*4.1. Water Quality Index*

The water quality index is represented in Table 1. Water quality classification based on WQI value. If the WQI value is between 0 and 25, it can be considered excellent, and it is good if it falls between 26 and 50. Some other ranges, including 51–75 (poor), 76–100 (very poor), and >100 (Unfit for consumption). The calculated WQI of this area is 900, and it is way beyond the recommended value.

The descriptive statistics presented in Table 2 reflect different mean values for each variable in this dataset. The cadmium concentration ranges from 0.17 to 0.183 ppm with a mean of 0.18, chromium concentration from 0.00048 to 0.023 ppm with a mean of 0.014 ppm, copper concentration from 0.000873 ppm to 0.004 ppm with a mean value of 0.001 ppm, potassium concentration observed to be from 2.704 to 17.202 ppm, with a mean value of 8.964 ppm. The mean value of the manganese concentration was found to be 0.0023 ppm. The Ni concentration ranges from 0.00049 to 0.0044 ppm with a mean value of 0.001 ppm. The Pb concentration ranges from 0.289 to 0.490 ppm with an observed mean value of 0.412 ppm, and the Zn concentration ranges from 0.000357 to 0.051 ppm with a mean value of 0.003 ppm. The mean concentration of Ba and Al is 0.166 and 0.990 ppm, respectively.

Hence, correlation analysis is presented in Figure 3. The correlation analysis was done to investigate the relationships between the parameters measured. *Water* **2022**, *14*, x FOR PEER REVIEW 8 of 20

### **Figure 3.** Correlation table. **Figure 3.** Correlation table.

Cadmium is negatively correlated with Cr, K, Pb, TDS, and EC and positively correlated with Mn, Zn, As, and pH. This shows that the heavy metals with pH other than Cd are affecting the Cd concentrations and this can be due to anthropogenic causes. Cr is positively correlated with Cu, K, Ba, Temp, and EC and negatively correlated with Mn, As, NO3, and HCO3. This shows that the increase in Cr concentration is associated with the other heavy metals (Antropogenic) along with the EC and temperature (Natural Process). Cu is positively correlated with Zn, Ba, NO3, Ni (Anthropogenic), EC, HCO3, and Temp. K exhibits a positive correlation with Mn, Ni, Pb, Al, As, TDS, NO3, SO4, and EC and a negative correlation with Zn, Ba, Temp, and pH. Mn positively correlates with Al and As and negatively correlates with Ba and Temp. Ni is positively correlated with Pb, The degree to which two variables are allied is weighed by a correlation coefficient, indicated by the letter r. This coefficient, named after its discoverer, Pearson, measures linear association used in statistics and education. It is necessary to utilize alternative, more though measures of the correlation if a curved line is required to describe the relationship. The correlation coefficient can be measured from + 1 to −1 and is expressed as a percentage. The degree of complete correlation between two variables is represented by the numbers + 1 or −1, respectively. Correlations are positive when one variable increases in response to another's increase and negative when one variable reduces in response to the other increases. The number zero represents the complete absence of association.

Al, As, TDS, NO3, SO4, and EC and negatively correlated with Zn and pH. Pb is positively correlated with Al, As, TDS, NO3, SO4, and EC and negatively correlated with Zn, Ba, Temp, and pH. Zn is positively correlated with Al and pH and negatively correlated with Temp and EC. Ba is positively correlated with Temp, pH, and EC and negatively correlated with Al, As, TDS, and HCO3. Al is positively correlated with As, TDS, NO3, SO4, and EC and negatively correlated with Temp and pH. As is positively correlated with TDS, NO3, SO4, HCO3, and EC and negatively correlated with Temp and pH. TDS is positively correlated with NO3, SO4, and EC and negatively correlated with HCO3, Temp, and pH. NO3 is positively correlated with SO4, HCO3, and EC and negatively correlated with Temp and pH. SO4 exhibits a negative correlation with HCO3, Temp, and pH and positively correlates with EC. HCO3 is negatively correlated with pH and EC. The observed cadmium value is 0.18 ppm in the study area. This water might not suit agriculture, irrigation, and drinking purposes. The increase in cadmium levels can be due to sewage sludge, fertilizers, battery alloys, and cigarette smoking. The increased levels of Cd can damage kidneys. Due to this, there will be disruption of the endocrine system and inhibition of sex hormones in humans. Long-time exposure to Cadmium may cause Itaiitai in humans. The recommended chromium standard in drinking water is 0.1 ppm (EPA) and 0.05 ppm (WHO). The maximum value of chromium in the water sample is 0.023, which is well within the permissible limits. Cu (0.004), Mn (0.01), Zn (0.051), Ba (0.457), NO3 (2.4), SO4 (45), Temperature (32.6), pH (7.19), and bicarbonates are within the normal Cadmium is negatively correlated with Cr, K, Pb, TDS, and EC and positively correlated with Mn, Zn, As, and pH. This shows that the heavy metals with pH other than Cd are affecting the Cd concentrations and this can be due to anthropogenic causes. Cr is positively correlated with Cu, K, Ba, Temp, and EC and negatively correlated with Mn, As, NO3, and HCO3. This shows that the increase in Cr concentration is associated with the other heavy metals (Antropogenic) along with the EC and temperature (Natural Process). Cu is positively correlated with Zn, Ba, NO3, Ni (Anthropogenic), EC, HCO3, and Temp. K exhibits a positive correlation with Mn, Ni, Pb, Al, As, TDS, NO3, SO4, and EC and a negative correlation with Zn, Ba, Temp, and pH. Mn positively correlates with Al and As and negatively correlates with Ba and Temp. Ni is positively correlated with Pb, Al, As, TDS, NO3, SO4, and EC and negatively correlated with Zn and pH. Pb is positively correlated with Al, As, TDS, NO3, SO4, and EC and negatively correlated with Zn, Ba, Temp, and pH. Zn is positively correlated with Al and pH and negatively correlated with Temp and EC. Ba is positively correlated with Temp, pH, and EC and negatively correlated with Al, As, TDS, and HCO3. Al is positively correlated with As, TDS, NO3, SO4, and EC and negatively correlated with Temp and pH. As is positively correlated with TDS, NO3, SO4, HCO3, and EC and negatively correlated with Temp and pH. TDS is positively correlated with NO3, SO4, and EC and negatively correlated with HCO3, Temp, and pH. NO3 is positively correlated with SO4, HCO3, and EC and negatively correlated with Temp

