature embryos; c: cotyledonary leaf; ec: embryogenic callus. (a–f, h, bar = 0.57 cm; g, bar = 1.4 cm). Culture medium is Murashige and Skoog's basal medium supplemented with 2% sucrose and 0.4% GPP. Culture vessels are 650 mL glass bottles, each containing 100 mL medium. Observations recorded after three months of incubation in a specially designed LED light growth chamber.

their efficiency in growth and development, LED lights have been used for micropropagation of many horticultural and agricultural crops [31–33]. Depending on requirements, different types of growth chambers equipped with LED lights can be designed. Since the supply of specific light spectra can be controlled in plant tissue culture systems via LED lights, the effects of individual or combinations of light spectra on plant growth and development can be investigated appropriately as in the present study. Several studies on the influence of LED lighting on plant growth, physiology, and secondary metabolism have been reviewed [11,34]. Recently, Pedmale and co-workers reported that the quality of light affects plant growth and development by the regulation of different mechanisms, including the selective activation of light receptors, such as phytochromes by red and

Light is one of the essential components required by plants for photosynthesis. However, its quantity and duration (photoperiod) drastically affect plant growth and development [29]. Fluorescent tubes are the most common lighting source in a culture room in a typical tissue culture


**1. Introduction**

Differential systems, or more generally functional differential systems, have been studied rather extensively for at least 200 years and are used as models to describe transportation systems, communication networks, teleportation systems, physical systems and biological systems, and so forth. Parts of fractional-order systems have not received much attention by reason of absence of appropriate utilization circumstances over the past 300 years. However, during the last 10 years fractional-order systems have been widely investigated as they have the qualification to explain various phenomena more precisely in many fields, for example, biological models, material science, finance, cardiac tissues, quantum mechanics, viscoelastic systems, medicine and fluid mechanics [1–8]. Caputo fractional differential systems have been studied in many types of stability such as uniform stability [9], Mittag-Leffler stability [10–13], Ulam stability [14], finite time stability [15,16] and asymptotic stability [17,18]. Nevertheless, the stability of Riemann-Liouville fractional differential systems is seldom considered, see References [19,20].

The neutral systems with time delays have already been applied in many fields, such as heartbeat, memorization, locomotion, mastication and respiration, see References [21–24]. Accordingly, the issue of stability analysis for differential and Riemann-Liouville fractional differential neutral systems has

attracted researchers. The asymptotic stability criteria for certain neutral differential equations (CNDE) with constant delays have been discussed in References [25–29] by applying Lyapunov-Krasovskii functional and several model transformations. In References [30–33], the researchers considered the exponential stability problem for CNDE with time-varying delays by several methods. In Reference [30], the results were established without the use of the bounding technique and the model transformation method, while researchers have studied it by using radially unboundedness, the Lyapunov-Krasovskii functional approach and the model transformation method in Reference [32]. Moreover, in Reference [34] Li et al. presented the asymptotic stability conditions for fractional neutral systems in the form of matrix measure and matrix norm of the system matrices. However, the criteria, drafted in the form of matrix norm, are more conservative, while Liu et al. used the Lyapunov direct method to establish the asymptotic stability criteria of Riemann-Liouville fractional neutral systems in the form of LMIs [35].

This paper is involved with the analysis problem for the asymptotic stability of differential and Riemann-Liouville fractional differential neutral systems with constant delays and nonlinear perturbation by applying a zero equation, model transformation and other inequalities. The novel asymptotic stability condition is instituted in the form of LMIs. Then we show the new delay-dependent asymptotic stability criterion of differential and Riemann-Liouville fractional differential neutral systems with constant delays. In addition, the improved delay-dependent asymptotic stability criterion of differential and Riemann-Liouville fractional differential neutral systems with single constant delay and the new delay-dependent asymptotic stability criterion of differential and Riemann-Liouville fractional differential neutral equations with constant delays are established. Numerical examples represent the capability of our results as compared with other research.

