*2.2. Software Design*

According to the hardware frame in Figure 1, the software of monitoring system includes three parts: (i) the sink node software. Receiving the data sent by each sensor node, starting the positioning module to obtain the positioning information, configuring the cloud platform, and compressing and sending the received data and so on must be completed by the software of the sink node. Before the sink node sends data, the cloud platform must be configured according to the actual situation. Then the data sent by the sink node can be stored in the cloud platform. The cloud platforms of different NB-IoT companies are different. People need to refer to the user manual for configuration. (ii) the sensor node software. The main task of sensor node software is to start each sensor on the node to make them work normally, then collect their data and send them to the sink node. (iii) the monitoring center software. The software of the monitoring center needs to download temperature, humidity and positioning information from the cloud platform and store them in the database. In addition, it also needs to display the location information on the map and draw the temperature and humidity curve.

#### 2.2.1. Software Design of Sink Nodes

The software design of sink node mainly focuses on four subprograms: NB-IoT subprogram, RFID subprogram, BDS/GPS subprogram and data compression subprogram. The main program flow chart of sink node is shown in Figure 4. After the initialization of each module, the main program will be looped in each module subprogram to deal with each module in real time. The watchdog is added to the main program to reset the program to prevent the program from getting stuck or running away.

OneNET platform is a NB-IoT cloud platform developed by China Mobile Communication Company. It can communicate with multiple sink nodes and can read data from multiple sink nodes at the same time by multithreading. We should login in the platform "https://open.iot.10086.cn (accessed on 20 January 2022)" to register the device name. Then add the objects for each device, as well as the number of points and properties of each object. In our experiment, there are three objects in each device, namely temperature, humidity and position information. Finally, the data type of each object should be described. For example, the data type of temperature is 31 bits floating point. According to the Internet Protocol for Smart Objects (IPSO) Alliance Technical Guideline, the longitude, the object ID of latitude, humidity and temperature is 3300, 3303 and 3304 separately. The instruction

format is described detailly in the manual [53]. After the NB-IoT module is connected to the OneNET platform, the platform will record the life cycle of the sink device (the life cycle is configured 3600 s in the initialization). When the life cycle expires, the OneNET platform will issue a life cycle update request, and the sink node can update the life cycle. Or the sink node can actively update the life cycle before the life cycle expires. In this paper, the life cycle is automatically updated, and the life cycle update flag is activated by setting a certain time through the timer. During the life time, the NB-IoT and cloud platform can communicate. The NB-IoT subprogram is shown in Figure 5. At first, the timer will judge whether it exceeds 3600. If yes, the NB-IoT module will be initialized. If no, it continues to judge whether the receive flag bit is 1. If it is 1, it means that the reception is completed, and the data needs to be sent and the receive flag should be cleared and over. Otherwise, the life cycle flag will be judged, if it is 1, it means the time is over and the new request of update the life cycle should be sent. The two flags are active in timer interrupt and UART interrupt respectively.

**Figure 4.** Main program flow chart of sink node.

**Figure 5.** The Flowchart of NB-IoT subprogram.

One sink node should receive data from several sensor nodes. How to receive data from multiple sensor nodes efficiently and successfully? Here, a polling mechanism is proposed. As Figure 6 shown, the sink node sends 'read request' to sensor node1, sensor node2, and sensor node3 in turn. If sensor node1 send 'answer request' in time, then the data of sensor node1 will be allowed to send, the sink node will receive the data from sensor node1. Then next sensor node. The RFID module cc1101 in sink node accesses a sensor node every second in turn. The response timeout mechanism of the sub node is set up to avoid the data transmission errors due to the fact that the sub node is not in the transmission range and does not respond when the sink node sends a request.

**Figure 6.** The flowchart of RFID subprogram of sink node.

RFID initialization mainly refers to the function configuration of cc1101 chip in RFID module. Specifically, through SPI communication mode, we can read and write the internal register of cc1101, so as to complete the setting of fundamental frequency, modulation and demodulation mode, baud rate, packet length and other related parameters. For one of the two general digital output pins with settable functions contained in cc1101, the level jump from low to high occurs when receiving data. The data can be received and read by configuring the rising edge of the I/O port of MCU to trigger interrupt. The flowchart of RFID subprogram is shown Figure 6.

BeiDou System is a positioning and navigation system independently developed by China. The positioning accuracy of BDS can reach 2.5 m in the Asia Pacific region and 10 m in the world. The test accuracy is 0.2 m/s; Timing accuracy is 10 ns. It also has its unique short message communication function. GPS is Global Positioning System. It is well known in the world and developed by the United States. We use BDS/GPS dual system for positioning to obtain higher positioning accuracy. The positioning principle is as follows:

$$\begin{array}{l} \rho\_B^{(m)} = r\_B^{(m)} + E\_B^{(m)} + i \ast \delta\_{\mathfrak{t}\_B} + j \ast \delta\_{\mathfrak{t}\_G} - \mathfrak{r}\_B^m + I\_B^m + T\_B^m + \mathfrak{e}\_B^m\\ \rho\_G^{(n)} = r\_G^{(n)} + E\_G^{(n)} + j \ast \delta\_{\mathfrak{t}\_G} + i \ast \delta\_{\mathfrak{t}\_B} - \mathfrak{r}\_G^n + I\_G^n + T\_G^n + \mathfrak{e}\_G^n \end{array} \tag{1}$$

where, *ρ*, *r*, *E*, *δt,τ*, *I*, *T* respectively represent the pseudo range measurement value of the receiver to a star, the real distance, ephemeris error, receiver clock error, satellite clock error, ionospheric delay, tropospheric delay and pseudo range measurement noise. Superscript *m*, *n* denotes different satellites. Subscripts *B* and *G* indicate different satellite systems. *B* represents Beidou and *G* represents GPS. *i* = 1, *j* = 0. If (*x*, *y*, *z*) is used to represent the

position coordinates of the unknown receiver, and (*x(n)*, *y(n)*, *z(n)*) is used to represent the position of satellite *n*, then *r*(*n*) is equal to the following expression.