and pH. SO4 exhibits a negative correlation with HCO3, Temp, and pH and positively correlates with EC. HCO3 is negatively correlated with pH and EC.

The observed cadmium value is 0.18 ppm in the study area. This water might not suit agriculture, irrigation, and drinking purposes. The increase in cadmium levels can be due to sewage sludge, fertilizers, battery alloys, and cigarette smoking. The increased levels of Cd can damage kidneys. Due to this, there will be disruption of the endocrine system and inhibition of sex hormones in humans. Long-time exposure to Cadmium may cause Itai-itai in humans. The recommended chromium standard in drinking water is 0.1 ppm (EPA) and 0.05 ppm (WHO). The maximum value of chromium in the water sample is 0.023, which is well within the permissible limits. Cu (0.004), Mn (0.01), Zn (0.051), Ba (0.457), NO3 (2.4), SO4 (45), Temperature (32.6), pH (7.19), and bicarbonates are within the normal range specified by WHO and US EPA. The EC values are at 3003, and it is problematic. Pb (0.49), As (0.028), and Al (1.45) are high in the water samples collected from the study area. The concentration of Cadmium in the observed samples is 0.184 (0.183984), and this is higher than the WHO recommended value of 0.003 mg/L. The concentration of lead observed in the groundwater samples is 0.4905 mg/L against 0.01 mg/L (WHO). The concentration of Aluminum in the samples analyzed is 1.4502 and is higher than the WHO recommended value of 0.9 mg/L.


*4.1.1 Wilkinson power divider (WPD)*

In the two-wayWPD, theoretically, the total input power is equally divided into two output ports. Assume that the input port is port 1 and the output ports are port 2 and 3, the transmission coefficient S21 = S31 = 3dB as shown in **Figure 18b**.

However, microstrip circuits on any substrate are affected by the loss tangent coefficient of the substrate and the microstrip discontinuities. The loss tangent represents the loss in the dielectric. The higher the loss tangent is, the more the loss is. For FR4 substrate, the loss tangent is about 0.025, so the forward gain of WPD on FR4 is less than theoretical one. Furthermore, the microstrip discontinuity phenomenon, as well as [22, 34, 35], appears in practice and generates parasitic

**Figure 18.** *The simulated 2-way WPD: (a) schematic circuit; (b) forward gains S21, S31.*

width *W* and length *L* are computed with the following Equation [15, 32, 33] where

r

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

ffiffiffiffiffiffiffiffiffiffi 2 *ε* þ 1

�

2

with *Leff* is the effective length, *ΔL* is the length adjustment, *εeff* is the effective

To validate the design of the antenna, every component in the phased array structure is evaluated and measured. All the simulations are performed on Keysight Technologies's Advanced Design System (ADS) software and CST Microwave Studio. All the components are fabricated on the FR4 substrate with dielectric constant ε of 4.3, loss tangent tanδ of 0.02, substrate thickness h of 62 mil (1.58 mm), and the

*W*

*h* þ 0*:*264 *W h* þ 0*:*8

¼ 3*:*9953

¼ 36*:*60 *mm* (30)

¼ 0*:*73 *mm*

*L* ¼ *Leff* � 2 � *ΔL* ¼ 28*:*07 *mm* (31)

*W* ¼ 3 � 1011 2 � *f*

f is center frequency (2.45 GHz):

*Block diagram of varactor diode controller.*

*Leff* ¼ 3 � 1011

*εeff* ¼ *ε* þ 1

2 � *f* � ffiffiffiffiffiffi

*ΔL* ¼ 0*:*412 � *h* � *εeff* þ 0*:*3

2 þ *ε* � 1 2

permittivity, and *h* is the substrate thickness.

conductive copper thickness t of 1.4 mil (1 oz).