#### **2. Problem Formulation and Preliminaries**

We introduce a differential and fractional differential neutral system with constant delays and nonlinear perturbation

$$\begin{aligned} \mathbf{x}\_{t\_0} D\_t^q [\mathbf{x}(t) + \mathbb{C}\mathbf{x}(t-\tau)] &= -A\mathbf{x}(t) + B\mathbf{x}(t-\sigma) + f(\mathbf{x}(t-\sigma)), \quad t > 0, \\ \mathbf{x}(t) &= \mathbf{q}(t), \quad t \in [-\kappa, 0], \end{aligned} \tag{1}$$

for 0 < *q* ≤ 1, the state vector *x*(*t*) ∈ R*n*, *A*, *B*, *C* are symmetric positive definite matrices with *C* < 1, *τ*, *σ* are positive real constants and ∈ *C*([ −*κ*, 0]; R*n*) with *κ* = max{*τ*, *σ*}.

The uncertainty *f*(.) represents the nonlinear parameter perturbation satisfying

$$f^T(\mathbf{x}(t))f(\mathbf{x}(t)) \quad \le \quad \delta^2 \mathbf{x}^T(t)\mathbf{x}(t),\tag{2}$$

$$f^T(\mathbf{x}(t-\sigma))f(\mathbf{x}(t-\sigma)) \quad \le \quad \eta^2 \mathbf{x}^T(t-\sigma)\mathbf{x}(t-\sigma),\tag{3}$$

where *δ*, *η* are given constants.

> Next, the Riemann-Liouville fractional integral and derivative [36] are defined as, respectively

$$\mathbf{x}\_{t0} D\_t^{-q} \mathbf{x}(t) \quad = \quad \frac{1}{\Gamma(q)} \int\_{t\_0}^t (t - s)^{q - 1} \mathbf{x}(s) ds, \quad (q > 0), \tag{4}$$

$$\,\_{t0}D\_t^q \mathbf{x}(t) \quad = \,\_1\frac{1}{\Gamma(n-q)} \frac{d^n}{dt^n} \int\_{t0}^t \frac{\mathbf{x}(s)}{(t-s)^{q+1-n}} ds, \quad (n-1 \le q < n). \tag{5}$$

**Lemma 1.** *[37] For x*(*t*) ∈ R*n and p* > *q* > 0*, then*

$$D\_{t\_0} D\_t^q (\_{t\_0} D\_t^{-p} \mathbf{x}(t)) = {}\_{t\_0} D\_t^{q-p} \mathbf{x}(t). \tag{6}$$

**Lemma 2.** *[17] For a vector of differentiable function x*(*t*) ∈ R*n, positive semi-definite matrix K* ∈ R*n*×*n and* 0 < *q* < 1*, then*

$$\frac{1}{2} \mathbf{1}\_{t\_0} D\_t^q(\mathbf{x}^T(t) \mathbf{K} \mathbf{x}(t)) \le \mathbf{x}^T(t) \mathbf{K}\_0 D\_t^q \mathbf{x}(t),\tag{7}$$

*for all t* ≥ *t*0*.*

#### **3. Main Results**

Consider the asymptotic stability for system (1) with constant delays and nonlinear perturbation. We define a new variable

$$
\Psi(t) = \mathbf{x}(t) + \mathbf{C}\mathbf{x}(t-\tau). \tag{8}
$$

Rewrite the Equation (1) in the following equation

$$D\_t D\_t^q \Psi(t) = -A\mathbf{x}(t) + B\mathbf{x}(t-\sigma) + f(\mathbf{x}(t-\sigma)).\tag{9}$$