$$r^{(n)} = \sqrt{\left(\mathbf{x} - \mathbf{x}^{(n)}\right)^2 + \left(y - y^{(n)}\right)^2 + \left(z - z^{(n)}\right)^2} \tag{2}$$

In Expressions (1) and (2), *ρ*, *τ*, *I*, *T* and the position of satellite can be calculated by original observation, navigation message and corresponding model. If the pseudo range measurement noise is ignored, five unknown parameters need to be solved, namely, the position of the receiver, the BDS clock difference of the receiver and the GPS clock difference ((*x*, *y*, *z*)> *δtB ,δtG* ). Define error correction pseudo range measurements *ρ* (*n*) *c* is as Expression (3) shown.

$$
\rho\_{\varepsilon}^{(m)} = \rho^{(n)} + \tau^{(n)} - I^{(n)} - T^{(n)} \tag{3}
$$

Then, the pseudo range observation equation of BDS/GPS dual system can be expressed as Expression (4):

$$\begin{aligned} \rho\_{c,B}^{(m)} &= \sqrt{\left(\mathbf{x} - \mathbf{x}\_{B}^{(m)}\right)^{2} + \left(y - y\_{B}^{(m)}\right)^{2} + \left(z - z\_{B}^{(m)}\right)^{2}} + i \ast \delta\_{t\_{B}} + j \ast \delta\_{t\_{G}} \\ \rho\_{c,G}^{(n)} &= \sqrt{\left(\mathbf{x} - \mathbf{x}\_{G}^{(n)}\right)^{2} + \left(y - y\_{G}^{(n)}\right)^{2} + \left(z - z\_{G}^{(n)}\right)^{2}} + j \ast \delta\_{t\_{B}} + i \ast \delta\_{t\_{G}} \end{aligned} \tag{4}$$

Linearize Expression (4) through the first-order Taylor expansion to obtain the linearized matrix equation as Expression (5) shown.

$$G \begin{bmatrix} \Delta x \\ \Delta y \\ \Delta z \\ \Delta \delta\_{t\_B} \\ \Delta \delta\_{t\_C} \end{bmatrix} \tag{5}$$

where,

$$G = \begin{bmatrix} -I\_{B,K-1}^{(1)} & -p\_{B,K-1}^{(1)} & -q\_{B,K-1}^{(1)} & 1 & 0 \\ -I\_{B,K-1}^{(2)} & -p\_{B,K-1}^{(2)} & -q\_{B,K-1}^{(2)} & 1 & 0 \\ & \dots & \dots & \dots & \dots & \dots \\ -I\_{B,K-1}^{(m)} & -p\_{B,K-1}^{(m)} & -q\_{B,K-1}^{(m)} & 1 & 0 \\ -I\_{G,K-1}^{(m+1)} & -p\_{G,K-1}^{(m+1)} & -q\_{G,K-1}^{(m+1)} & 0 & 1 \\ & \dots & \dots & \dots & \dots & \dots \\ -I\_{G,k-1}^{(m+n)} & -p\_{G,k-1}^{(m+n)} & -q\_{G,k-1}^{(m+n)} & 0 & 1 \end{bmatrix} \tag{6}$$

$$b = \begin{bmatrix} \rho\_{c,B}^{(1)} - r\_{B,k-1}^{(1)} - i \ast \delta\_{l,B,k-1} - j \ast \delta\_{l,G,k-1} \\ \rho\_{c,B}^{(2)} - r\_{B,k-1}^{(2)} - i \ast \delta\_{l,B,k-1} - j \ast \delta\_{l,G,k-1} \\ \dots \\ \rho\_{c,B}^{(m)} - r\_{B,k-1}^{(m)} - i \ast \delta\_{l,B,k-1} - j \ast \delta\_{l,G,k-1} \\ \rho\_{c,G}^{(m+1)} - r\_{G,k-1}^{(m+1)} - j \ast \delta\_{b,k-1} - i \ast \delta\_{l,G,k-1} \\ \dots \\ \rho\_{c,G}^{(m+n)} - r\_{G,k-1}^{(m+n)} - j \ast \delta\_{b,k-1} - i \ast \delta\_{l,G,k-1} \end{bmatrix}\_{0} \tag{7}$$

 −*I* (*m*) *B*,*k*−1, −*p* (*m*) *B*,*k*−1, −*q* (*m*) *B*,*k*−1 is the directional cosine of the observation vector from the receiver to satellite m. Using the principle of weighted least squares to solve Expression (5), continue to use Newton iterative algorithm to solve the location result.

In order to reduce the utilization rate of CPU, the positioning data of BDS/GPS is transmitted to memory of MCU by UART port through DMA mode every second. The UART idle interrupt is triggered after all the positioning data are sent successfully, and the MCU responds to the interrupt to read the data in DMA for subsequent processing. The process of interrupt response program is similar to NB-IoT. It is not described in detail. The subprogram flowchart of BDS/GPS is given in Figure 7. After the BDS/GPS module parameters are set successfully, we can use the serial port assistant to read the module positioning information.

