**1. Introduction**

We consider two sources of the distortions of the light coming to the high-resolution Echelle spectrograph: the behavior of the telescope itself (its instability) and the aberrations caused by atmospheric turbulence [1]. A corrector to control for the position of the center of a star image has been developed and is being used at the entrance to the NES highresolution spectrograph [2]. The statistics of the deviations of the position of the image center of a star have been collected for various atmospheric parameters in the surface layer (temperature, wind speed, etc.). From the practical experience of observations and statistics, it turned out that the tilts (Zernike 1 and Zernike 2) of the input beam with a diameter of 6 m are characterized by a low-frequency spectrum, with maximum deviations in the position of a star depending on the image quality.

Quasi-periodic fluctuations of the position of a star on the spectrograph slit were detected during the first observations at the Nasmyth foci of the 6 m telescope. At the very beginning, we believed that those fluctuations were caused by the restless behavior of the astronomer, who was on the moving platform of the Nasmyth focus, but this was proved to be false. It was also found that the excessively frequent correction of the position of the star through the telescope's driving system could lead to slowly damped oscillations or even rock the telescope. The relationship between the nature of fluctuations and the speed and duration of a single act of correction was also revealed. The effect of slowly damped oscillations on the position of a star was also discovered as a result of short-term wind gusts (up to a speed of several m/s). At the minimum speed of the correction of the movement of the telescope, oscillations occurred, with an amplitude of up to 1", and the damping time of the oscillations was 25 s. As the correction speed increased, the effect became stronger. It was concluded that, to keep the center of the star image at the center of the spectrograph slit, it was optimal to use a local corrector, which would eliminate the need to correct the overall tilt of the telescope at high (for a telescope) frequencies. became stronger. It was concluded that, to keep the center of the star image at the center of the spectrograph slit, it was optimal to use a local corrector, which would eliminate the need to correct the overall tilt of the telescope at high (for a telescope) frequencies. It is already an established practice in astronomy to use an image-stabilization system

damping time of the oscillations was 25 s. As the correction speed increased, the effect

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It is already an established practice in astronomy to use an image-stabilization system [3–8]. For example, a fast-steering mirror was produced and designed to correct for the image stabilization of the astronomical telescopes [3]; this work describes the optimization process of mirror development and the system performance. Another approach, when one piezo-driven tip-tilt mirror was used to minimize the motions of the solar optical telescope images with a frequency above 14 Hz to improve polarimetric measurements, is described in [4]. A tip-tilt controller for the Solar Orbiter is discussed in [5]. A control algorithm was developed by the authors as well [6]. An image-stabilization system was described recently [7] by Chinese scientists. They presented both simulations and experiments on testing the accuracy of the image-stabilization system of a space telescope. The influence of the vibration isolation platform was also considered. Moreover, there are a number of works that are not related directly to astronomy but are of particular interest for beam stabilization [9,10]. [3–8]. For example, a fast-steering mirror was produced and designed to correct for the image stabilization of the astronomical telescopes [3]; this work describes the optimization process of mirror development and the system performance. Another approach, when one piezo-driven tip-tilt mirror was used to minimize the motions of the solar optical telescope images with a frequency above 14 Hz to improve polarimetric measurements, is described in [4]. A tip-tilt controller for the Solar Orbiter is discussed in [5]. A control algorithm was developed by the authors as well [6]. An image-stabilization system was described recently [7] by Chinese scientists. They presented both simulations and experiments on testing the accuracy of the image-stabilization system of a space telescope. The influence of the vibration isolation platform was also considered. Moreover, there are a number of works that are not related directly to astronomy but are of particular interest for beam stabilization [9,10].

This paper discusses a system for active laser beam stabilization based on an FPGA [11]. The system is a part of a closed-loop adaptive optical system to improve the efficiency of high-resolution spectroscopy on the 6 m telescope BTA [12]. The structure of the overall adaptive system for the telescope is presented in Figure 1. Each part is used for a certain purpose: some mirrors serve to compensate for large-scale slowly changing aberrations, while other mirrors compensate for small-scale aberrations that occur when passing through a turbulent atmosphere, and some mirrors are used to stabilize the position of the beam. We expect the whole adaptive system to decrease a star image on the spectrograph slit from 1 arcsec to 0.2 arcsec. This paper discusses a system for active laser beam stabilization based on an FPGA [11]. The system is a part of a closed-loop adaptive optical system to improve the efficiency of high-resolution spectroscopy on the 6 m telescope BTA [12]. The structure of the overall adaptive system for the telescope is presented in Figure 1. Each part is used for a certain purpose: some mirrors serve to compensate for large-scale slowly changing aberrations, while other mirrors compensate for small-scale aberrations that occur when passing through a turbulent atmosphere, and some mirrors are used to stabilize the position of the beam. We expect the whole adaptive system to decrease a star image on the spectrograph slit from 1 arcsec to 0.2 arcsec.

**Figure 1.** A cascade of adaptive systems to increase the efficiency of high-resolution spectroscopy. **Figure 1.** A cascade of adaptive systems to increase the efficiency of high-resolution spectroscopy.

At the initial stage of our work, we analyzed the structure of the aberrations in terms of Zernike polynomials and the typical amplitudes of Zernike coefficients measured in our laboratory setup [13]. Experiments showed that the contribution of the tilts is essential, so the beam position should be separately controlled during the wavefront correction of a beam distorted by the atmospheric turbulence. The experimental setup consisted of two tip-tilt mirrors, two control units to apply voltages to the piezo drivers, and two quadrant photodiodes to detect the beam position jitter. The first mirror is needed to set the beam to the desired point, and the second one facilitates directing the beam along the optical axis. The use of the FPGA allows the combination of all the electronics in one box, and the main goal of the FPGA is to reduce calculation time so that the tip-tilt correctors become the main limiting element of the system. The FPGA collects data from the quadrant photodiodes, analyzes the data, and calculates and applies voltages to the tip-tilt At the initial stage of our work, we analyzed the structure of the aberrations in terms of Zernike polynomials and the typical amplitudes of Zernike coefficients measured in our laboratory setup [13]. Experiments showed that the contribution of the tilts is essential, so the beam position should be separately controlled during the wavefront correction of a beam distorted by the atmospheric turbulence. The experimental setup consisted of two tip-tilt mirrors, two control units to apply voltages to the piezo drivers, and two quadrant photodiodes to detect the beam position jitter. The first mirror is needed to set the beam to the desired point, and the second one facilitates directing the beam along the optical axis. The use of the FPGA allows the combination of all the electronics in one box, and the main goal of the FPGA is to reduce calculation time so that the tip-tilt correctors become the main limiting element of the system. The FPGA collects data from the quadrant photodiodes, analyzes the data, and calculates and applies voltages to the tip-tilt mirror's drivers. A scheme of a digital control module and its interaction with the tip-tilt mirrors and control

units is elaborated. To confirm the ability of the beam stabilization, a series of experiments were carried out. series of experiments were carried out. **2. Atmospheric Turbulence Input** series of experiments were carried out. **2. Atmospheric Turbulence Input**

mirror's drivers. A scheme of a digital control module and its interaction with the tip-tilt mirrors and control units is elaborated. To confirm the ability of the beam stabilization, a

mirror's drivers. A scheme of a digital control module and its interaction with the tip-tilt mirrors and control units is elaborated. To confirm the ability of the beam stabilization, a

#### **2. Atmospheric Turbulence Input** To analyze the input of the atmospheric turbulence, we used a setup based on a self-To analyze the input of the atmospheric turbulence, we used a setup based on a self-