*εeff*

**4. Evaluation of phased array antenna performance**

p ¼ 29*:*53 *mm*

*εeff* � 0*:*258

1 þ 12 *h W* � ��1

where

**116**

**Figure 17.**

8

>>>>>>>>>>>><

>>>>>>>>>>>>:

components, changing the parameters of circuit. Thus, in simulation, it is necessary to insert discontinuity components such as Bend and Tee so that the simulated results are close to measured results. After that, dimension of transmission lines is slightly adjusted by using optimization tool in ADS software in order to get the desired results. The two-way WPD is shown in **Figure 19** and S21 = S31 = 3.23 dB.

According to the cascade structure, the eight-way WPD is formed from the twoway WPDs and 50 Ω transmission lines. In addition, in microwave circuit, when transmission lines are placed closely to each other, the coupling between them occurs, which affects to characteristics of circuit. The coupling is an issue not easy to calculate and estimate and does not appear on schematic simulation, so the EM simulation and

Finally, the eight-way WPD is fabricated on FR4 substrate and then measured by PNA N5222A network analyzer. The forward gain for each port are shown in **Figure 21,** in which the thin red line, bold blue lines, and blue dash lines are forward gains of 8-way WPD in schematic simulation, EM co-simulation and fabrication, respectively. It can be seen that for schematic simulation, the forward gains in all output ports are identical and equal to 10.882 dB instead of 9 dB as in theory. The loss occurs during propagating on FR4 substrate with loss tangent coefficient tanδ = 0.025. For EM co-simulation, the forward gains at the output ports are not identical and change from 11.031 to 10.493 dB. As mentioned above, the difference comes from the coupling between transmission lines.

The forward gains of 8-way WPD made on FR4 material are relatively close to the simulation results in the schematic circuit and EM co-simulation at the designed frequency 2.45 GHz. The measurement results change around the simulation results with a difference less than 1 dB. The differences are due to the fabrication tolerance

Additionally, in **Figure 22,** the thin red line, thick blue line and dash blue line represent the schematic simulated result, electromagnetic co-simulation, and the measured result, respectively. The isolation between output ports of the power

For low loss variation, the important component – RTPS is designed on substrate

Roger4003C with dielectric constant ε of 3.55, loss tangent tanδ of 0.0021 and substrate thickness h of 0.8 mm. The RTPS structure is simulated on Advanced Design Systems ADS software. ADS has built-in components such as microstrip lines, terminals, DC voltage, and stubs; however, the selected SMV1247 varactors are not supported, hence it is necessary to create an equivalent model of SMV1247

before designing and simulating the RTPS. The C-V characteristic curve of SMV1247 with the ADS model at 100 MHz is shown in **Figure 23b**. This curve is

as well as the heterogeneity of the material in reality, causing the change of mismatch and loss compared with simulation. For the other frequency band, namely less than 2.4 GHz and greater than 2.5 GHz, there are large differences between simulation and reality. The reason is that the fabrication errors will make a great impact at undesired frequencies. However, this error does not affect the final

result because the system is designed to operate at 2.45 GHz.

divider is about 20 dB, similar to products on the market.

*4.1.2 Reflection type phase shifter*

**Figure 20.**

**119**

*Eight-way Wilkinson power divider.*

EM co-simulation are used to ensure the final simulated results (**Figure 20**).

*Beamforming Phased Array Antenna toward Indoor Positioning Applications*

*DOI: http://dx.doi.org/10.5772/intechopen.93133*

#### **Figure 19.**

*The 2-way WPD: (a) schematic circuit; (b) forward gains S21, S31*.

#### *Beamforming Phased Array Antenna toward Indoor Positioning Applications DOI: http://dx.doi.org/10.5772/intechopen.93133*

According to the cascade structure, the eight-way WPD is formed from the twoway WPDs and 50 Ω transmission lines. In addition, in microwave circuit, when transmission lines are placed closely to each other, the coupling between them occurs, which affects to characteristics of circuit. The coupling is an issue not easy to calculate and estimate and does not appear on schematic simulation, so the EM simulation and EM co-simulation are used to ensure the final simulated results (**Figure 20**).

Finally, the eight-way WPD is fabricated on FR4 substrate and then measured by PNA N5222A network analyzer. The forward gain for each port are shown in **Figure 21,** in which the thin red line, bold blue lines, and blue dash lines are forward gains of 8-way WPD in schematic simulation, EM co-simulation and fabrication, respectively. It can be seen that for schematic simulation, the forward gains in all output ports are identical and equal to 10.882 dB instead of 9 dB as in theory. The loss occurs during propagating on FR4 substrate with loss tangent coefficient tanδ = 0.025. For EM co-simulation, the forward gains at the output ports are not identical and change from 11.031 to 10.493 dB. As mentioned above, the difference comes from the coupling between transmission lines.

The forward gains of 8-way WPD made on FR4 material are relatively close to the simulation results in the schematic circuit and EM co-simulation at the designed frequency 2.45 GHz. The measurement results change around the simulation results with a difference less than 1 dB. The differences are due to the fabrication tolerance as well as the heterogeneity of the material in reality, causing the change of mismatch and loss compared with simulation. For the other frequency band, namely less than 2.4 GHz and greater than 2.5 GHz, there are large differences between simulation and reality. The reason is that the fabrication errors will make a great impact at undesired frequencies. However, this error does not affect the final result because the system is designed to operate at 2.45 GHz.