**Figure 7.** The flowchart of BDS/GPS subprogram.

Data compression subprogram is used to compress data redundancy, reduce power consumption of sink node and improve data transmission efficiency. According to the characteristics of small amount of data transmission and good local time correlation of the system studied in this paper, the wireless remote data compression system designed includes sequence correlation packet processing and Adaptive Optimal Zero Suppression (AOZS) compression. Correlation grouping processing refers to the grouping rearrangement of the original sequence according to the transmission data format to obtain *n* groups of incremental subsequences with good time correlation. Then AOZS compression is performed on each subsequence to eliminate the time redundancy in the sequence.

Adaptive optimal zero suppression (AOZS) compression algorithm is improved from differential code compression algorithm, which is suitable for the compression of incrementally sorted data sequences. AOZS algorithm reduces the number of coded data and removes the time redundancy in the original sequence by eliminating zeros and encoding the zero eliminational factor, and selects the best coding bits by calculating the compressed data length to shorten the length of the final coding. Assume that the data sequence collected by the sink node in an upload cycle can be expressed as *D*.

$$D = \begin{bmatrix} d\_1 \\ d\_2 \\ \vdots \\ \vdots \\ d\_m \end{bmatrix} = \begin{bmatrix} v\_{11} & \cdots & v\_{1n} \\ \vdots & \ddots & \vdots \\ v\_{m1} & \cdots & v\_{mn} \end{bmatrix} \tag{8}$$

where, *m* is the acquisition times of sensor terminal in an upload cycle; *di* is the data sequence value collected for the *i*-th time; *n* is the number of measurement parameters; *vjk* is the value of the *k*-th measurement parameter in the *j*-th data acquisition. There are *N* data that appear only once, and the minimum data is recorded as α, the maximum difference between adjacent data is recorded as *β*. When in data compression, the relevant bit factor {*r*1,*r*2,...,*rm*} of the original data sequence should be recorded at first:

$$r = \begin{cases} 1, \, d\_i = d\_{i-1}, i = 2, 3, \dots, m \\ 0, \, d\_i > d\_{i-1}, i = 2, 3, \dots, m \end{cases} \tag{9}$$

The relevant bit factor records the repetition of sequence adjacent data. By default, the relevant bit factor *r*1 of *d*1 = 0. The coding bit factor *Cx* is determined by the minimum binary coding bit factor *Cα* and the maximum difference binary coding bit factor *Cβ* of the sequence as Expression (10).

$$\mathbb{C}\_{\mathbf{x}} = \begin{cases} \mathbb{C}\_{\mathcal{S}\prime} & \mathbb{C}\_{\mathbf{a}} \le \mathbb{C}\_{\mathcal{S}} \\ \mathbb{C}\_{\gamma\prime} & \mathbb{C}\_{\mathbf{a}} > \mathbb{C}\_{\mathcal{S}} \end{cases} \tag{10}$$

Here, *Cγ* ∈ *Cβ*, *Cα* . Then, the maximum value that can be represented by a set of *Cx* bit codes *dx* = 2*Cx* − 1. 3-bit binary is used to record the encoded bit information in AOZS algorithm. 000, 001, ... , 111 means using 2, 3, ... , 9 bits binary to coding *di* respectively. The relationship of *Cx* and *di* is shown in Table 1. The relationship of coding length *Lx* and coding bit factor *Cx* is shown as Expression (11).

**Table 1.** The relationship of bit numbers, code, *Cx* and *di*.

$$L\_x = \left( \left[ \frac{a}{d\_x} \right] + N - 1 \right) \mathbf{C}\_x + M + 3 \tag{11}$$

where, [ ] means to take up as an integer. *M* is the numbers of {*d*1, *d*2,..., *dm*}, notes the minimum of *Lx* as *Lmin*, and its corresponding code bit factor *Cx* is the best code numbers, noted as *Coptimal.* Zero elimination operation refers to subtracting an integer value (recorded as zero elimination factor) from all data of the sequence, and finally making all data of the original sequence become 0. The zeroing factor *si* of the *i*-th order is recorded as Expression (12).

$$s\_i = \min\left\{d\_{\min}, d\_{\text{optimal}}\right\} \tag{12}$$

where, *dmin* is the minimum value of sequence.

After the above parameters are determined, the algorithm records the sequence coding information and related information, and uses *Coptimal* bit number binary to encode the all zero elimination factors. The flow chart is shown in Figure 8. Table 2 shows the implementation process of the AOZS algorithm.

**Figure 8.** The flowchart of data compress algorithm.

**Table 2.** An example of AOZS algorithm.

So, the compression code is 100,001001,1111,0100,0110,0010.

Compression Ratio (CR) is used to describe the efficiency of compression.

$$CR = \frac{S\_{CP}}{S\_{OR}}\tag{13}$$

Here, *SCP* is the amount of compressed data, *SOR* is the amount of original data. Obviously, the smaller the CR, the smaller the proportion of the compressed data to the original data, and the better the compression performance.

#### 2.2.2. Software Design of Sensor Nodes and Monitoring Center

Software of each sensor node is simpler than that of sink node. It includes two parts. One is to read the humidity data and temperature data of the DHT11. The other is to drive the Bluetooth module to send these data to the sink node. The flowchart is given in Figure 9. It needs to complete the initialization of MCU peripherals and related modules. Clock initialization is used to set the working frequency of the system. Timer initialization is used to read temperature and humidity sensor data. Because the single line communication mode of DHT11 does not have a standard communication format, it is necessary to use a timer to simulate the communication sequence to realize the reception and transmission of data. Finally, the cc1101 module should be initialized.