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To analyze the input of the atmospheric turbulence, we used a setup based on a self-made Shack–Hartmann wavefront sensor [14–18]. A laser beam wavefront is distorted by the airflow made by a 1.5 kW fan heater with a diameter of 200 mm. The aberrations caused by the fan heater are then analyzed with a high-speed Shack–Hartmann wavefront sensor (Figure 2) [19]. The image resolution is 480 × 480, and the microlenses array has 20 × 20 lenses with a focal length of 12 mm and a size of 240 µ × 240 µ each. A laser diode (LDS-655-FP-1.5 by Laserscom) with a wavelength of 650 nm coupled to an optical fiber is collimated up to a diameter of 50 mm. The data is obtained with a frequency of 2000 Hz. To reconstruct the wavefront, a modal method is used [20,21]. More information on the experiment is presented in [13]. The spectral energy distribution over Zernike polynomials is presented in Figure 3. This picture confirms that not only the tilts have to be corrected but also that higher-order aberrations have sufficient input. Thus, the use of the tip-tilt correction system is only the first step, and higher-order adaptive optics should be used [22] when the tilts are compensated. The tilts (Zernike 1 and Zernike 2) have the largest amplitude among the distortions caused by the atmospheric turbulence [23]. made Shack–Hartmann wavefront sensor [14–18]. A laser beam wavefront is distorted by the airflow made by a 1.5 kW fan heater with a diameter of 200 mm. The aberrations caused by the fan heater are then analyzed with a high-speed Shack–Hartmann wavefront sensor (Figure 2) [19]. The image resolution is 480 × 480, and the microlenses array has 20 × 20 lenses with a focal length of 12 mm and a size of 240µ × 240µ each. A laser diode (LDS-655-FP-1.5 by Laserscom) with a wavelength of 650 nm coupled to an optical fiber is collimated up to a diameter of 50 mm. The data is obtained with a frequency of 2000 Hz. To reconstruct the wavefront, a modal method is used [20,21]. More information on the experiment is presented in [13]. The spectral energy distribution over Zernike polynomials is presented in Figure 3 . This picture confirms that not only the tilts have to be corrected but also that higher-order aberrations have sufficient input. Thus, the use of the tip-tilt correction system is only the first step, and higher-order adaptive optics should be used [22] when the tilts are compensated. The tilts (Zernike 1 and Zernike 2) have the largest amplitude among the distortions caused by the atmospheric turbulence [23]. made Shack–Hartmann wavefront sensor [14–18]. A laser beam wavefront is distorted by the airflow made by a 1.5 kW fan heater with a diameter of 200 mm. The aberrations caused by the fan heater are then analyzed with a high-speed Shack–Hartmann wavefront sensor (Figure 2) [19]. The image resolution is 480 × 480, and the microlenses array has 20 × 20 lenses with a focal length of 12 mm and a size of 240µ × 240µ each. A laser diode (LDS-655-FP-1.5 by Laserscom) with a wavelength of 650 nm coupled to an optical fiber is collimated up to a diameter of 50 mm. The data is obtained with a frequency of 2000 Hz. To reconstruct the wavefront, a modal method is used [20,21]. More information on the experiment is presented in [13]. The spectral energy distribution over Zernike polynomials is presented in Figure 3 . This picture confirms that not only the tilts have to be corrected but also that higher-order aberrations have sufficient input. Thus, the use of the tip-tilt correction system is only the first step, and higher-order adaptive optics should be used [22] when the tilts are compensated. The tilts (Zernike 1 and Zernike 2) have the largest amplitude among the distortions caused by the atmospheric turbulence [23].

**Figure 2.** Experimental setup for wavefront statistical characteristics investigation. **Figure 2.** Experimental setup for wavefront statistical characteristics investigation. **Figure 2.** Experimental setup for wavefront statistical characteristics investigation.

**Figure 3.** Distribution of turbulence energy over Zernike polynomials 1–24. **Figure 3.** Distribution of turbulence energy over Zernike polynomials 1–24. **Figure 3.** Distribution of turbulence energy over Zernike polynomials 1–24.

The displacements of the center of the star image in front of the spectrograph of the 6 m telescope were also measured, and results are presented in Figure 4. The fluctuations are caused not only by the turbulence but also by the fundamental frequency of the telescope,

the non-ideal position of the axes, mechanical deformations, and the wind inside the telescope dome. The center of the image fluctuates with a typical amplitude of ±1 arcsec. As the spectrograph slit is just about 0.7 arcsec, these fluctuations of the image lead to 80% loss of the incoming light. If the light is stabilized with an accuracy of ±0.1–0.2 arcsec, it allows an increase in the spectrograph transmission up to 2.5 times, which is equivalent to one order of magnitude gain in angular resolution. scope, the non-ideal position of the axes, mechanical deformations, and the wind inside the telescope dome. The center of the image fluctuates with a typical amplitude of ±1 arcsec. As the spectrograph slit is just about 0.7 arcsec, these fluctuations of the image lead to 80% loss of the incoming light. If the light is stabilized with an accuracy of ±0.1–0.2 arcsec, it allows an increase in the spectrograph transmission up to 2.5 times, which is equivalent to one order of magnitude gain in angular resolution. scope, the non-ideal position of the axes, mechanical deformations, and the wind inside the telescope dome. The center of the image fluctuates with a typical amplitude of ±1 arcsec. As the spectrograph slit is just about 0.7 arcsec, these fluctuations of the image lead to 80% loss of the incoming light. If the light is stabilized with an accuracy of ±0.1–0.2 arcsec, it allows an increase in the spectrograph transmission up to 2.5 times, which is equivalent to one order of magnitude gain in angular resolution.

The displacements of the center of the star image in front of the spectrograph of the 6 m telescope were also measured, and results are presented in Figure 4. The fluctuations are caused not only by the turbulence but also by the fundamental frequency of the tele-

The displacements of the center of the star image in front of the spectrograph of the 6 m telescope were also measured, and results are presented in Figure 4. The fluctuations are caused not only by the turbulence but also by the fundamental frequency of the tele-

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**Figure 4.** Drift of the star image center along X axis (**left**) and Y axis (**right**). **Figure 4.** Drift of the star image center along X axis (**left**) and Y axis (**right**). **Figure 4.** Drift of the star image center along X axis (**left**) and Y axis (**right**).

When the weather conditions are bad and the wind is strong, the amplitude of the fluctuations of the position of a star image increases (Figure 5). Here we can also visualize the total drift of the star image over tens of seconds. When the weather conditions are bad and the wind is strong, the amplitude of the fluctuations of the position of a star image increases (Figure 5). Here we can also visualize the total drift of the star image over tens of seconds. When the weather conditions are bad and the wind is strong, the amplitude of the fluctuations of the position of a star image increases (Figure 5). Here we can also visualize the total drift of the star image over tens of seconds.

**Figure 5.** Drift of the star image center along X on a windy night. **Figure 5.** Drift of the star image center along X on a windy night. **Figure 5.** Drift of the star image center along X on a windy night.

#### **3. FPGA-Based Light Position Stabilization 3. FPGA-Based Light Position Stabilization 3. FPGA-Based Light Position Stabilization**

#### *3.1. Tip-Tilt Mirror Structure 3.1. Tip-Tilt Mirror Structure 3.1. Tip-Tilt Mirror Structure*

To stabilize the beam position, two tip-tilt mirrors controlled with an FPGA board were used. The tilts of the mirrors were ensured by piezoceramic elements with a capacity of 1 mF. The diameter of the mirror was 60 mm. The first resonance frequency of the mirror was found at 615 Hz. The mirror could be controlled with a frequency up to 500 Hz. The full dynamic range for the tilt correction was equal to 200 µrad, with a resolution of 0.05 µrad. A photo of the mirror and a cross-section together with the back view are shown in Figure 6. The piezoceramic elements (Ring Multilayer Piezo Actuators PTH1500525101 To stabilize the beam position, two tip-tilt mirrors controlled with an FPGA board were used. The tilts of the mirrors were ensured by piezoceramic elements with a capacity of 1 mF. The diameter of the mirror was 60 mm. The first resonance frequency of the mirror was found at 615 Hz. The mirror could be controlled with a frequency up to 500 Hz. The full dynamic range for the tilt correction was equal to 200 µrad, with a resolution of 0.05 µrad. A photo of the mirror and a cross-section together with the back view are shown in Figure 6. The piezoceramic elements (Ring Multilayer Piezo Actuators PTH1500525101 To stabilize the beam position, two tip-tilt mirrors controlled with an FPGA board were used. The tilts of the mirrors were ensured by piezoceramic elements with a capacity of 1 mF. The diameter of the mirror was 60 mm. The first resonance frequency of the mirror was found at 615 Hz. The mirror could be controlled with a frequency up to 500 Hz. The full dynamic range for the tilt correction was equal to 200 µrad, with a resolution of 0.05 µrad. A photo of the mirror and a cross-section together with the back view are shown in Figure 6. The piezoceramic elements (Ring Multilayer Piezo Actuators PTH1500525101 by Suzhou Pant Piezoelectric Tech. Co) were combined with the adjustment screws (9SM127M-10 by Standa).

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**Figure 6.** Photo and drawings of the tip-tilt mirror. **Figure 6.** Photo and drawings of the tip-tilt mirror. **Figure 6.** Photo and drawings of the tip-tilt mirror.

#### *3.2. Beam Stabilization Setup 3.2. Beam Stabilization Setup 3.2. Beam Stabilization Setup*

(9SM127M-10 by Standa).

(9SM127M-10 by Standa).