**Theorem 1.** *Let δ and η be positive scalars, if there are any appropriate dimensions matrices Qj*(*j* = 1, 2, 3) *and symmetric positive definite matrices Ki*(*i* = 1, 2, 3, 4, 5) *such that satisfy*

$$
\sum = \begin{bmatrix}
\* & \Omega\_{(2,2)} & Q\_2 \mathbb{C} + Q\_3^T & 0 & 0 \\
\* & \* & \Omega\_{(3,3)} & 0 & 0 \\
\* & \* & \* & -K\_5 - \sigma I & 0 \\
\* & \* & \* & \* & -K\_3 + \sigma \eta^2 I
\end{bmatrix} < 0,\tag{10}
$$

*where*

$$\begin{array}{l} \Omega\_{(1,2)} = -K\_1 A + Q\_1 - Q\_2^T, \\ \Omega\_{(2,2)} = Q\_2 + Q\_2^T + K\_2 + K\_3 + \tau K\_4 + \delta^2 K\_5, \\ \Omega\_{(3,3)} = Q\_3 \mathcal{C} + \mathcal{C}^T Q\_3^T - K\_2. \end{array}$$

*Then the system* (1) *is asymptotically stable.*

**Proof of Theorem 1.** For symmetric positive definite matrices *Ki*(*i* = 1, 2, 3, 4, 5) and any appropriate dimensions matrices *Qj*(*j* = 1, 2, 3). Consider the Lyapunov-Krasovskii functional

$$V(t) = \sum\_{i=1}^{2} V\_i(t),\tag{11}$$

for

$$\begin{aligned} V\_1(t) &= \int\_{t\_0}^t \boldsymbol{D}\_t^{q-1} \boldsymbol{\Psi}^T(t) \boldsymbol{K}\_1 \boldsymbol{\Psi}(t) \\ V\_2(t) &= \int\_{t-\tau}^t \boldsymbol{\varkappa}^T(s) \boldsymbol{K}\_2 \boldsymbol{\varkappa}(s) ds + \int\_{t-\sigma}^t \boldsymbol{\varkappa}^T(s) \boldsymbol{K}\_3 \boldsymbol{\varkappa}(s) ds \\ &+ \int\_{t-\tau}^t (\tau - t + s) \boldsymbol{\varkappa}^T(s) \boldsymbol{K}\_4 \boldsymbol{\varkappa}(s) ds \\ &+ \int\_{t-\sigma}^t f^T(\boldsymbol{\varkappa}(s)) \boldsymbol{K}\_5 f(\boldsymbol{\varkappa}(s)) ds. \end{aligned}$$

Computing the differential of *V*(*t*) on the solution of system (1) *Mathematics* **2020**, *8*, 82

$$
\dot{V}(t) = \sum\_{i=1}^{2} \dot{V}\_i(t). \tag{12}
$$

The differential of *V*1(*t*) is computed by Lemma 2

$$\begin{array}{rcl}\dot{V}\_{1}(t) &=& \, \_{t\,0}D\_{t}^{f}\mathbf{Y}^{T}(t)K\_{1}\mathbf{Y}(t) \\ &\leq& 2\mathbf{Y}^{T}(t)K\_{1}(\,\_{t}D\_{t}^{f}\mathbf{Y}(t)) \\ &=& 2\mathbf{Y}^{T}(t)K\_{1}[-Ax(t)+Bx(t-\sigma)+f(\mathbf{x}(t-\sigma))] \\ &+& 2\mathbf{Y}^{T}(t)Q\_{1}[-\mathbf{Y}(t)+\mathbf{x}(t)+\mathbf{Cx}(t-\tau)] \\ &+& 2\mathbf{x}^{T}(t)Q\_{2}[-\mathbf{Y}(t)+\mathbf{x}(t)+\mathbf{Cx}(t-\tau)] \\ &+& 2\mathbf{x}^{T}(t-\tau)Q\_{3}[-\mathbf{Y}(t)+\mathbf{x}(t)+\mathbf{Cx}(t-\tau)]. \end{array} \tag{13}$$