Additionally, in **Figure 22,** the thin red line, thick blue line and dash blue line represent the schematic simulated result, electromagnetic co-simulation, and the measured result, respectively. The isolation between output ports of the power divider is about 20 dB, similar to products on the market.

### *4.1.2 Reflection type phase shifter*

For low loss variation, the important component – RTPS is designed on substrate Roger4003C with dielectric constant ε of 3.55, loss tangent tanδ of 0.0021 and substrate thickness h of 0.8 mm. The RTPS structure is simulated on Advanced Design Systems ADS software. ADS has built-in components such as microstrip lines, terminals, DC voltage, and stubs; however, the selected SMV1247 varactors are not supported, hence it is necessary to create an equivalent model of SMV1247 before designing and simulating the RTPS. The C-V characteristic curve of SMV1247 with the ADS model at 100 MHz is shown in **Figure 23b**. This curve is

**Figure 20.** *Eight-way Wilkinson power divider.*

components, changing the parameters of circuit. Thus, in simulation, it is necessary to insert discontinuity components such as Bend and Tee so that the simulated results are close to measured results. After that, dimension of transmission lines is slightly adjusted by using optimization tool in ADS software in order to get the desired results. The two-way WPD is shown in **Figure 19** and S21 = S31 = 3.23 dB.

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

**Figure 19.**

**118**

*The 2-way WPD: (a) schematic circuit; (b) forward gains S21, S31*.

**Figure 21.** *Forward gain from port 2–9 of the WPD.*

similar to the characteristic curve in the SMV1247 datasheet **Figure 23a**, so this SMV1247 model will be used on all ADS simulations. These characteristic curves were calculated from model in **Figure 24**.

model of SMV1247 is built with the Ls, Cp, and Rs values corresponding to SC-79

The RTPS is deployed both in schematic simulation and EM co-simulation to ensure the real operation (**Figure 25**). The microstrip discontinuity components such as bend, tee, and steps in width are inserted and their dimensions are adjusted in order to obtain the desired results. Then the RTPS is fabricated on Roger4003c (**Figure 26**) and measured by the Keysight PNA N5222A Network Analyzer. The results of the phase shifter are shown in **Figure 27**, where the green dash line, blue dash dot line, and red circle line are schematic simulation, EM

packages, Ls = 0.7nH, Cp = 0.54 pF, and Rs = 4.9 Ω (**Figure 24b**).

*Beamforming Phased Array Antenna toward Indoor Positioning Applications*

*DOI: http://dx.doi.org/10.5772/intechopen.93133*

co-simulation, and measured results, respectively.

*C-V curve of SMV1247 on: (a) technical document [36]; (b) ADS.*

**Figure 22.**

**Figure 23.**

**121**

*Isolation between output ports.*

In the technical documentation of SMV1247, Skyworks provides the SPICE model (**Figure 24a**). After importing this model into ADS software, the equivalent *Beamforming Phased Array Antenna toward Indoor Positioning Applications DOI: http://dx.doi.org/10.5772/intechopen.93133*

**Figure 23.** *C-V curve of SMV1247 on: (a) technical document [36]; (b) ADS.*

model of SMV1247 is built with the Ls, Cp, and Rs values corresponding to SC-79 packages, Ls = 0.7nH, Cp = 0.54 pF, and Rs = 4.9 Ω (**Figure 24b**).

The RTPS is deployed both in schematic simulation and EM co-simulation to ensure the real operation (**Figure 25**). The microstrip discontinuity components such as bend, tee, and steps in width are inserted and their dimensions are adjusted in order to obtain the desired results. Then the RTPS is fabricated on Roger4003c (**Figure 26**) and measured by the Keysight PNA N5222A Network Analyzer.

The results of the phase shifter are shown in **Figure 27**, where the green dash line, blue dash dot line, and red circle line are schematic simulation, EM co-simulation, and measured results, respectively.

similar to the characteristic curve in the SMV1247 datasheet **Figure 23a**, so this SMV1247 model will be used on all ADS simulations. These characteristic curves

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

In the technical documentation of SMV1247, Skyworks provides the SPICE model (**Figure 24a**). After importing this model into ADS software, the equivalent

were calculated from model in **Figure 24**.

*Forward gain from port 2–9 of the WPD.*

**Figure 21.**

**120**

**Figure 24.** *Equivalent model of SMV1247: (a) on SPICE [36]; (b) on ADS.*

**Figure 27** shows the forward gains (S21) in all three cases simulating the schematic, electromagnetic field simulation, and measurement results. I find that the energy transmitted through the phase shifter is little less than that of the schematic and electromagnetic simulation. The reason may be due to the error in fabrication process as well as contact between the varactors and the microstrip line, which is

**Figure 25.**

**123**

*Schematic circuit of reflection type phase shifter on ADS.*

*Beamforming Phased Array Antenna toward Indoor Positioning Applications*

*DOI: http://dx.doi.org/10.5772/intechopen.93133*

*Beamforming Phased Array Antenna toward Indoor Positioning Applications DOI: http://dx.doi.org/10.5772/intechopen.93133*