The setup to investigate the efficiency of the work of the beam-stabilization system is presented in Figure 7. The 50 mm laser beam distorted by a fan heater reflected from two tip-tilt mirrors, and the center of the beam was then detected with two quadrant photodiodes with a diameter of 10.2 mm, each placed at different distances from the light source (25 cm in our case). The data from the photodiodes was collected by the FPGA, and then the voltages to be applied to the piezo drivers of the mirrors to correct for the beam displacement were calculated. The FPGA sent commands to the control units (CU 1, CU 2) to change the voltages on the piezo drivers of the mirrors. Control units allow the application of voltages from −30 V to 190 V. We used a PC to adjust the system before the correction was started, to measure the response functions of each tip-tilt mirror, and to save the resulting data in an appropriate format. The setup to investigate the efficiency of the work of the beam-stabilization system is presented in Figure 7. The 50 mm laser beam distorted by a fan heater reflected from two tip-tilt mirrors, and the center of the beam was then detected with two quadrant photodiodes with a diameter of 10.2 mm, each placed at different distances from the light source (25 cm in our case). The data from the photodiodes was collected by the FPGA, and then the voltages to be applied to the piezo drivers of the mirrors to correct for the beam displacement were calculated. The FPGA sent commands to the control units (CU 1, CU 2) to change the voltages on the piezo drivers of the mirrors. Control units allow the application of voltages from −30 V to 190 V. We used a PC to adjust the system before the correction was started, to measure the response functions of each tip-tilt mirror, and to save the resulting data in an appropriate format. The setup to investigate the efficiency of the work of the beam-stabilization system is presented in Figure 7. The 50 mm laser beam distorted by a fan heater reflected from two tip-tilt mirrors, and the center of the beam was then detected with two quadrant photodiodes with a diameter of 10.2 mm, each placed at different distances from the light source (25 cm in our case). The data from the photodiodes was collected by the FPGA, and then the voltages to be applied to the piezo drivers of the mirrors to correct for the beam displacement were calculated. The FPGA sent commands to the control units (CU 1, CU 2) to change the voltages on the piezo drivers of the mirrors. Control units allow the application of voltages from −30 V to 190 V. We used a PC to adjust the system before the correction was started, to measure the response functions of each tip-tilt mirror, and to save the resulting data in an appropriate format.

by Suzhou Pant Piezoelectric Tech. Co) were combined with the adjustment screws

by Suzhou Pant Piezoelectric Tech. Co) were combined with the adjustment screws

**Figure 7.** Beam-stabilization system. **Figure 7.** Beam-stabilization system. **Figure 7.** Beam-stabilization system.

#### *3.3. FPGA Implementation* • An analog-to-digital conversion unit to receive the voltages from the quadrant pho-

8):

*3.3. FPGA Implementation*

The FPGA was placed on a printed board to make one digital module connected with the external modules to digitize the voltages from the photodiodes, to calculate the voltages for the tip-tilt drivers, to make a digital-to-analog conversion of the voltages, and also to synchronize the entire system. The board consisted of the following parts (Figure 8): todiodes; • Unit for calculating digital values of corrective voltages; • Unit for digital-to-analog conversion. Each unit was placed on its own PC board. The elements were connected by a digital

The FPGA was placed on a printed board to make one digital module connected with the external modules to digitize the voltages from the photodiodes, to calculate the voltages for the tip-tilt drivers, to make a digital-to-analog conversion of the voltages, and also to synchronize the entire system. The board consisted of the following parts (Figure

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• Unit for digital-to-analog conversion. ages for piezo drivers.

**Figure 8.** Scheme of the digital module. **Figure 8.** Scheme of the digital module.

The FPGA configuration was developed in the Quartus Prime 18.0 CAD environment from Intel. The configuration consists of 4 main modules: • Control and communication module with an analog-to-digital conversion (ADC) Each unit was placed on its own PC board. The elements were connected by a digital channel with the LVCMOS 2.5 V interface. The Cyclone 10 LP FPGA Evolution Kit board FPGA Intel® Cyclone® 10CL025YU256 was used as a unit to calculate the value of the voltages for piezo drivers.

unit • Control and communication module with a digital-to-analog conversion (DAC) unit • Module for receiving and sending data to the control program installed on the PC The FPGA configuration was developed in the Quartus Prime 18.0 CAD environment from Intel. The configuration consists of 4 main modules:

The control and communication module with an analog-to-digital conversion unit had eight channels for converting an analog signal into a digital code. It also facilitated working with 1-bit, 8-bit, and 16-bit data buses. In the described system, due to the limited size of the connector, the mode with an 8-bit data bus was used. The module controlled the operation of the chip, receiving and buffering data.

The control and communication module with the digital-to-analog conversion unit provided sequential loading of the values into each channel of the DAC chip in double-buffering mode for the simultaneous setting of the voltages in all analog channels of the chip. The communication module contained internal registers to interact with the control module for calculating digital values of the voltages. The operation of the module was independent of the main operation cycle of the tip-tilt system.

size of the connector, the mode with an 8-bit data bus was used. The module controlled

The control and communication module with the digital-to-analog conversion unit provided sequential loading of the values into each channel of the DAC chip in doublebuffering mode for the simultaneous setting of the voltages in all analog channels of the

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the operation of the chip, receiving and buffering data.

The communication module contained internal registers to interact with the control module for calculating digital values of the voltages. The operation of the module was independent of the main operation cycle of the tip-tilt system. The control module analyzed the state of the control registers of the communication module with the control program. Upon detection of the value corresponding to the start of the work cycle, the module performed the following actions:

The control module analyzed the state of the control registers of the communication module with the control program. Upon detection of the value corresponding to the start of the work cycle, the module performed the following actions: • Sent a start command to the control and communication module with the analog-todigital converter; • Wrote registers with the data, on command from the analog-to-digital conversion

chip.

The cyclogram of the control module is presented in Figure 9. The overall time for the implementation of the main operations took 62.1 µs; the most time-consuming operation (60 µs) was receiving data from the analog-to-digital converter. The limit frequency of such a system was 15 kHz; however, when changing the analog-to-digital converter operating mode to 16-bit, 30 kHz frequency could be reached. "Pause" on the cyclogram related to the tip-tilt mirror properties and was not taken into account; only control module characteristics were estimated. Based on the amplitude–resonance characteristics of the mirror, the "pause" should be about 2 ms to avoid mirror oscillations, which led to the frequency of the overall system to be 500 Hz. The cyclogram of the control module is presented in Figure 9. The overall time for the implementation of the main operations took 62.1 µs; the most time-consuming operation (60µs) was receiving data from the analog-to-digital converter. The limit frequency of such a system was 15 kHz; however, when changing the analog-to-digital converter operating mode to 16-bit, 30 kHz frequency could be reached. "Pause" on the cyclogram related to the tip-tilt mirror properties and was not taken into account; only control module characteristics were estimated. Based on the amplitude–resonance characteristics of the mirror, the "pause" should be about 2 ms to avoid mirror oscillations, which led to the frequency of the overall system to be 500 Hz.

**Figure 9.** Cyclogram of the control module. **Figure 9.** Cyclogram of the control module.

#### *3.4. Correction Algorithm 3.4. Correction Algorithm*

To stabilize the laser beam in a certain place and direction, the control module should continuously determine the deviation of the laser beam from it and drive the tip-tilt mirrors' actuators. For example, a paraboloid interpolation algorithm was used in [6] to estimate an image jitter at high resolution. A Jacobian matrix feedback controller was proposed in [10], which was adaptive to the changes of the beam path. A centroid algorithm generated inverse response to the tip-tilt aberration to control for the tip-tilt mirror at UC Mount John Observatory [24]. To stabilize the laser beam in a certain place and direction, the control module should continuously determine the deviation of the laser beam from it and drive the tip-tilt mirrors' actuators. For example, a paraboloid interpolation algorithm was used in [6] to estimate an image jitter at high resolution. A Jacobian matrix feedback controller was proposed in [10], which was adaptive to the changes of the beam path. A centroid algorithm generated inverse response to the tip-tilt aberration to control for the tip-tilt mirror at UC Mount John Observatory [24].

To calculate the control voltages, we used an algorithm based on the least square method [25]. The information on the beam position was collected from the quadrant photodiodes (Figure 10). We used FD-20KP quadrant photodiodes with a diameter of 10.2 mm and a gap of 0.3 mm between the four sectors. A standard 4-channel current-to-To calculate the control voltages, we used an algorithm based on the least square method [25]. The information on the beam position was collected from the quadrant photodiodes (Figure 10). We used FD-20KP quadrant photodiodes with a diameter of 10.2 mm and a gap of 0.3 mm between the four sectors. A standard 4-channel currentto-voltage conversion circuit on an operational amplifier [26] with a conversion factor of 1,000,000 was produced to transmit the signals from each quadrant photodiode. The beam diameter on the photodiodes was 5 mm.

voltage conversion circuit on an operational amplifier [26] with a conversion factor of 1,000,000 was produced to transmit the signals from each quadrant photodiode. The beam

**Figure 10.** Beam position detection. **Figure 10.** Beam position detection.

diameter on the photodiodes was 5 mm.