Taking the differential of *V*2(*t*), we obtain

$$\begin{array}{rcl}\mathcal{W}\_{2}(t) &=& \mathbf{x}^{T}(t)\mathbf{K}\_{2}\mathbf{x}(t) - \mathbf{x}^{T}(t-\tau)\mathbf{K}\_{2}\mathbf{x}(t-\tau) \\ &+ \mathbf{x}^{T}(t)\mathbf{K}\_{3}\mathbf{x}(t) - \mathbf{x}^{T}(t-\sigma)\mathbf{K}\_{3}\mathbf{x}(t-\sigma) \\ &+ \tau \mathbf{x}^{T}(t)\mathbf{K}\_{4}\mathbf{x}(t) - \int\_{t-\tau}^{t} \mathbf{x}^{T}(\mathbf{s})\mathbf{K}\_{4}\mathbf{x}(s) \\ &+ f^{T}(\mathbf{x}(t))\mathbf{K}\_{5}f(\mathbf{x}(t)) - f^{T}(\mathbf{x}(t-\sigma))\mathbf{K}\_{5}f(\mathbf{x}(t-\sigma)) \\ &\leq & \mathbf{x}^{T}(t)\mathbf{K}\_{2}\mathbf{x}(t) - \mathbf{x}^{T}(t-\tau)\mathbf{K}\_{2}\mathbf{x}(t-\tau) \\ &+ \mathbf{x}^{T}(t)\mathbf{K}\_{3}\mathbf{x}(t) - \mathbf{x}^{T}(t-\sigma)\mathbf{K}\_{3}\mathbf{x}(t-\sigma) \\ &+ \tau\mathbf{x}^{T}(t)\mathbf{K}\_{4}\mathbf{x}(t) + \delta^{2}\mathbf{x}^{T}(t)\mathbf{K}\_{5}\mathbf{x}(t) \\ &- f^{T}(\mathbf{x}(t-\sigma))\mathbf{K}\_{5}f(\mathbf{x}(t-\sigma)). \end{array} \tag{14}$$

Next, from (3), we obtain

$$0 \le \sigma \eta^2 \mathbf{x}^T (t - \sigma) \mathbf{x} (t - \sigma) - \sigma f^T (\mathbf{x}(t - \sigma)) f(\mathbf{x}(t - \sigma)). \tag{15}$$

According to (13), (14) and (15), we can conclude that

$$
\dot{V}(t) \le \tilde{\xi}^T(t) \sum \tilde{\xi}(t), \tag{16}
$$

where *ξ*(*t*) = *col*{Ψ(*t*), *x*(*t*), *x*(*t* − *τ*), *f*(*x*(*t* − *σ*)), *xT*(*t* − *σ*)}.

Since linear matrix inequality (10) holds, then the system (1) is asymptotic stability.

Next, we consider system (1) with *f*(*x*(*t* − *σ*)) = 0,

$$\begin{aligned} \left[\mathbf{x}\_{0}D\_{t}^{q}\right]\mathbf{x}(t) + \mathbf{C}\mathbf{x}(t-\tau)\mathbf{j} &=& -A\mathbf{x}(t) + B\mathbf{x}(t-\sigma) \quad t>0, \\ \mathbf{x}(t) &=& \mathbf{q}(t), \quad t \in [-\kappa, 0], \end{aligned} \tag{17}$$

for 0 < *q* ≤ 1, the state vector *x*(*t*) ∈ R*n*, *A*, *B*, *C* are symmetric positive definite matrices with *C* < 1, *τ*, *σ* are positive real constants and ∈ *C*([−*κ*, 0]; R*n*) with *κ* = max{*τ*, *σ*}.