**Figure 9.** The flowchart of sensor nodes.

The monitoring center is developed with C++ language, which mainly realizes the following functions: (i) according to the longitude and latitude coordinates obtained from the cloud platform, it can display the location of the mobile carriage (sink node) in real time; (ii) it can display the real-time temperature and humidity in the carriage; (iii) it can dynamically draw the temperature and humidity change line chart; (iv) Using database to manage the collected data, it can save the historical data for data analysis. Figure 10 is the operation flowchart of monitoring center.

**Figure 10.** The operation flowchart of monitoring center.


*3.2. From Lockdown to Sleep Problems through Mindfulness and Distress*

Table 2 shows the estimated coefficients of the model including the high-level variables (Figure 1). This analysis revealed that lockdown significantly decreased mindfulness, and this, in turn, decreased distress, while distress increased sleep problems. Furthermore, we found three significant indirect effects: from lockdown to distress through mindfulness (b = 0.61, CI = [0.08, 1.64], SE = 0.35, β = 0.07), from lockdown to sleep through both mindfulness and distress (b = 0.51, CI = [0.05, 1.88], SE = 0.36, β = 0.03), and from mindfulness to sleep problems through distress (b = −0.06, CI = [−0.16, −0.01], β = −0.09). All indirect

effects represented full mediation, as time did not significantly affect distress or sleep problems, nor did mindfulness directly affect sleep. Testing the model while removing the covariates did not significantly alter any of the considered paths. Hence, we tested the fit of the simplified model containing only significant paths and no covariate. This showed good fit statistics: χ2(3) = 1.04, *p* = 0.79, CFI = 1.00, TLI = 1.00, RMSEA = 0.01, SRMR = 0.03.

**Table 2.** SEM estimated coefficients for model 1.

Note. b = unstandardized coefficient, CIlower and CIupper = lower and upper 95% bootstrapped confidence intervals of b, SE = standard error, β = standardized coefficient. Significant paths are reported in boldface. Significant level is indicated as follows: \* *p* < 0.05.

**Figure 1.** First model including only the total scores. Continuous arrows represent significant paths, while dotted arrows represent non-significant paths. Standardized coefficients are reported only for significant paths.


*4.2. Stirring Mechanism*

Generally, there are different stirring mechanisms that affect the flow characteristics of the molten bath, including the momentum stirring, the bubble stirring and the electromagnetic stirring. In addition, the buoyancy force arising from the bath temperature gradient and the bath concentration gradient will also make contributions to stir the bath. In the present study, the electromagnetic stirring is assumed to be neglected in this alternating current (AC) EAF since the electromagnetic field induced is limited to the small region around the arcs and has a minor effect on the molten bath flow [28–30]. The stirring caused by the buoyancy-driven flow is always included in the model by applying the Boussinesq hypothesis for the liquid steel phase since it is a natural phenomenon built in any thermal system. As for the rest two stirring mechanisms, that is, the momentum stirring and the bubble stirring, they are the main stirring power provided by the supersonic coherent jet to the refining process, which are valuable and feasible to be investigated using the 3D integrated model proposed above.

In the actual steel refining process, the distance between the burner nozzle exit and the liquid steel bath surface is usually controlled to be within the jet potential core length to guarantee the coherent jet can maintain a relatively high kinetic energy when reaching the liquid steel bath. In this case, the coherent jet is able to push the liquid steel aside to form a cavity and transfer the momentum to the bath simultaneously. Generally, only a portion of the jet momentum can be transferred to a liquid steel bath and further used to generate the stirring. Sano et al. [23] reported that only 6% of the total jet momentum is transferable to the liquid steel and this value has also been proved to be suitable for the current simulation under the given burner operating condition. The stirring generated by this direct momentum transfer is called the momentum stirring. Another critical stirring mechanism during the refining process is the bubble stirring. The decarburization reaction will generate the CO bubbles that float upward together with oxygen bubbles quickly, creating a strong stirring power inside the liquid steel bath due to the bubble-liquid drag force. The CO bubbles will eventually be absorbed by the slag layer and subsequent chemical reactions will occur there. It has been reported that bubble stirring plays a key role in the homogenization of the flow field, therefore the corresponding investigation conducted below is aimed to reveal the impact of those two stirring mechanisms on the development of the flow field.

The comparison of the flow filed with and without the bubble stirring is given in Figure 8. Figure 8a shows the case only considering the momentum stirring. It can be seen that the high momentum transferred from the surface of the jet penetration cavity pushes the liquid steel downward, resulting in a high-speed liquid steel flow in the axial direction of the cavity together with vortexes generated on both sides. The volume-averaged liquid steel velocity in the bath fluctuates most frequently around a value of 0.01425 m/s. Figure 8b shows the case considering both the momentum stirring and the bubble stirring. Because of the bubble stirring, the high-velocity region occurs surrounding the jet penetration cavity instead and the direction of the vortex changes significantly. This is mainly due to the intensive oxidation reactions in that region generating a large number of oxides and the stirring intensity of the momentum transfer became much weaker compared to that of the floating CO and oxygen bubbles. In addition, significant turbulence is generated through bubble stirring in the liquid steel bath as well, which can lead to a better mixing. The volume-averaged velocity of the liquid steel bath is approximately 0.1485 m/s, which is about 10 times as much as the case only considering the momentum stirring. From this comparison, it can be seen that bubble stirring greatly promotes the homogenization of the liquid steel bath and is one of the most important stirring mechanism in the EAF refining stage.

**Figure 8.** (**a**) Momentum stirring without bubble stirring; (**b**) Momentum stirring together with bubble stirring.