First, the equations set was composed as follows: First, the equations set was composed as follows:

$$\mathbf{X1} = \mathbf{a11} \times \mathbf{U1} + \mathbf{a12} \times \mathbf{U2} + \mathbf{a13} \times \mathbf{U3} + \mathbf{a14} \times \mathbf{U4},\tag{1}$$

$$\text{Y1} = \text{a21} \times \text{U1} + \text{a22} \times \text{U2} + \text{a23} \times \text{U3} + \text{a24} \times \text{U4},\tag{2}$$

$$\mathbf{X2} = \mathbf{a31} \times \mathbf{U1} + \mathbf{a32} \times \mathbf{U2} + \mathbf{a33} \times \mathbf{U3} + \mathbf{a34} \times \mathbf{U4},\tag{3}$$

$$\text{Y2} = \text{a41} \times \text{U1} + \text{a42} \times \text{U2} + \text{a43} \times \text{U3} + \text{a44} \times \text{U4},\tag{4}$$

where U1–U4 are voltages to be applied to the drivers of the mirrors, and coefficients a11– a44 are normalizing values proportional to the response of the actuators, and X1/X2 and Y1/Y2 are the positions of the beam center on the photodiodes calculated as where U1–U4 are voltages to be applied to the drivers of the mirrors, and coefficients a11–a44 are normalizing values proportional to the response of the actuators, and X1/X2 and Y1/Y2 are the positions of the beam center on the photodiodes calculated as

$$\mathbf{X} = ((\mathbf{S2} + \mathbf{S3}) - (\mathbf{S1} + \mathbf{S4}))/(\mathbf{S1} + \mathbf{S2} + \mathbf{S3} + \mathbf{S4}),\tag{5}$$

$$\mathbf{Y} = ((\mathbf{S1} + \mathbf{S2}) - (\mathbf{S3} + \mathbf{S4})) / (\mathbf{S1} + \mathbf{S2} + \mathbf{S3} + \mathbf{S4}),\tag{6}$$

S1–S4 are levels of the signal on the segments of the quadrant photodiodes. S1–S4 are levels of the signal on the segments of the quadrant photodiodes.

The response functions of the actuators were measured when a unit voltage, for example, U1 = 10 V, was applied to one actuator of the mirror. When U1 was applied to the actuator #1 (X-actuator of the first tip-tilt mirror), the displacements of the focal spots on the photodiodes ΔX1(U1), ΔX2(U1), ΔY1(U1), and ΔY2(U1) were measured, and coefficients a11–a14 could be calculated as The response functions of the actuators were measured when a unit voltage, for example, U1 = 10 V, was applied to one actuator of the mirror. When U1 was applied to the actuator #1 (X-actuator of the first tip-tilt mirror), the displacements of the focal spots on the photodiodes ∆X1(U1), ∆X2(U1), ∆Y1(U1), and ∆Y2(U1) were measured, and coefficients a11–a14 could be calculated as

$$\mathbf{a}\mathbf{a}\mathbf{1} = \Delta\mathbf{X}1/\mathbf{U}1,\mathbf{a}\mathbf{2}\mathbf{1} = \Delta\mathbf{Y}1/\mathbf{U}1,\mathbf{a}\mathbf{3}\mathbf{1} = \Delta\mathbf{X}2/\mathbf{U}1,\mathbf{a}4\mathbf{1} = \Delta\mathbf{Y}2/\mathbf{U}1,\tag{7}$$

The same procedure was repeated for each of the four actuators, and, as a result, matrix A was filled as 11 ⋯ 14 where ∆X1/∆X2 and ∆Y1/∆Y2 are the displacements of the focal spot on the photodiodes. The same procedure was repeated for each of the four actuators, and, as a result, matrix A was filled as

where ΔX1/ΔX2 and ΔY1/ΔY2 are the displacements of the focal spot on the photodiodes.

$$A = \begin{bmatrix} a11 & \cdots & a14 \\ \vdots & \ddots & \vdots \\ a41 & \cdots & a44 \end{bmatrix}. \tag{8}$$

the voltages and to read the coordinates of the beam center. Then an inverse matrix |B| = |A|−1 was calculated by the PC, and the PC sent this matrix to the FPGA. The system was then calibrated. The correction cycle includes three steps performed by the FPGA only: With response functions measurements, the PC sent commands to the FPGA to set the voltages and to read the coordinates of the beam center. Then an inverse matrix |B| = |A|−<sup>1</sup> was calculated by the PC, and the PC sent this matrix to the FPGA. The system was then calibrated.

1. The FPGA reads the signals from the photodiodes and determines the coordinates of the beam center on two photodiodes of X1, X2, Y1 or Y2, using Equations (5) and (6). The correction cycle includes three steps performed by the FPGA only:

$$\|\mathbf{U}\| = \|\mathbf{B}\| \times \|\mathbf{X}\mathbf{1}\mathbf{Y}\mathbf{1}\mathbf{X}\mathbf{2}\mathbf{Y}\mathbf{2}\|\tag{9}$$

3. The voltages are applied to the actuators of the tip-tilt correctors. A photo of the setup to test the abilities of the stabilizing system is presented in Fig-|U|=|B|×| X1 Y1 X2 Y2 | (9)

3. The voltages are applied to the actuators of the tip-tilt correctors.

*Photonics* **2022**, *9*, x FOR PEER REVIEW 9 of 11

*Photonics* **2022**, *9*, x FOR PEER REVIEW 9 of 11

#### **4. Results and Discussion** ure 11. The laser beam is reflected from the tip-tilt mirrors and then detected with the quadrant photodiodes. The signal from the photodiodes is transferred to the FPGA. The 3. The voltages are applied to the actuators of the tip-tilt correctors.

**4. Results and Discussion**

A photo of the setup to test the abilities of the stabilizing system is presented in Figure 11. The laser beam is reflected from the tip-tilt mirrors and then detected with the quadrant photodiodes. The signal from the photodiodes is transferred to the FPGA. The FPGA analyzes the data and calculates and sends voltages to the drivers of the tip-tilt mirrors. The displacement of the beam position is saved for further analysis. FPGA analyzes the data and calculates and sends voltages to the drivers of the tip-tilt mirrors. The displacement of the beam position is saved for further analysis. Figure 12 shows the measured fluctuation of the displacements on the first photodiode for 500 ms; the red line corresponds to the beam center position without any correction, and the green line shows the behavior of the beam center when correction is on. **4. Results and Discussion** A photo of the setup to test the abilities of the stabilizing system is presented in Figure 11. The laser beam is reflected from the tip-tilt mirrors and then detected with the quadrant photodiodes. The signal from the photodiodes is transferred to the FPGA. The FPGA analyzes the data and calculates and sends voltages to the drivers of the tip-tilt

**Figure 11.** Photo of the testbed. **Figure 11.** Photo of the testbed.

Figure 12 shows the measured fluctuation of the displacements on the first photodiode for 500 ms; the red line corresponds to the beam center position without any correction, and the green line shows the behavior of the beam center when correction is on. **Figure 11.** Photo of the testbed.

positioning was ±2 µrad. **Figure 12.** Fluctuations of the beam position on the 1st quadrant photodiode. **Figure 12.** Fluctuations of the beam position on the 1st quadrant photodiode.

correction. The RMS without correction was 1 mm, and it was 0.3 mm with the correction loop closed. The ratio between these data was 20 lg(RMSoff/RMSon), which showed that the system allowed the reduction of the amplitude of the beam position change up to 10 dB. The accuracy of the center position calculation on each photodiode was 0.25 µm. As the distance between the two photodiodes was 25 cm, the accuracy of the beam positioning was ±2 µrad. The accuracy of the center position calculation on each photodiode was 0.25 µm. As the distance between the two photodiodes was 25 cm, the accuracy of the beam positioning was ±2 µrad.

the distance between the two photodiodes was 25 cm, the accuracy of the beam

The RMS of the beam position was calculated during 30 s both with and without

The RMS of the beam position was calculated during 30 s both with and without correction. The RMS without correction was 1 mm, and it was 0.3 mm with the correction loop closed. The ratio between these data was 20 lg(RMSoff/RMSon), which showed that the system allowed the reduction of the amplitude of the beam position change up to 10 dB. The RMS of the beam position was calculated during 30 s both with and without correction. The RMS without correction was 1 mm, and it was 0.3 mm with the correction loop closed. The ratio between these data was 20 lg(RMSoff/RMSon), which showed that the system allowed the reduction of the amplitude of the beam position change up to 10 dB.

Regarding the electronics and mechanical elements, it is possible to achieve a required jitter reduction better than 10 dB. To improve the result, the beam diameter on the photodiodes should be optimized to provide a linear response at the positions close to the photodiode center; additionally, the type of photodiodes should be chosen, taking into account their signal-to-noise characteristics. One of the weak points might be an algorithm of correction based on proportional terms only. As a next step, it is necessary to modify the algorithm and integral and derivative terms.

For sure our laboratory setup was not equal to the real conditions at the telescope herein, we wanted to present our results on a comparatively low-cost, reliable, and simple beam-stabilization system that could be installed on a 6 m telescope.