**Figure 25.** *Schematic circuit of reflection type phase shifter on ADS.*

**Figure 27** shows the forward gains (S21) in all three cases simulating the schematic, electromagnetic field simulation, and measurement results. I find that the energy transmitted through the phase shifter is little less than that of the schematic and electromagnetic simulation. The reason may be due to the error in fabrication process as well as contact between the varactors and the microstrip line, which is

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

*Equivalent model of SMV1247: (a) on SPICE [36]; (b) on ADS.*

**Figure 24.**

**122**

**Figure 26.** *Reflection type phase shifter fabricated on Roger4003c.*

respectively. The VSWR and S11 of patch antenna versus frequency are shown in **Figure 29**, where the thin red lines and dash blue lines are simulated and measured characteristics, respectively. It can be seen that in simulation, my microstrip patch antenna can operate at Wi-Fi band with resonant frequency from 2.4 to 2.484 GHz. However, due to the heterogeneity of the material in practice as well as errors in fabrication, my microstrip patch antenna in reality can only operate from 2.424 to 2.485 GHz. Although this antenna cannot be used in whole range of Wi-Fi band, it

*Beamforming Phased Array Antenna toward Indoor Positioning Applications*

*DOI: http://dx.doi.org/10.5772/intechopen.93133*

can still operate at several channels of Wi-Fi.

*Microstrip patch antenna parameters: (a) VSWR; (b) return loss.*

**Figure 28.**

**Figure 29.**

**125**

*Fabricated microstrip patch antenna.*

**Figure 27.** *Transmission coefficient S21 of RTPS: (a) magnitude; (b) phase.*

not ideal and alter the resistance value in the equivalent circuit of the varactors and resulting in a loss of power that is much larger than simulation. The insertion loss is about 2.5 dB with nearly 0.5 dB of variation. The low insertion loss variation enables us to make a phased array antenna with uniform amplitude and spacing. **Figure 27b** shows phase shifts in three cases, the schematic, electromagnetic simulation, and measurement results. The phase shifters are capable of shifting about 370°. The phase shifter in reality although is not nearly as linear as the rest of the two cases, but with DAC12bits, the phase shifter can still be controlled with small resolution.

### *4.1.3 Microstrip patch antenna*

The fundamental part – microstrip patch antenna is simulated on CST Microwave Studio software and then fabricated on FR4 material, as shown **Figure 28**. In order to determine the bandwidth of an antenna, two parameters, VSWR and return loss S11, can be measured by the network analyzer PNA N5222A. At the operating frequency, the desired VSWR and S11 are less than 2 and 10 dB,

*Beamforming Phased Array Antenna toward Indoor Positioning Applications DOI: http://dx.doi.org/10.5772/intechopen.93133*

**Figure 28.** *Fabricated microstrip patch antenna.*

respectively. The VSWR and S11 of patch antenna versus frequency are shown in **Figure 29**, where the thin red lines and dash blue lines are simulated and measured characteristics, respectively. It can be seen that in simulation, my microstrip patch antenna can operate at Wi-Fi band with resonant frequency from 2.4 to 2.484 GHz. However, due to the heterogeneity of the material in practice as well as errors in fabrication, my microstrip patch antenna in reality can only operate from 2.424 to 2.485 GHz. Although this antenna cannot be used in whole range of Wi-Fi band, it can still operate at several channels of Wi-Fi.

**Figure 29.** *Microstrip patch antenna parameters: (a) VSWR; (b) return loss.*

not ideal and alter the resistance value in the equivalent circuit of the varactors and resulting in a loss of power that is much larger than simulation. The insertion loss is about 2.5 dB with nearly 0.5 dB of variation. The low insertion loss variation enables us to make a phased array antenna with uniform amplitude and spacing. **Figure 27b** shows phase shifts in three cases, the schematic, electromagnetic simulation, and measurement results. The phase shifters are capable of shifting about 370°. The phase shifter in reality although is not nearly as linear as the rest of the two cases, but with DAC12bits, the phase shifter can still be controlled with small

*Advanced Radio Frequency Antennas for Modern Communication and Medical Systems*

The fundamental part – microstrip patch antenna is simulated on CST Microwave Studio software and then fabricated on FR4 material, as shown **Figure 28**. In order to determine the bandwidth of an antenna, two parameters, VSWR and return loss S11, can be measured by the network analyzer PNA N5222A. At the operating frequency, the desired VSWR and S11 are less than 2 and 10 dB,

resolution.

**124**

**Figure 27.**

**Figure 26.**

*Reflection type phase shifter fabricated on Roger4003c.*

*Transmission coefficient S21 of RTPS: (a) magnitude; (b) phase.*

*4.1.3 Microstrip patch antenna*


**1. Introduction**

Malignancies of the thymus are rare accounting for <1% of all adult neoplasms. Thymoma and thymus carcinoma account for 20% of tumors in the anterior mediastinum and are therefore the most commonly found malignancy in that specific location.

There are several groups of thymic tumors to distinguish thymic epithelial tumors, germ cell tumors, lymphoid and hematological malignancies, and mesenchymal tumors. It is known that they derive from or differentiate toward thymic cellular components, but the etiology of thymic neoplasms is largely unknown [1].