The bath mixing efficiency evaluation for both cases was conducted as well and the results are given in Figure 9. A tracer is introduced in the center of the liquid steel bath and the simulations stop once the tracer is fully diffused inside the bath. During the simulation, the area-averaged concentration of the tracer is monitored at three different horizontal planes in the bath to evaluate the mixing time. The vertical distance between those three horizontal planes and the bottom of the furnace is 0.07 m (plane monitor 1), 0.47 m (plane monitor 2) and 0.57 m (plane monitor 3), respectively. Noticed that, plane monitor 1 is close to the furnace bottom surface, which is aimed to avoid the dead zone and guarantee the full diffusion of the tracer in the domain. Figure 9a shows the contours of the in-bath tracer mass fraction variation over time. Figure 9b,c show the molar concentration variation of the tracer over time. When the variation in the molar concentration of the tracer is negligible, the mixing time required for the liquid steel bath to reach full diffusion can be obtained. It can be seen that without the bubble stirring, the mixing time is estimated to be 1665.3 s, which is almost 9.5 times longer than the time needed for the case with the bubble stirring. Therefore, the bubble stirring needs to be considered in the stirring mechanism for the future simulation of the liquid steel flow field.

**Figure 9.** (**a**) In-bath tracer mass fraction over time (with momentum transfer and bubble stirring); (**b**) Mixing time of flow field without bubble stirring; (**c**) Mixing time of flow field with bubble stirring.


**SOFTWARE PRODUCT LINE – ADVANCED TOPIC**

Edited by **Abdelrahman Osman Elfaki**

#### **Software Product Line - Advanced Topic**

http://dx.doi.org/10.5772/2480 Edited by Abdelrahman Osman Elfaki

#### **Contributors**

Camille Salinesi, Raul Mazo, Shahliza Abd Halim, Noraini Ibrahim, Safaai Deris, Dayang Jawawi, Amougou Ngoumou, Marcel Fouda Ndjodo, Rasha Tawhid, Dorina C. Petriu, Yguaratã Cerqueira Cavalcanti, Paulo Anselmo Da Mota Silveira Neto, Ivan Do Carmo Machado, Luanna Lopes Lobato

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*School of Biomedical Engineering, Health and Sciences, Drexel University, Philadelphia*

Biological interfaces and accompanying interfacial processes constitute one of the most dynamic and expanding fields in science and technology such as biomaterials, tissue engineering, and biosensors. For example, in biomaterials, the bio-interfacial processes between biomaterials and surrounding tissue plays a crucial role in the biocompatibility of the layer (Werner, 2008). In tissue engineering, cellular adhesion plays an important role in the regulation of cell behavior, such as the control of growth and differentiation during development and the modulation of cell migration in wound healing, metastasis, and angiogenesis (Hong et al., 2006). Performance of a biosensor is highly dependent on interfacial processes involving the sensor sensing interface and a target analyte. Therefore, quantitative information on the novel and robust immobilization of detector molecules is

Thickness shear mode (TSM) sensors have been used in a variety of studies including interfacial biological processes, cells, tissue and properties of various proteins and their reaction (Cote et al., 2003). Phenomena such as cell adhesion (Soonjin et al., 2006.), superhydrophobicity (Sun et al., 2006, Roach et al., 2007), particle-surface interactions (Zhang et al.,2005), organic and inorganic particle manipulation (Desa et al., 2010) and rheological and interfacial properties of blood coagulation (Ergezen et al. 2007) were studied using TSM sensors. Due to the high interfacial sensitivity of TSM sensors, it has been shown that cell motility can be monitored by analyzing the noise of the TSM sensor response (Sapper et al., 2006). It has also been demonstrated that the number of motile sperm in a semen sample can be assessed in real-time using a flow-chamber integrated with a thickness

The TSM sensor response is affected by the complex nature of the interface. Its response is influenced by the geometrical and material properties of the interacting surfaces such as surface roughness (Cho et al., 2007), hydrophobicity (Ayad and Torad, 2009), interfacial

one the most important aspects of the biosensor field (Kroger et al., 1998).

**1.1 Quantification of Thickness Shear Mode (TSM) sensor response**

Johann Desa, Matias Hochman, Robert Weisbein Hart, Qiliang Zhang, Sun Kwoun,

*School of Biomedical Engineering, Health and Sciences, Drexel University, Philadelphia*

shear mode sensor (Newton et al., 2007).

Piyush Shah and Ryszard Lec

 \*

*USA*

**1. Introduction**

**Using Thickness–Shear Mode Sensors**


*3.1. D Ray Launching Simulations*

The specific scenario under analysis is shown in Figure 12a, and it corresponds to the zoomed area shown in Figure 10 (i.e., measurements zone). The scenario has been created for simulations by the 3D Ray Launching tool. All the materials as well as the real distances and sizes of the surrounding trees have been taken into account in order to obtain accurate estimations of the RF power distribution and therefore, the path loss. Both the material properties and simulation parameters are the same as those used for the isolated tree case, and can be seen in Tables 1 and 2, except the transmitted power, which in this case was 8.35 dBm.

**Figure 10.** Location of the Orgi Forest and its different zones, including the measurement zone (zoomed). Extracted from Google maps.

**Figure 11.** Picture of *Tomaszelaieta* zone within the Orgi Forest.

**Figure 12.** (**a**) Picture of the measurements zone. (**b**) The scenario created for the simulations. (**c**) Detail of the position of the dense vegetation wall.

Figure 12b shows the schematic view of the created scenario for simulation purposes. As can be seen, the morphology and size of each of the single trees surrounding the tree under analysis have been taken into account. In the same way, a specific wall has been created in the scenario in order to model the thick in-leaf trees background that exists behind the tree under analysis (the wall in green in Figure 12c). This wall has been defined with the material Foliage (see Table 1), while the rest have been defined as air.