#### **5. Conclusions**

The FPGA platform was used to implement a beam-position-stabilization system. The system consists of two tip-tilt piezo-driven mirrors with control units and two quadrant photodiodes. The signals from the photodiodes are collected with the FPGA, and the control voltages necessary for the correction of the beam center displacement are calculated and applied to the actuators of the mirrors. The FPGA allows the speeding up of data processing and DAC/ADC data conversion, and the system performance only depends on the properties of the tip-tilt corrector and could be improved if necessary. The proposed beam-stabilization system allows reducing the beam jitter up to 10 dB. This number is actually not the expected one (for the BTA telescope, we need at least 5 times the jitter reduction), but we have the ideas and a way to improve it.

**Author Contributions:** Conceptualization, V.K. and J.S.; methodology, A.R.; software, V.B.; validation, I.G., A.N. and A.R.; formal analysis, A.R.; investigation, A.N.; resources, A.R.; data curation, A.K.; writing—original draft preparation, I.G.; writing—review and editing, A.K.; visualization, I.G.; supervision, V.K.; project administration, J.S.; funding acquisition, J.S. and A.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** The investigations of the local correction or the beam position (Sections 3 and 4) were supported by the Russian Science Foundation under grant #20-19-00597, and the artificial turbulence investigation (Section 2) was supported by the state assignment of the Ministry of Science and Higher Education of the Russian Federation (theme No.<PHONE_NUMBER>254-7-1.5.4).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

### **References**

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*3.7. Role of Reactive Species*

The scavengers of *di*–ammonium oxalate monohydrate (AO), 2–propanol, and ascorbic acid (AA) were used to identify the reactive species for MB photodegradation. The three scavengers' effect on the MB photodegradation with CuO/ZnO is displayed in Figure 7. From the results (Figure 7), it was observed that with the addition of 2–propanol, AO, and AA as a scavenger, photocatalytic degradation dropped from 66% (without scavenger) to 23% (•O2 −), 13% (•OH), and 53% (h+) for 1h, while the photocatalytic activity significantly decreased from 88% (without scavenger) to 29% (•O2 −), 37% (•OH), and 79% (h+) for 2 h irradiation, respectively. It can be surmised that the important roles played for the photodegradation of MB were by •O2 − and •OH radicals, while a minor role was played by the h+ radical in the degradation process under sunlight [38].

**Figure 7.** Effects of scavengers on the photodegradation of MB with CuO/ZnO using sunlight.


**Author details**

Mariana Correa Rossi1 \*, Liliana Romero Resendiz2 and Vicente Amigó Borrás1

1 Polytechnic University of Valencia, Valencia, Spain

2 Materials Research Institute – UNAM, Mexico

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*2.2. The SWAT Model: Basins and Impoundments*

SWAT is one of the few hydrological models that have an input for ponds [47], which makes it an ideal tool for this study. SWAT is a semi-physical deterministic distributed and continuous hydrologic model that functions at a daily time step with options for sub-hourly routing, as well [55]. It is a quite complex model with significant input data demands. However, the basic components can be readily obtained and are relatively simple [56]. Essential inputs consist of weather data, digital elevation model (DEM), soil, and land occupation maps [57]. Based on these inputs, SWAT divides the basin into sub-basins, which further divides into smaller hydrological response units (HRUs) [58]. HRUs are defined as units with homogeneous land occupation, topography, and pedological properties [59]. These are generated in order to lump somewhat similar areas scattered through the basin into a single unit, simplifying the model's run by avoiding unpractical simulations while accounting for the diversity of different factors in the basin [60]. Within SWAT, ponds are defined as waterbodies,

being an integral part of a sub-basin's hydrological network; they are capable of intercepting surface runoff [60], thereby modifying the hydro-sedimentary behavior of the basin. Since this work targets the erosion/sedimentary behavior of the SWAT model, only their related equations will be presented. Further details can be found in Neitsch et al. (2011) [47].

**Figure 1.** Study area.

Soil erosion is calculated based on the Modified Universal Soil Loss Equation (MUSLE) using the following formula [55]:

$$\text{seed} = 11.8 \times (Q\_{\text{surf}} \cdot q\_{\text{pank}} \cdot area\_{\text{hru}})^{0.56} \times K\_{\text{USLE}} \times C\_{\text{USLE}} \times P\_{\text{USLE}} \times LS\_{\text{USLE} \times \text{CFRG}} \tag{1}$$

where *sed* is the HRU sediment yield (t); *Qsurf* is the surface runoff volume (mm); *qpeak* is the peak runoff rate (m3/s); *areahru* is the area of the HRU (ha); *KUSLE* is the Universal Soil Loss Equation (USLE) soil erodibility factor; *CUSLE* is the USLE cover and management factor; *PUSLE* is the USLE support practice factor; *LSUSLE* is the USLE topographic factor; and C*FRG* is the coarse fragment factor.

It should be noted that MUSLE virtually calculates the sediment yields due to soil erosion that reach the main streams of the sub-basins in a unit of time. This is the reason why sediment delivery ratios are not required, contrarily to USLE and RUSLE [61].

The mass balance equation of sediment in ponds is described by the following formula [47]:

$$scd\_{wb} = scd\_{wbi} + scd\_{flawin} - scd\_{stl} - scd\_{flawout} \tag{2}$$

where *sedwb* is the amount of sediment at the end of the day; *sedwbi* is the amount of sediment at the day's beginning; *sedflowin* is the amount of sediment provided from inflows; *sedstl* is the amount of settled sediments; and *sedflowout* is the amount of sediment transported as outflow. All components are expressed in metric tons.

#### *2.3. The CORINE Erosion Model*

The CORINE erosion model is a simplification of the USLE model. In the CORINE model, erosion risks are classified on a scale of 0–3, with 0 corresponding to the no-erosion class, 1 to low erosion risks, 2 to moderate erosion risks, and 3 to high-erosion risks [62]. For the estimation of erosion using the

CORINE model, parameters such as soil erodibility, climate erosivity, topography (slope), and LU/LC (vegetation cover) are required [61]. Each parameter, in turn, consists of several sub-factors, and is classified according to the CORINE model into respective indices (Figure 2). Once established, the soil erodibility index is combined with climate erosivity and slope, to yield the potential soil erosion risk map. Subsequently, the potential soil erosion risk map indices (0–3) are crossed with those of the vegetation cover to yield the actual soil erosion map.

**Figure 2.** CORINE model framework, MFI: Modified Fournier Index [63]; Pi is the total precipitation in month i and *P* is the mean annual total precipitation; BGI: Bagnouls–Gaussen Index [64]; ti the mean temperature for the month i; Pi the total precipitation in month i and ki the proportion of the month i in which 2ti − Pi > 0 [62].

*2.4. Input Data and Adaptation to the SWAT and CORINE Models*

The input data used in this study, along with a short description, are summarized in Table 1.

#### **Table 1.** CORINE erosion model and SWAT input data.

† The correspondence of the Claise basin to a natural park renders any human induced modifications on soils minor. Further, the Claise's land occupation pattern is relatively stable. Therefore, climate is the only variable factor in the study area. Hence, the temporal difference of the utilized datasets does not cause temporal induced biases.

ArcSWAT version 2012.10\_3.19 was applied in an ArcGIS 10.3 (Environmental Systems Research Institute, Inc. (Esri), 380 New York Street, Redlands, California, USA) environment to perform simulations at a daily time step for the period 1970–2018. The first seven years of the simulation, from 1970 to 1976, were used for the model's warm up. The calibration and validation phases of the model were carried out by means of comparison between simulated discharges and measured discharge data from the station L6202040–La Claise au Grand–Pressigny (Pont de Fer), obtained from Eau France Banque HYDRO–Ministère de l'Ecologie, de l'Energie, du Développement Durable et de l'Aménagement du Territoire (MEEDDAT)/Direction Générale de la Prévention des Risques (DGPR)/Service des Risques Naturels et Hydrauliques (SRNH). Due to the unavailability of sediment records for this study, only a hydrologic calibration was performed. However, surface runoff and stream flow—regardless of the approach used to model the sedimentary cycle—are the driving forces of runoff and streambed erosion, as well as of overland and stream sediment transport [65]. Hence, one might say that the calibration of the sediment part of the study might be implicit, but nonetheless robust. According to Hallouz et al. (2018) [66], a calibrated hydrologic model gives a certain degree of reliability to the sedimentary output. However, the availability of sediment data is often a constraint in relative studies, as measurements, either in the form of gross or net erosion or in the form of total sediment discharge, in streams, are often nonexistent. Even in cases where such data is available, it is in the form of sparse discrete measurements and rarely in the form of continuous sediment graphs, where they could be used for a robust calibration. In the case of the Claise, this would be a very challenging task, given the large number of ponds. For this reason, SWAT was calibrated according to the calibration scheme of Jalowska and Yuan (2019) [38], where a complete representation of an impoundment effect (here, ponds) is ensured. Nonetheless, the absence of sediment records poses some short of limitation that needs to be considered by the decision-makers of the basin.