This chapter is about the epithelial tumors thymoma and thymic carcinoma (which include the neuroendocrine tumors). These tumors are histologically characterized by thymic stroma and lymphocytes. Thymomas usually exhibit a slow growth pattern, but they do have malignant potential and show a propensity for local invasion and intrathoracic recurrence. Extrathoracic metastases of thymomas are quite rare [2]. Thymic carcinomas are more aggressive, with invasion in the surrounding mediastinal structures. Extrathoracic metastases occur in <7% of patients [3]. Although thymomas and thymic carcinomas arise mostly in the anterior

mediastinum, they can also be found in other mediastinal compartments, as well as in the neck, lung, pleura, and thyroid, due to ectopic thymic tissue [1, 4, 5].

Approximately 1000 new cases are diagnosed per year in Europe [6]. Patients are usually between 20 and 70 years of age, with a peak in 30–40 years for those with myasthenia gravis and 60–70 years for those without myasthenia gravis. Incidence is similar in men and women [3, 7]. There are no known risk factors [3, 7], although clusters of thymoma are described in patients with multiple endocrine neoplasia type 1 (MEN1) syndrome [8] and an association between the Epstein– Barr virus and myasthenia gravis in thymoma patients has been suggested [9].


*2.5. Bioinformatic Analysis*

Sequencing data were subjected to NonPareil 3 [23] for sequencing depth assessment and later processed with Qiime2 (version 2018.11) package [24]. The reads were imported into Qiime2 and run through the dada2 plugin to obtain amplicon sequence variants (ASV) [25]. Taxonomy was assigned for each of the ASVs using a pre-trained naive Bayes classifier, based on the Silva 132 99% database [26], which was trimmed to include only the V3 and V4 regions of the 16S rRNA gene, bound by the Bakt\_341F and Bakt\_805R primer sequences. Alfa and beta diversity metrics were generated using the following Qiime2 plugins: phylogeny (including mafft aligner and FastTree tool), diversity and emperor [27–29].


*2.2. Identification of Key Residues for the Enantioselectivity of OYE2y*

It was previously reported that the variant W116F of OYE1 from *S. pastorianus* partly reversed the enantioselectivity in the neral reduction [22]. However, the same substitution at site W117 corresponding to W116 in OYE1 even lowered the *e.e.* value from 38.13% (*R*) to 24.01% (*R*) when (*E*/*Z*)-citral was tested as substrate, indicating that the enantioselectivity switch for OYE1 and OYE2y seemed different. Furthermore, the NAD(P)H-dependent enoate reductase (OYE2p) from *S. cerevisiae* YJM1341 was newly discovered for asymmetric reduction of (*E*/*Z*)-citral to (*R*)-citronellal with the *e.e.* value of 88.8% (*R*), with four amino acid residues—G13, A59, I289, and H330—in OYE2p different from S13, S59, V289, and R330 in OYE2y (Figure 2). Considering the difference of enantioselectivity between OYE2p and OYE2y, it was expected that S13, S59, V289, and/or R330 might be critical for the enantioselectivity. Then, the single substitutions were conducted to evaluate this expectation. The catalytic performance of the variants S13G, S59A, and V289I was similar to that of OYE2y (Table 1). The substitution R330 to H significantly increased the (*R*)-enantioselectivity from 38.13% to 86.88% when (*E*/*Z*)-citral was used as substrate. Remarkably, the enantioselectivity was reversed from 32.66% (*S*) to 71.92% (*R*) when (*Z*)-citral was tested. Except for sequence alignment, the identification of key residues was conducted through the single mutation on the randomly-selected residues. Through multiple mutation attempts, the variant P76M was discovered to benefit the (*R*)-enantioselectivity of OYE2y in the citral reduction (Table 1). The substitution P76 to M increased the (*E*/*Z*) citral-derived *e.e.* value from 38.13% (*R*) to 57.60% (*R*), while the enantioselectivity in the reduction of (*Z*)-citral was lowered from 32.66% (*S*) to 4.76% (*S*). Thus, both R330 and P76 were chosen as the targets for subsequent site saturation mutagenesis.

**Figure 2.** Structure-related sequence alignment between OYE2y and its homologous OYEs. 1OYB: PDB code of OYE1 from *S. pastorianus*; OYE2p: NAD(P)H-dependent enoate reductase from *S. cerevisiae* YJM1341. The secondary structural elements of 1OYB (α-helices, β-strands, T-turns, and η-helices) were indicated above the aligned sequences. The numbering shown was from 1OYB. A red background highlights conserved residues. , the positions where the amino acid residues differed between OYE2y and OYE2p; , key residues for catalytic activity. The figure was produced using ESPript 3.0 [32].

a Data present mean values ± SD from three independent experiments. (*E*)-citral contained 98.38% geranial and 1.62% neral, (*Z*)-citral contained 96.84% neral and 3.16% geranial, and (*E*/*Z*)-citral contained 58.45% geranial and 41.55% neral.