**4.3 Effect of the mixture: Alginate + FeCl3**

#### *4.3.1 Alginate alone*

The study of the elimination of the pollution of leachate by coagulation flocculation by the alginate alone is given in **Figure 14**. The results obtained showed that the optimal dose of alginate for the elimination of COD and the turbidity are respectively 60 and 75% with an optimal dose of 120 mg/l of alginate, while producing a small quantity of sludge (20 ml/l).

The effect of pH on alginate flocculation coagulation (**Figure 15**), showed that the latter has little effect on the removal of COD and turbidity. For a pH between

**Figure 14.** *Effect of alginate alone on COD removal and turbidity Turb = 125 NTU.*

*Reducing Pollution of Stabilized Landfill Leachate by Mixing of Coagulants and Flocculants… DOI: http://dx.doi.org/10.5772/intechopen.97253*

#### **Figure 15.**

*Effect of pH on the reduction of turbidity and COD by alginate (120 mg/l).*

1 and 10, no sludge production was observed for Alginate concentrations below 800 mg/l, while the volume of sludge increases from pH 10.

Furthermore, the study of the elimination of pollution from leachate discharges by the mixture of FeCl3 and alginate is given in **Figure 16** (fixed alginate: 120 mg/l and variable FeCl3).

The results obtained showed that the mixture of FeCl3 and Alginate has an optimal concentration of around 120 mg/l of Alginate and a variable concentration of FeCl3 increases the COD from 2900 to 1100 mg/l with a yield 60% COD 65% turbidity. The COD value decreases with the increase in the FeCl3 concentration. As for the variation in turbidity, this remains negligible (30 NTU) from a concentration of 120 mg/l of alginate. The amount of sludge formed increases with an increase in alginate. The optimal concentrations for the removal of COD and turbidity by Alginate vary around 20 mg/l with a FeCl3 concentration of 2500 mg/l at pH 6.5.

**Figure 16.** *Study of the mixture: Alginate (120 mg/l) + variable FeCl3.*

**Figure 17.** *Study of the mixture FeCl3 (2500 mg/l) + alginate.*

**Figure 18.** *Variation of turbidity (a) and COD (b) during coagulation flocculation by flocculants supplied by an Italian company.*

*Reducing Pollution of Stabilized Landfill Leachate by Mixing of Coagulants and Flocculants… DOI: http://dx.doi.org/10.5772/intechopen.97253*

Furthermore, **Figure 17** illustrates the effect of Alginate on flocculation coagulation while keeping a fixed concentration of FeCl3 (2500 mg/l).

The results showed that the Alginate and FeCl3 mixture has an optimal concentration of around 2500 mg/l, which resulted in very high COD removal with a 50% removal efficiency. The COD value increases with that of the Alginate The turbidity which varies around 30 NTU and remains constant from 50 mg/l of Alginate, while the volume of sludge produced increases with the increase of the Alginate stabilizes around 125 ml/l. The optimum concentration of Alginate is 120 mg/l. COD and turbidity values for optimum FeCl3 and Alginate values show values of 1360 mg/l and 30 NTU, respectively.

#### **4.4 Effect of flocculants chemec 1, 2 and 3 supplied by an Italian company**

We carried out a comparative study on the elimination of pollution from leachate using three flocculant (Chimec 1, 2 and 3) supplied by an Italian company. The results (**Figure 18a**) showed that the turbidity decreases from 190 to 40 NTU for Chimec 1,2, for an optimal concentration of 4 mg/l then increases to 100 NTU for 30 mg/l of Chimec1 and 2. As regards the flocculant Chemic 3, the results showed a final elimination of the turbidity which is around 60 NTU for the same optimal concentration (4 mg/l) while the turbidity remains stable for Chimec 3 for a concentration of 10 mg/l.

As for the COD content (**Figure 18b**) during the treatment with the three flocculants, the results showed a decrease up to 1500 mg/l for 4 mg/l of flocculant, with an elimination efficiency of 40%, then increased for Chimec 1 and 2 arriving at around 2000 mg/l for 30 mg/l of flocculant. The COD remains almost stable for Chimec3 from 4 mg/l. In fact, no significant difference was observed as regards the elimination of COD by the three flocculants while producing a small amount of sludge in the case of the three flocculants.


**12. Deciphering a potentially disease‐related IgG ITS**

#### **12.1. MS‐specific targets? A puzzling problem**

From an historical point of view, the quest for an Ig target in MS has followed the methodol‐ ogy successfully used to solve the paradigmatic problem of subacute sclerosing panence‐ phalitis (SSPE), where it was demonstrated that the majority of intrathecal IgG was virus‐ specific. Unfortunately, MS CSF demonstrated weak and inconsistent interaction with CNS antigens in numerous earlier studies. It was thereafter proposed that IgG production in MS may be due to nonsense antibodies [71]. These over‐hasty conclusions should now be miti‐ gated.