**2. Functional Bacterial Amyloid Proteins (FUBA) May Cross-Seed the Aggregation of A**β **as Well as Other Neuronal Amyloids**

Chapman and colleagues originally described amyloid proteins made by bacteria in 2002 [7]. It has now been demonstrated that many organisms present inside our bodies are capable of producing FUBAs, including Streptococcus, Staphylococcus, Salmonella, Mycobacteria, Klebsiella, Citrobacter, and Bacillus species [8]. These bacterial amyloids are adaptive, help the organisms stick together, and protect bacterial communities by resisting destruction by viruses and other agents. Bacterial amyloids have been linked to infections as well to autoimmunity [9]. In 2015, Friedland proposed that bacterial amyloid may cross-seed the aggregation of neuronal proteins such as Aβ, alpha synuclein (AS), tau and others to initiate a prion-like propagation [10]. This process may begin with enteroendocrine and M cells in the gut epithelium and be transmitted to the brain through the autonomic nervous system via a bidirectional pathway (the gut-brain axis, [11,12]). It was subsequently demonstrated by Chen et al. that feeding bacterial amyloid to aged rats accelerates AS deposition in the gut, as well as in the brain [13].

It has been observed that protein folding in an amyloid configuration is a highly conserved process throughout evolution [14]. Recently, a series of studies have shown that FUBAs may interact with neuronal proteins in the manner predicted. That is, a bacterial amyloid protein may influence the aggregation of neuronal proteins. Perov et al. discovered structural similarity between the fibers of the best studied bacterial amyloid protein curli, and neuronal amyloids [14]. They noted that the curli protein CsgA cross-seeds the fibrillation of Aβ, as was previously proposed [10]. Curli cross-seeding with Aβ was also observed to have a concentration dependent effect, suggesting that this cross-seeding may be of widespread impact [15]. It has also been shown that the functional amyloid protein FapC, produced by Pseudomonas, may affect fibrillation of AS [16]. It should be noted that these studies were done in vitro with nonbiological buffers, suggesting a need for in vivo studies using simple systems such as yeast or *C. elegans* to demonstrate this process.

#### **3. FUBAs May Enhance Neuro-Inflammation through Molecular Mimicry**

Gut bacteria have been shown to remarkably influence microglial function [17] and bacterial amyloid is recognized by the innate immune system as a pathogen associated molecular pattern (PAMP) involving toll-like receptor 2 (TLR2), cluster of differentiation 14 (CD14), nuclear factor kappa light chain enhancer of activated B cells (NFkB), and inducible nitric oxide synthase (iNOS) [10]. Remarkably, this pathway is also involved in the recognition of misfolded neuronal proteins such as Aβ and AS [18]. The presence of Aβ aggregates in the brain, often found with healthy aging, may be recognized by the innate immune system as a bacterial product, due to its structural similarity to bacterial proteins. Bacterial amyloid also causes activation of the NLRP3 inflammasome, causing downstream release of pro-inflammatory interleukin-1β in microglia, eventually leading to Aβ and tau aggregation [6,19,20].

#### **4. Microbiota May Influence Inflammation in the Brain through E**ff**ects on Circulating Immune Cells**

Over 70% of our lymphocytes reside in the gut—a sign of the enormous influence of the microbiota on the immune system. Bacteria produce short-chain fatty acids such as propionate, butyrate and acetate that epigenetically promote the function of anti-inflammatory regulatory lymphocytes (Tregs) [21]. Conversely, some groups of bacteria enhanced by a low fiber diet do not produce short chain fatty

acids and upregulate the production of pro-inflammatory CD4+ Th17 cells [21]. As we have discussed above, there is an inflammatory component to all neurodegenerations.

#### **5. Microbiota May Influence the E**ff**ects of Apolipoprotein E (Apo E) Genotype on Disease Risk**

It was proposed in 2015 that the effect of Apo E on Alzheimer risk may be caused by the influence of Apo E genotype on the microbiota [10]. Recently, Tran et al. provided evidence that in humans and transgenic mice the microbial populations and metabolite panels are different between ApoE genotypes [22]. This relationship may provide a valuable clue to development of microbiota-based therapies. It is important to recall that susceptibility to Creutzfeldt Jakob disease and bovine spongiform encephalopathy is determined by the genotype at codon 129 of the prion protein gene (people who are heterozygous MV, or valine homozygotes VV are resistant).

### **6. Microbiota May Influence Neurodegeneration through Production of Vitamins and Other Nutrients**

The microbiome produce important metabolic products that influence health and disease. It was recently demonstrated that gut bacteria are involved in amyotrophic lateral sclerosis (ALS). Transgenic ALS hSOD1 G93A mice were found to have bacterial dysbiosis and mice lacking gut bacteria had accelerated disease. Preliminary studies suggest that it may be due to a deficiency of nicotinamide (vitamin B3) [23]. Nicotinamide is a NAD+ precursor crucial for energy and redox metabolism [24].

#### **7. Further Considerations**

Current microbiota studies have focused largely on bacterial populations in the intestines involving studies of stool. The potential role of oral, nasal, laryngeal and pharyngeal organisms has not been extensively studied. This is especially important in neurodegeneration, as hyposmia is a feature of both AD and Parkinson's disease (PD). Furthermore, the proximity of the brain to the nose (which has its own microbiota) demonstrates the opportunity for interactions between olfactory receptors, microbes and microbial metabolites [25,26]. The mouth is also a critical ecological niche for hundreds of different bacterial species in humans. Also, the common oral pathogen *Streptococcus mutans* may produce an amyloid product. Studies of stool do not assess the intestinal microbiota that reside in the mucus layer. In addition to the description of the populations present in the gut, it is imperative to consider metabolic products generated by the microbiota, which undoubtedly influence health and disease [27,28].

A central question which remains unanswered is: what FUBAs are present in the body, and how much? Current work has been devoted largely to bacteria while the role of viruses, fungi, parasites, and other agents have not been considered. It is estimated that there may be twice as many phagesin the gut as bacteria—their contribution to bacterial homeostasis is just beginning to be addressed. Furthermore, fungi can act as commensals in the gut and a few fungal amyloid proteins have been described [29]. Additional variables that need to be considered are sex-specific microbiota changes, as recently demonstrated in antibiotic treated transgenic mouse models of Aβ amyloidosis [30], and their potential effect of sex on FUBAs. These outstanding questions are addressed in the Table 1.

One potentially exciting aspect to microbiota studies is the relative ease with which microbial populations can be adjusted. This can be called "gene therapy in the kitchen", because it is possible to change bacterial populations in the gut by changing diet in as little as two weeks [31]. The potential for therapeutic interventions based on FUBA is illustrated by a recent paper by Sampson et al. [32] that elegantly reports the role of the curli (CsgA) protein in producing neurodegeneration in alpha synuclein overexpressing transgenic PD model mice. Oral treatment of wild type *E. coli* producing the bacterial amyloid protein CsgA with epigallocatechin gallate (EGCG) reduced biofilm formation and inhibited the production of aggregated alpha synuclein. EGCG is a potent polyphenol found in plants, which impairs the production of bacterial CsgA. As EGCG is not well-absorbed from the gut to the bloodstream, it is likely that these influences are localized to the intestinal microbiota. This work supports the exciting opportunity to treat neurodegenerative diseases with agents that act primarily on the microbiota, and not directly on the brain. The potential for influencing pathogenic processes in AD through therapies targeting the gut shows great promise and is now receiving academic and commercial attention worldwide.

**Table 1.** Outstanding questions concerning the role of functional bacterial amyloid proteins (FUBA) and neurodegeneration.

**Author Contributions:** Conceptualization R.P.F.; methodology R.P.F.; software R.P.F.; validation R.P.F.; formal analysis R.P.F.; investigation R.P.F.; resources R.P.F., Z.K.; data creation, R.P.F.; Z.K., J.D.M.; writing—review and editing R.P.F., Z.K., J.D.M.; visualization R.P.F.; supervision R.P.F. project administration R.P.F.; funding acquisition R.P.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Japan Society for the Promotion of Science, the University of Louisville, the Jewish Heritage Fund for Excellence, Walter Cowan, the National Institute on Aging, the family of E.A. Ford III and Axial Biotherapeutics.

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


**8. Diagnostic evaluation**

Ultimately, multimodality imaging techniques are often utilized including computed tomography (CT), magnetic resonance imaging (MRI), and/or endoscopic retrograde cholangiopancreatography (ERCP) to confirm the extent of ductal involvement or the presence of extrahepatic disease [25]. Frequently, further imaging techniques are used to differentiate type I CC from type IVa, in the presence of intrahepatic biliary dilatation [11]. A cyst, presenting in the porta hepatitis, separated from the gallbladder and continuing with enlarged biliary ducts can be shown by ultrasound (US). Additionally, fusiform dilatation of choledoch, intrahepatic biliary dilatation (60–80%), biliary stones, and state of liver parenchyma can be shown by US [86].