#### *2.3. Site-Saturation Mutagenesis of R330 in OYE2y*

All R330X variants of OYE2y (X = one of the other 19 amino acids) were successfully expressed in *E. coli*. After the cells were harvested by centrifugation and then disrupted by ultrasonication, each variant with *N*-terminal His tag was purified using affinity chromatography. As shown in Figure S1, all 19 variant proteins remained in the soluble fraction, revealing that these substitutions did not decrease the solubility. In comparison with the wild type OYE2y, the R330 variants of OYE2y fell into three categories: R330P without catalytic activity, R330Y with similar (*R*)-stereoselectivity to OYE2y, and the other 17 variants with improved (*R*)-stereoselectivity (Table 2). R330P did not retain the yellow color, suggesting that its substitution might deactivate the coenzyme binding. When (*E*/*Z*)-citral was tested as substrate, the variants except R330Y and R330P increased the (*R*)-stereoselectivity but decreased the product yield to some extent. In contrast to the reduction of (*E*)-citral, those 17 variants showed more significant improvement of (*R*)-enantioselectivity in the reduction of (*Z*)-citral. Among them, the variants R330H, R330D, and R330W had superior catalytic performance in terms of activity and enantioselectivity.

**Table 2.** The catalytic performance of OYE2y and its R330X variants a.

a X represents any of 20 amino acids. Data present mean values ± SD from three independent experiments. (*E*)-citral contained 98.38% geranial and 1.62% neral, (*Z*)-citral contained 96.84% neral and 3.16% geranial, and (*E*/*Z*)-citral contained 58.45% geranial and 41.55% neral. b "/" represents no catalytic activity.


**4. Discussion**

According to the results of this study, the rooting of *Morus alba* L. cuttings induced by auxins is superior to that of the control group treated with water, and the ABT-1 treatment is the most effective, followed by IAA and IBA. Auxins can accelerate the initiation of root primordia and propel the formation of adventitious roots, causing a notable improvement in rooting [26–29], which aligns with the results of *Acacia mangium* et al. [30].

IAA can stimulate cell division in the cambium and regulate the formation of callus tissue [31]. In this study, IAA promoted the rooting rate of *Morus alba* L., and the rooting rate gradually decreased with an increase in IAA concentration within the study concentration range, which was contrary to the results of Li et al. [24] and similar to the results of Raju et al. [32].This phenomenon may be caused by the fact that IAA is easily destroyed by metabolism in plants, its action is highly unstable, it easily decomposes in water, and its action time is short [31]. Moreover, the stability of IAA decreases with an increase in concentration. IBA can accelerate cell metabolism and promote the induction and formation of root primordia [33]. According to Singh et al. [34], the rooting percentage, longest root sprout length, germination rate, and root length of *Morus alba* L. cuttings treated with 2000 mg·L−1 IBA are all higher. These findings bear similarity to the results of the vegetable mulberry treatment, but differ from those of the fruit mulberry treatment. As indicated by the notable difference in the effect of IBA concentration required by vegetable mulberry and fruit mulberry, the plants exhibit different sensitivities to the same type of auxin, affecting the physiological and biochemical processes inside the cuttings, and further causing differences in rooting efficiency, which is consistent with the results of Hu [35]. ABT-1 can accelerate the synthesis of hormones in the rooting region and promote branch proliferation [36]. As discovered by Chen et al. [21], ABT-1 is most effective in promoting the rooting of Morus alba L., followed by IBA and IAA. As indicated by the results of this study, the optimal treatment for rooting of vegetable mulberry cuttings is 1000 mg·L−1 ABT-1, and 500 mg·L−1 ABT-1 for rooting of fruit mulberry. The different optimal treatment concentrations for vegetable mulberry and fruit mulberry may be caused by the different sensitivities of different plants to exogenous auxins. Hence, for different plant cuttings, the optimal concentration range of auxins is also different [37]. The results of this study show that the effects of IAA and IBA treatment are inferior to that of ABT-1. This is probably caused by the fact that ABT-1 is a composite of IBA and NAA, which can more effectively propel the increase in endogenous hormone content and important enzyme activity and promote plant metabolism intensity, thereby greatly improving rooting

ability [38]. These findings are similar to the results of studies on *Chimonanthus praecox* [39], *Sophora japonica* [22] and *Taxus chinensis* (Pilger) Rehd F. [36].