CSF Igs constitute a private repertoire that may harbor toxic properties [216]. Functional electrophysiological modifications of neurotransmission after CSF application were observed long ago. Patterns of demyelinating lesions containing Ig and complement deposition in and around macrophages are typical of pattern 2 observed in MS [148, 217, 218]. The application of CSF to cultured cells (rat cerebellar granule neurons) significantly labels the axonal surface with IgM dots [118] and purified antibodies against myelin/oligodendrocyte glycoprotein (MOG) from MS serum bind to intact myelin in rat [219]. Various recombinant antibodies (rAb) synthesized from CD138+ CSF B‐cells of MS patients stain most of the glial components [220, 221] and many specific antibodies have been shown to react against cultured oligodendrocytes or human CNS tissue [222]. In animal models, anti‐MOG antibodies purified from human MS serum strongly enhance lesions without increasing inflammation [219, 223]. In a model of focal implantation of hybridoma cells secreting Ig against myelin in rat spinal cord, demyelination occurred around the implantation site [224]. Moreover, the implantation of anti‐GalC hybrid‐ oma cells may inhibit myelination in a complement‐dependent pathway both in vivo and in vitro [225, 226]. These latter models elegantly combine focal IgG synthesis with focal anatom‐ ical lesions and are reminiscent of the infiltration by plasma cells inside non‐remyelinated plaques.

In conclusion, the hypothesis that CSF Ig could play an active role in brain dysfunction, both at functional and at anatomical levels, is largely justified.

#### **12.2. Does the microheterogeneity of ITS translate into disease heterogeneity?**

inflammatory disorders [194, 213]. An experimental demonstration was given by a series of MS patients who developed CSF IgG against TT after tetanus toxoid vaccination [196]. According to this assumption, the recruitment of intrathecal antibody‐secreting cells should be progressive and the complete MRZ pattern might take years or even decades to achieve. This was observed in clinical studies where the mean age at spinal tap and disease duration correlated with the number of elevated antibody indices [39]. Half of the patients having fewer than 5 years of disease duration had no elevated AI (MRZ), whereas 70% of those with more than 10 years duration had AI ≥2 [39]. A similar trend was observed in a study based on 17

From an historical point of view, the quest for an Ig target in MS has followed the methodol‐ ogy successfully used to solve the paradigmatic problem of subacute sclerosing panence‐ phalitis (SSPE), where it was demonstrated that the majority of intrathecal IgG was virus‐ specific. Unfortunately, MS CSF demonstrated weak and inconsistent interaction with CNS antigens in numerous earlier studies. It was thereafter proposed that IgG production in MS may be due to nonsense antibodies [71]. These over‐hasty conclusions should now be miti‐

CSF Igs constitute a private repertoire that may harbor toxic properties [216]. Functional electrophysiological modifications of neurotransmission after CSF application were observed long ago. Patterns of demyelinating lesions containing Ig and complement deposition in and around macrophages are typical of pattern 2 observed in MS [148, 217, 218]. The application of CSF to cultured cells (rat cerebellar granule neurons) significantly labels the axonal surface with IgM dots [118] and purified antibodies against myelin/oligodendrocyte glycoprotein (MOG) from MS serum bind to intact myelin in rat [219]. Various recombinant antibodies (rAb) synthesized from CD138+ CSF B‐cells of MS patients stain most of the glial components [220, 221] and many specific antibodies have been shown to react against cultured oligodendrocytes or human CNS tissue [222]. In animal models, anti‐MOG antibodies purified from human MS serum strongly enhance lesions without increasing inflammation [219, 223]. In a model of focal implantation of hybridoma cells secreting Ig against myelin in rat spinal cord, demyelination occurred around the implantation site [224]. Moreover, the implantation of anti‐GalC hybrid‐ oma cells may inhibit myelination in a complement‐dependent pathway both in vivo and in vitro [225, 226]. These latter models elegantly combine focal IgG synthesis with focal anatom‐ ical lesions and are reminiscent of the infiltration by plasma cells inside non‐remyelinated

In conclusion, the hypothesis that CSF Ig could play an active role in brain dysfunction, both

at functional and at anatomical levels, is largely justified.

**12. Deciphering a potentially disease‐related IgG ITS**

**12.1. MS‐specific targets? A puzzling problem**

antigens [197].

72 Trending Topics in Multiple Sclerosis

gated.

plaques.

Although the bulk of ITS targets unknown antigens, some of the antibody clones are thought to play an active biological role that could be either deleterious or beneficial. For example, occasional cases of MS may display anti‐N‐methyl‐D‐aspartate receptor (NMDAR) (<1%) or anti‐MOG (12%) antibodies, which are associated with cognitive and demyelinating features [227]. However, until now and unlike the anti‐aquaporin4 (AQP4) observed in neuromyelitis optica, none of the hundred or so identified antibodies has definitely been associated with the clinical (i.e., epilepsy, amnesia) or pathological distinctive features of MS (i.e., ability to remyelinate). The pathogenicity of these antibodies may be characterized in vitro by the extent of the demyelination or axonal loss obtained in myelinated cultures [216], by specific cell losses such as oligodendrocyte progenitors [228] or by functional alteration of synaptic transmission [89, 228]. For example, IgGs directed against MOG or sulfatide produce a complete myelin loss whereas the axons are preserved. On the other hand, IgG against neurofascine produces both myelin loss and loss of 15–40% of axons [216]. Therefore, at the global level, subsets of patients harbor variable toxicity against myelin or axons owing to one or more different antibody specificities against the targets. In the seminal work of Elliott et al. [216], a third of patients' sera were able to destroy myelin and only 6% targeted axons, but this was probably massively underestimated owing to the limited number of targets examined, fewer than 10. Heteroge‐ neity is also present at the clonal level since different IgG clones targeting the same antigens may display different pathogenicity ranging from null to high. This clonal microheterogeneity is driven by the ability of each clone to fix complement, and, for example, among the many MOG‐specific clones generated in EAE, only some of them also recognize the myelin sheath, and their pathogenicity is driven by complement fixation [187]. On the other end of the Ig spectrum, anti‐MBP IgMs are associated with a more benign course. Interestingly, these Ig target a shared epitope antigen between MBP and the extracellular loop of CD64, which is one of the IgG Fc receptors responsible for the immunologic properties of macrophages [229].