Other intraabdominal cysts, such as pancreatic pseudocysts, echinococcal cysts, or biliary cystadenomas should also be differentiated from CCs, whether the cyst has continuity with the biliary tree or not [40]. CT is not only useful for demonstrating continuity of the cyst with the biliary tree, but also demonstrates relation of the cyst with the surrounding structures and the presence of associated malignancy [87]. In order to correctly plan surgery, CT cholangiography can be used to identify the full anatomy of the biliary tree but unfortunately it has been reported to be less sensitive for imaging the pancreatic duct which is responsible for the reflux of contrast into the biliary ducts [87]. As it is well known today, the nephrohepatotoxicity of the contrast and the ionized radiation exposed are the restrictions of CT utilization in pediatric population (**Figure 3**) [62].

MRCP is noninvasive and highly sensitive (70–100%) and specific (90–100%) in the diagnosis of CCs [88, 89], so, is considered the current gold standard imaging even for initial evaluation [62]. Additionally, there is no irradiation, and modern scanners have alleviated the need for protracted breath-hold making it more amenable to the pediatric population [10, 90]. Although both ultrasound and CT are highly sensitive and specific in the diagnosis of CCs, MRCP can better identify the CCs subtypes and coexisting abnormalities [89]. For example, MRI can easily identify the pancreaticobiliary ductal anatomy, while ultrasound cannot accurately demonstrate the APBDU [89, 91]. Additionally, MRCP is preferred modality in the pediatric population due to invasive nature and inherent risks of endoscopic ultrasound and ERCP, despite their ability of detecting the abnormality of the common

**Figure 3.** *Type III CC. (a) MRCP; (b) CT image.*

channel [89]. MRCP has also been shown to be as effective as intraoperative cholangiography in planning surgery [18]. In addition, lower cost and morbidity compared to other imaging/diagnostic modalities, and reliability for detecting abnormalities associated with CCs such as cholangiocarcinoma and choledocholithiasis, are some of the favorable features of MRCP [89, 92]. Unfortunately, as seen often in patients with CCs, intraductal air, debris, stones, or protein plugs can interfere with the signal and alter visualization (**Figure 4**) [93].

Although it is highly sensitive, invasiveness and associated risks including cholangitis, bleeding, pancreatitis, and perforation makes percutaneous transhepatic cholangiography (PTC) or ERCP utilization less frequently [94]. Moreover, PTC and ERCP can be technically challenging and require general anesthesia in the pediatric population. A lot of surgeons are finding the use of diagnostic ERCP and PTC in CCs unnecessary due to advantages of both MRCP and perioperative cholangiography (performed in nearly almost patients and give highly detailed information about

**95**

*Pediatric Choledochal Cysts: Unknowns are Decreasing DOI: http://dx.doi.org/10.5772/intechopen.84301*

**8.1 The differential diagnosis**

necessary [107].

biliary anatomy) [10, 95, 96]. Contemporary, ERCP should only be performed in cases where the appropriate diagnosis cannot be made by other less-invasive examinations, or when therapeutic performance (complications such as cholangitis or biliary stone obstruction [97, 98] and stabilization of the patients with preparing

Another method that is not preferred now for diagnostic purposes is hepatobiliary scintigraphy. Although it is possible to do it with all IDA variants, DISIDA is the best. The radio isotopic substance is collected in the liver and is normally thrown into the biliary tract, but accumulates in the cyst space in patients with CC [101, 102].

There are many diseases including biliary atresia, infectious hepatitis, embryonal hepatic rhabdomyosarcoma, biliary lithiasis, pancreatitis, biliary hamartoma in the differential diagnosis of CCs, especially biliary atresia that is one of the two causes of neonatal obstructive jaundice in neonatal period [10]. Differentiating cystic biliary atresia (CBA), a subtype of biliary atresia and has an entirely different treatment approach, from CCs is particularly difficult. Therefore, prompt accurate diagnosis is critical [103]. While earlier presentation (<months of age), smaller cysts with less dilatation of the intrahepatic biliary system, and an atretic gallbladder with irregular and hypoplastic biliary radicles that is seen on ultrasound and cholangiography are characteristics of CBA patients [10, 20, 104, 105]; a dilated gallbladder communicated with the cyst in addition with a dilated intrahepatic

It is still important to differentiate biliary rhabdomyosarcoma, a rare soft tissue tumor that affects only 1% of children, from CC [106, 107]. In the presence of a mass or intraductal growth that causes obstructive jaundice, the possible diagnosis should return in favor of rhabdomyosarcoma in children and prompt evaluation is

"Children with CBD dilatation did not differ significantly in clinical characteristics compared with children who had obstructive CBD dilatation" said Oh and colleagues [78] by evaluating the cholangiographic characteristics of 85 children with CBD dilatation to differentiate obstructive and congenital CBD dilatation. Indeed, it can be difficult to distinguish them. Therefore, in the pediatric population with dilated biliary trees, ruling out a distal biliary obstruction that causes secondary

As noted above, type I CC may present with intrahepatic biliary dilatation secondary to biliary stasis, thus resembling a type IVa CC. Some authors consider that this distinction is critical given the therapeutic implications and the need to include hepatic resection (in the case of type IVa CC) in addition to extrahepatic biliary tree excision [18], whereas the others consider that the need preoperatively distinguishing between type I and IVa CCs is controversial because for both, complete excision of the extrahepatic bile duct and intensive long-term follow-up

The treatment time of antenatal diagnosed of CCs has been still a matter of debate. Some reports say that they can be operated within 2–6 weeks, even if they are asymptomatic, due to a potential complication risk of cysts, whereas, the others suggest that they can be followed-up for a time of period with US and regular

them to the next definitive surgery) is required (**Figure 4**) [39, 99, 100].

biliary tree is mostly a determiner to infantile CC [104].

biliary dilatation is prudent and essential [18].

remains standard of care [11].

monitoring of liver functions [108, 109].

**9. Management**

**Figure 4.** *PTC reveals the detailed anatomy of the biliary tracts and associated CC.*

biliary anatomy) [10, 95, 96]. Contemporary, ERCP should only be performed in cases where the appropriate diagnosis cannot be made by other less-invasive examinations, or when therapeutic performance (complications such as cholangitis or biliary stone obstruction [97, 98] and stabilization of the patients with preparing them to the next definitive surgery) is required (**Figure 4**) [39, 99, 100].

Another method that is not preferred now for diagnostic purposes is hepatobiliary scintigraphy. Although it is possible to do it with all IDA variants, DISIDA is the best. The radio isotopic substance is collected in the liver and is normally thrown into the biliary tract, but accumulates in the cyst space in patients with CC [101, 102].

### **8.1 The differential diagnosis**

*Pediatric Surgery, Flowcharts and Clinical Algorithms*

signal and alter visualization (**Figure 4**) [93].

*PTC reveals the detailed anatomy of the biliary tracts and associated CC.*

**Figure 3.**

*Type III CC. (a) MRCP; (b) CT image.*

channel [89]. MRCP has also been shown to be as effective as intraoperative cholangiography in planning surgery [18]. In addition, lower cost and morbidity compared to other imaging/diagnostic modalities, and reliability for detecting abnormalities associated with CCs such as cholangiocarcinoma and choledocholithiasis, are some of the favorable features of MRCP [89, 92]. Unfortunately, as seen often in patients with CCs, intraductal air, debris, stones, or protein plugs can interfere with the

Although it is highly sensitive, invasiveness and associated risks including cholangitis, bleeding, pancreatitis, and perforation makes percutaneous transhepatic cholangiography (PTC) or ERCP utilization less frequently [94]. Moreover, PTC and ERCP can be technically challenging and require general anesthesia in the pediatric population. A lot of surgeons are finding the use of diagnostic ERCP and PTC in CCs unnecessary due to advantages of both MRCP and perioperative cholangiography (performed in nearly almost patients and give highly detailed information about

**94**

**Figure 4.**

There are many diseases including biliary atresia, infectious hepatitis, embryonal hepatic rhabdomyosarcoma, biliary lithiasis, pancreatitis, biliary hamartoma in the differential diagnosis of CCs, especially biliary atresia that is one of the two causes of neonatal obstructive jaundice in neonatal period [10]. Differentiating cystic biliary atresia (CBA), a subtype of biliary atresia and has an entirely different treatment approach, from CCs is particularly difficult. Therefore, prompt accurate diagnosis is critical [103]. While earlier presentation (<months of age), smaller cysts with less dilatation of the intrahepatic biliary system, and an atretic gallbladder with irregular and hypoplastic biliary radicles that is seen on ultrasound and cholangiography are characteristics of CBA patients [10, 20, 104, 105]; a dilated gallbladder communicated with the cyst in addition with a dilated intrahepatic biliary tree is mostly a determiner to infantile CC [104].