According to most scholars, endogenous hormone expression and nutrient allocation in cuttings are regulated by exogenous hormones [40–42]. In this study, the changes in IAA content suggest that ABT-1 can increase endogenous IAA content and enhance adventitious root formation [43], similar to the results of Shang et al. [44]. This demonstrates that endogenous IAA can propel root primordium accumulation and adventitious root formation [31]. Endogenous IBA can drive root primordium formation and promote rooting [45]. As observed from the changes in IBA content in this study, IBA content remarkably increases under ABT-1 treatment, thereby enhancing adventitious root formation and elongation. ABA is an inhibitory plant hormone [46]. As discovered in various studies, the ABA content of vegetable and fruit mulberry cuttings increases, thereby regulating the adaptability of cuttings to stress and preparing them for the formation of root primordia, followed by decreases in endogenous ABA content, suggesting that low levels of ABA are conducive to root primordium differentiation and root formation [47]. In this study, changes in ABA content indicate that ABT-1 can induce a decrease in ABA content during the production of callus tissue, which is beneficial to the growth and development of adventitious roots. ZR can promote cell division and differentiation, thereby affecting adventitious root formation [22]. As shown by previous studies, low concentrations of ZR are conducive to adventitious root formation and differentiation [48,49]. In this study, the control group had a higher ZR content compared with the treatment group after the initiation period, indicating that ABT-1 has an inhibitory effect on ZR in the early rooting stage, thus promoting root primordia. Subsequently, the ZR content in the treatment group exhibited a slight increase before decreasing, suggesting that cuttings can synthesize ZR by themselves during the production and elongation of adventitious roots, thus causing an increase in ZR content. For the rooting process of vegetable and fruit mulberry cuttings, low ZR concentration facilitates the formation of callus tissue, and high ZR concentration contributes to the formation of adventitious roots, in accordance with the results of *Tilia mandshurica* [50], *Zizyphus jujuba* Mill. [51] and Hybrid Aspen [52]. GA3 mainly enhances cell division and elongation [53]. Studies have discovered that a high concentration of GA3 inhibits adventitious root formation, and that a low concentration of GA3 is conducive to adventitious root formation [54]. This was confirmed by the changes in GA3 content in this study, conforming to findings from the study of Mu et al. [50]. JA can promote plant regeneration [47]. In this study, the JA content reached its peak during the production of callus tissue, suggesting that high levels of JA can mediate the development of callus tissue and provide conditions for root primordium formation. Afterwards, the JA content gradually decreased, indicating the consumption of JA by adventitious root growth and development. Nevertheless, in comparison to the control group, the treatment group presented the higher JA content, indicating that ABT-1 can improve cell regeneration ability and stimulate callus tissue development, thereby enhancing adventitious root formation. As demonstrated in previous studies, SL can regulate root system morphology, and promote lateral roots and root hair production [55–57]. Furthermore, in this study, the optimal treatment group presented the highest SL content during the initiation period, and the SL content gradually stabilized after the production of callus tissue, suggesting that ABT-1 can propel a large amount of SL synthesis during the initiation period and provide conditions for adventitious root development.

During plant cuttings, the ratio of endogenous hormone levels bears close relation with the formation of adventitious roots [58]. According to Quan et al. [59], the higher the values of IAA/ABA and IAA/ZR are, the easier it is to form roots. As discovered in this study, the IAA/ABA and IAA/ZR values in the treatment group greatly increased after the formation of callus, indicating that *Morus alba* L. can be induced to root under the combined action of auxin and endogenous hormones.


**4. Toward a treatment?**

Ultimately, the primary goal of genome research should be to propose targets for interven‐ tion. As mentioned above, a number of translational studies have begun to probe the metab‐ otropic glutamate receptor, mGluR5, as a potential target for fragile X syndrome treatment. These studies have a theoretical basis in the hypothesis that protein-synthesis-dependent functions of metabotropic receptors are exaggerated in fragile X syndrome (Bear, Huber & Warren, 2004) [87]. Thus, the fragile X protein, FMRP, is thought to work in functional oppo‐ sition to mGluR5 (and mGluR1). Where FMRP is absent, mGluR-dependent protein synthe‐ sis becomes over-activated, resulting in neurological and behavioral abnormalities.

Dölen *et al*. (2007) [89] crossed *Fmr1* mutant with Grm5 mutant to produce *Fmr1* knockouts who also had a selective reduction in mGluR5 expression. They found that a 50% reduction in mGluR5 gene dosage rescued a range of deficits in *Fmr1* mutants. Relevant measures in‐ cluded protein synthesis in hippocampus, density of dendritic spines (layer 3 pyramidal neurons), visual responsiveness, and cognitive performance (inhibitory avoidance – a hippo‐ campus-dependent memory). This provides confirmation that mGluR5 and FMRP are func‐ tionally oppositional. Moreover, it suggests possible pharmacological avenues by which this genetic disease may be treated.

A range of translational studies have begun to target this pathway. These include efforts to inhibit the activity of individual mGluR5 (Jacquemont *et al*., 2011; Berry-Kravis *et al*., 2009) [88,90], and FMRP-regulated proteins (Paribello *et al*., 2010) [91], NMDA (Wei *et al*., 2012) [92], and GSK3β (lithium, Berry-Kravis *et al*., 2008) [93], which have shown promise in open label and (in some instances) clinical trials (see Berry-Kravis *et al*., 2011 for review) [94]. Moreover, these compounds may have clinical application to the broader ASD phenotype. Silverman *et al*. (2012) [95] recently reported that the mGluR5 antagonist, GRN-529, de‐ creased ASD-related symptoms of autism in two different mouse models of the disease (re‐ petitive grooming/repetitive jumping). In addition to the Iossifov *et al*. (2012) [85] sequencing study discussed above, Kelleher *et al*. (2012) [96] recently showed that idiopathic autism cases may have higher burden of mGluR5 variants. The group found that in 209 idio‐ pathic cases, there was significant enrichment for rare functional variants in the mGluR5 pathway—namely the genes *TSC1*, *TSC2* and *SHANK3*, and *HOMER1*—relative to controls (n=300). It is likely that drugs targeting the mGluR5 pathway, if/when approved for fragile X syndrome, will lead to human clinical trials for ASD. This translational approach – which delineates a direct route from gene discovery, through functional validation to treatment, is clearly the blueprint by which genome research can have tangible clinical impact.