**Corollary 1.** *If there are any appropriate dimensions matrices Qj*(*j* = 1, 2, 3) *and symmetric positive definite matrices Ki*(*i* = 1, 2, 3, 4) *such that satisfy*

$$
\begin{bmatrix}
\* & Q\_2 + Q\_2^T + K\_2 + K\_3 + \tau K\_4 & Q\_2\mathbb{C} + Q\_3^T & 0 \\
\* & \* & Q\_3\mathbb{C} + \mathbb{C}^T Q\_3^T - K\_2 & 0 \\
\* & \* & \* & -K\_3
\end{bmatrix} < 0. \tag{18}
$$

*Then the system* (17) *is asymptotically stable.*

**Proof of Corollary 1.** For symmetric positive definite matrices *Ki*(*i* = 1, 2, 3, 4) and any appropriate dimensions matrices *Qj*(*j* = 1, 2, 3). Consider the Lyapunov-Krasovskii functional

$$V(t) = \sum\_{i=1}^{2} V\_i(t),\tag{19}$$

for

$$\begin{aligned} V\_1(t) &= \int\_{t\_0}^t D\_t^{q-1} \Psi^T(t) K\_1 \Psi(t), \\ V\_2(t) &= \int\_{t-\tau}^t \mathbf{x}^T(s) K\_2 \mathbf{x}(s) ds + \int\_{t-\sigma}^t \mathbf{x}^T(s) K\_3 \mathbf{x}(s) ds, \\ &\quad + \int\_{t-\tau}^t (\tau - t + s) \mathbf{x}^T(s) K\_4 \mathbf{x}(s) ds. \end{aligned}$$

According to Theorem 1, we present the asymptotic stability criterion (18) of system (17).

Next, we consider system (1) with *f*(*x*(*t* − *σ*)) = 0 and *σ* = *τ*,

$$\begin{aligned} \, \_{t \, \_0} D\_t^q [\mathbf{x}(t) + \mathbb{C}\mathbf{x}(t-\tau)] &= \, \_- - A\mathbf{x}(t) + B\mathbf{x}(t-\tau) \quad t > 0, \\ \mathbf{x}(t) &= \, \_0 \mathbf{q}(t), \quad t \in [-\tau, 0]. \end{aligned} \tag{20}$$

for 0 < *q* ≤ 1, the state vector *x*(*t*) ∈ R*n*, *A*, *B*, *C* are symmetric positive definite matrices with *C* < 1, *τ* is positive real constants and ∈ *C*([−*τ*, 0]; R*n*).

**Corollary 2.** *If there are any appropriate dimensions matrices Qj*(*j* = 1, 2, 3) *and symmetric positive definite matrices Ki*(*i* = 1, 2, 3) *such that satisfy*

$$
\begin{bmatrix}
\* & Q\_2 + Q\_2^T + K\_2 + \tau K\_3 & Q\_2C + Q\_3^T \\
\* & \* & Q\_3C + C^T Q\_3^T - K\_2
\end{bmatrix} < 0. \tag{21}
$$

*Then the Equation* (20) *is asymptotically stable.*

**Proof of Corollary 2.** For symmetric positive definite matrices *Ki*(*i* = 1, 2, 3) and any appropriate dimensions matrices *Qj*(*j* = 1, 2, 3). Consider the Lyapunov-Krasovskii functional

$$V(t) = \sum\_{i=1}^{2} V\_i(t)\_\prime \tag{22}$$

for

$$\begin{array}{rcl} \mathcal{V}\_{1}(t) &=& \int\_{t\_{0}} D\_{t}^{q-1} \Psi^{T}(t) \mathcal{K}\_{1} \Psi(t), \\ \mathcal{V}\_{2}(t) &=& \int\_{t-\tau}^{t} \mathbf{x}^{T}(s) \mathcal{K}\_{2} \mathbf{x}(s) ds \\ &+& \int\_{t-\tau}^{t} (\tau - t + s) \mathbf{x}^{T}(s) \mathcal{K}\_{3} \mathbf{x}(s) ds. \end{array} \tag{23}$$

According to Theorem 1, we present the asymptotic stability criterion (21) of system (20).