It is still important to differentiate biliary rhabdomyosarcoma, a rare soft tissue tumor that affects only 1% of children, from CC [106, 107]. In the presence of a mass or intraductal growth that causes obstructive jaundice, the possible diagnosis should return in favor of rhabdomyosarcoma in children and prompt evaluation is necessary [107].

"Children with CBD dilatation did not differ significantly in clinical characteristics compared with children who had obstructive CBD dilatation" said Oh and colleagues [78] by evaluating the cholangiographic characteristics of 85 children with CBD dilatation to differentiate obstructive and congenital CBD dilatation. Indeed, it can be difficult to distinguish them. Therefore, in the pediatric population with dilated biliary trees, ruling out a distal biliary obstruction that causes secondary biliary dilatation is prudent and essential [18].

As noted above, type I CC may present with intrahepatic biliary dilatation secondary to biliary stasis, thus resembling a type IVa CC. Some authors consider that this distinction is critical given the therapeutic implications and the need to include hepatic resection (in the case of type IVa CC) in addition to extrahepatic biliary tree excision [18], whereas the others consider that the need preoperatively distinguishing between type I and IVa CCs is controversial because for both, complete excision of the extrahepatic bile duct and intensive long-term follow-up remains standard of care [11].


*4.9. Acute Inflammation Induced by TmC4-47.2*

Balb/c mice were intraperitoneally injected with TmC4-47.2 at 10 μg in 500 μL, according to Lima et al. [32]. As a negative control, mice were injected i.p. with PBS. After 2, 16, and 24 h, the peritoneum exudates were harvested for total and differential cell count and protein determination. IL-6, TNF-α, IL-1β, KC, eotaxin, and MCP-1 were analyzed using a specific two-site sandwich ELISA with OpEIA Kits (BD-Pharmingen, San Diego, CA, USA). The total leukocyte count was performed in Turk solution, and for differential counts, neutrophils (Ly6G+), eosinophils (CCR3+), and macrophages (F4/80+) were identified by flow cytometry and based on staining and morphologic characteristics using a light microscope Axio Imager A1 (Carl Zeiss, Germany) with an AxioCam ICc1 digital camera (Carl Zeiss).

#### *4.10. Statistical Analysis*

All values were expressed as mean ± SEM. Experiments using 3 to 5 mice per group were performed independently two times. Parametric data were evaluated using analysis of variance, followed by the Bonferroni test for multiple comparisons. Non-parametric data were assessed using the Mann-Whitney test. Differences were considered statistically significant at *p* < 0.05 using GraphPad Prism (Graph Pad Software, v6.02, 2013, La Jolla, CA, USA).

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/toxins14010002/s1. Figure S1: (A) Fractionation process of the *Thalassophryne maculosa* venom. A total of 5 mg of venom was applied to a semi-preparative reversed-phase C18 column coupled to a high-pressure liquid chromatography (HPLC) system. The gradient used was from 20 to 80% buffer B in 60 min under 5 mL/min flow rate. The absorbance was monitored at 214 nm. (B) The protein content was evaluated by 12% SDS-PAGE electrophoresis in polyacrylamide gel (10 μg/well). Samples 1, 2, and 3 correspond to the fractions obtained by chromatography. MW corresponds to molecular mass markers, and the band circled in red refers to the TmC4-47.2.; Figure S2: (A) Second fractioning step for toxin purification. Fraction 2 (1 mg mL−1) obtained in the first chromatography was applied to a reversed-phase analytical C8 column coupled to a high-pressure liquid chromatography (HPLC) system. The gradient used was 20 to 80% of buffer B over 35 min under a flow rate of 1 mL/min. The absorbance was monitored at 214 nm. (B) The protein content of fractions 1, 2s, 2d, 3 and 4 was evaluated by 12% SDS-PAGE electrophoresis on polyacrylamide gel (10 μg/well). MW corresponds to molecular mass markers. The bands highlighted in red refer to TmC4-47.2 toxin; Figure S3: (A) Third fractionation for purification of *Thalassophryne maculosa* TmC4-47.2 toxin. Fractions 2s, 2d and 3 were mixed and 1 mg mL−1 of the pool was applied to a reversed-phase analytical C8 column coupled to a high-pressure liquid

chromatography (HPLC) system. The gradient used was 20 to 80% buffer B over 35 min under 1 mL min−1 flow rate. The absorbance was monitored at 214 nm. (B) The protein content of fractions 1, 2s and 2d was evaluated by 12% SDS-PAGE electrophoresis on polyacrylamide gel (10 μg/well). MW corresponds to molecular mass markers and the bands highlighted in red refer to TmC4-47.2 toxin.

**Author Contributions:** Methodology, validation, formal analysis, investigation, data curation, writing—original draft preparation, P.I.S.J., K.C., A.L.A.M., L.B.-L., G.R.D.; Conceptualization, resources, data curation, formal analysis, writing—review and editing, visualization, supervision, project administration, funding acquisition, M.L.-F., I.S.-R. and C.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the São Paulo Research Foundation—FAPESP (#2013/07467-1) and in part by the Coordination for the Improvement of Higher Education Personnel—CAPES (#001) and National Council for Scientific and Technological Development—CNPq (#305414/2019-4).

**Institutional Review Board Statement:** The study was conducted according to the animal welfare standards, and all necessary permits for the capture of *T. maculosa* and to collect the venom were obtained from the Escuela de Ciencias Aplicadas del Mar, Núcleo Nueva Esparta, Universidad de Oriente, Isla Margarita; Venezuela (Permit Number #30/07). Additional experiments were carried out under the National Council for Animal Experiment Control (CONCEA) and approved by the Butantan Institute's Animal Use Ethics Commission (CEUAIB #275/06).

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** We thank the São Paulo Research Foundation—FAPESP, CAPES, and CNPq for the financial support. Additional thanks to Douglas Boletini-Santos for the assistance in the data collection and to the Butantan Institute Immunoregulation Unit staff. We also thank the anonymous reviewers for their careful reading and insightful comments and suggestions.

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

### **References**


**10. Discussion and conclusion**

The advent of advanced targeted, 'customizable,' and precise new technologies for insect resistant plants, in their various embodiments and combinations, symbolises a sustainable option countering the emergence of resistant weeds, lessening agrochemical use, and curtailing adverse effects on nontarget organisms. The direct application of chimeric ODNs or siRNAs to plant cells enables for the generation of technically non-GM organisms. Inevitably, the refinement of promising techniques that are not currently destined to assign insect resistance, such as RNA manipulation with pentatricopeptide repeat proteins, the use of polygalacturonase-inhibiting proteins (PGIPs), ribozymes, and riboswitches, will likely expand the defence mechanism against pests available to researchers and farmers. However, thanks to recent advancements like RenSeq and directed molecular evolution, which enable the rapid identification of novel immune receptor genes, the pool of deployable genes for enhanced resistance to other microorganisms has grown significantly. In the near future, developments in molecular stacking and targeted gene insertion by genome editing are projected to predominate in establishing broad-spectrum resistance against both viral and nonviral diseases. Moreover, increasingly diverse, accurate, and economical genome-editing techniques like CRISPR-Cas allow for precise change of endogenous genes for disease resistance, such as susceptibility and decoy genes. On the flip side, the persistent cultivation of herbicide crops is estimated to have resulted in the resurgence of herbicide resistance in numerous weeds. Nevertheless, by benefiting from the shortcomings of the issue, the adverse consequences of herbicide-based technologies might be substantially minimised by introducing variety in weed control using alternative approaches, with an emphasis on crop rotation, herbicide rotation, and herbicide formulations. Stress-resistant plants may be generated with or without tissue culture by using simple knock-in, knock-out, replacement, fine-tuning of gene regulation, and point mutations at any gene locus. Although genome editing technology is in in its beginning phases, disruptions in specific genes can have unintended

#### **Table 8.**

*List of globally approved and released transgenic crops for various characters.*

negative consequences for plant growth and development. More research is needed to fully exploit the ability of the CRISPR-Cas System in regulating abiotic stress. One rationale could be that the genes governing beneficial qualities in crops are largely


**9. Life case studies of the use of PAAs in institutions**

In an attempt to simplify the conceptual complexities of PAAs, a select number of themes are included with life case studies. It is my hope that such an approach will enable readers to better internalize some of what has been accomplished and relate these accomplishments to their respective and unique themes.


**Leonel Pereira**

Acknowledgements

This work has the support of Fundação para a Ciência e Tecnologia, through the strategic projects UIDB/MAR/04292/2020 and UIDP/50017/2020+UIDB/50017/2020 granted to MARE and CESAM, respectively. Ana Marta Gonçalves acknowledges the

University of Coimbra for the contract IT057 18-7253.

MARE—Marine and Environmental Sciences Centre, Faculty of Sciences and Technology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal

### **Ana Marta Gonçalves**

MARE—Marine and Environmental Sciences Centre, Faculty of Sciences and Technology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal

> Department of Biology and CESAM, University of Aveiro, Aveiro, Portugal