Microarrays using a small subset of autoantigens discriminated immune signatures in the sera of RR, SP, and PP MS patients [84]. Interestingly, immune signatures based on 13 IgG and one IgM also discriminated pathologic patterns I and II. These results assign a main role to Ig patterns in the pathophysiology of axonal loss, and replication in the CSF may be even more informative since paired serum and CSF samples display different patterns [93]. The large‐ scale collection of targeted antigens would help to define the functional state of the intrathecal immune system. Moreover, the availability of techniques able to profile ITS IgG diversity and pathogenicity might help to decipher the crucial problem of predicting the severity of MS.

#### **12.3. Putative target antigens—what do OCBs target inside the brain?**

The targets are often considered to be largely unknown [230] and studies have yielded contradictory results. CSF antibodies were often considered to be nonspecific owing to their lack of specificity against the three major myelin proteins, but this does not preclude any other specificity and studies devoid of a priori may reveal unknown specificities against other membrane proteins, lipids, or glycolipids. The failure to find a major antigen for intra‐BBB‐ synthesized Ig may not relate to a nonsense antibody production but instead may reflect the molecular complexity of the CNS and the presumed antigenic target [71]. By analogy with infectious diseases where the antigenic response is driven by pathogen antigenicity, most strategies have concentrated upon the identification of relevant antigenic targets. Paradoxi‐ cally, from these efforts emerged the unexpected problem of the immense multiplicity and variability of targets.

#### *12.3.1. A priori methods*

In the last 30 years, the paradigmatic representation came by analogy with EAE models triggered by a single antigen vaccination, in line with Koch's postulates. Therefore, it was initially thought that ITS was directed against a (single) putative antigen. Unfortunately, intensive investigations of a wide range of potential targets were based on this misleading conception and failed to demonstrate any definitive association apart from more than 100 protein targets [231–233] and roughly 300 epitopes (6% being non‐peptidic) [234]. Unlike in many other autoimmune disorders (e.g., myasthenia gravis), no study has succeeded in demonstrating any single antibody specificity for MS but few candidate epitopes (i.e., GAGA4) are still awaiting confirmation of their specificity by replication studies and the availability of commercial kits. Similar results have been obtained in SLE where large‐scale clusters of antibodies capture significant information regarding diagnosis and prognosis, whereas limited information is obtained from routinely measured antibodies [235].

#### *12.3.2. Non‐a priori methods*

#### *<IP_ADDRESS>. Phage display*

The phage display random peptide library was used to try to characterize unknown epitopes. Phagotopes identified from a CSF test pool of 10 MS patients were not recognized by the CSF of 55 other MS patients [76], which was interpreted as the demonstration that isolated phagotopes are patient‐ but not disease‐specific epitopes [76]. In a study using a similar design, 6 clones were selected from a CSF of 4 CIS, but panning to 187 MS patients found 0–22% positivity depending on the clone and MS type [236]. In the seminal work by Yu et al. [72], one dominant peptide was isolated from each of five MS CSF. Each peptide was patient‐specific, and in a given CSF, multiple OCBs (but not all) reacted against the peptide under study, confirming a restricted clonal IgG expression in CSF [72]. The specificity of CSF Ig against peptides was 10‐fold higher than from serum, suggesting a local affinity maturation of antibodies.

#### *<IP_ADDRESS>. Generation of recombinant antibodies (rAbs) from clonally expanded plasma cells from CSF*

The major advantages of this technique are as follows: (a) absence of a priori knowledge of the putative targets, (b) revelation of the most representative targeted peptides, and (c) precise identification of the candidate protein. Its main drawbacks are that it samples very few clones from each CSF. The generated rAbs clones bound to neuronal or astrocyte targets but myelin array failed to identify any precise target in most of them [237]. Interestingly, the aligned protein candidates identified among the multiple CSF analysis from the different groups did not overlap, thus suggesting a broad range of specificities [72, 238].

#### *<IP_ADDRESS>. CSF analysis with protein microarrays*

molecular complexity of the CNS and the presumed antigenic target [71]. By analogy with infectious diseases where the antigenic response is driven by pathogen antigenicity, most strategies have concentrated upon the identification of relevant antigenic targets. Paradoxi‐ cally, from these efforts emerged the unexpected problem of the immense multiplicity and

In the last 30 years, the paradigmatic representation came by analogy with EAE models triggered by a single antigen vaccination, in line with Koch's postulates. Therefore, it was initially thought that ITS was directed against a (single) putative antigen. Unfortunately, intensive investigations of a wide range of potential targets were based on this misleading conception and failed to demonstrate any definitive association apart from more than 100 protein targets [231–233] and roughly 300 epitopes (6% being non‐peptidic) [234]. Unlike in many other autoimmune disorders (e.g., myasthenia gravis), no study has succeeded in demonstrating any single antibody specificity for MS but few candidate epitopes (i.e., GAGA4) are still awaiting confirmation of their specificity by replication studies and the availability of commercial kits. Similar results have been obtained in SLE where large‐scale clusters of antibodies capture significant information regarding diagnosis and prognosis, whereas

The phage display random peptide library was used to try to characterize unknown epitopes. Phagotopes identified from a CSF test pool of 10 MS patients were not recognized by the CSF of 55 other MS patients [76], which was interpreted as the demonstration that isolated phagotopes are patient‐ but not disease‐specific epitopes [76]. In a study using a similar design, 6 clones were selected from a CSF of 4 CIS, but panning to 187 MS patients found 0–22% positivity depending on the clone and MS type [236]. In the seminal work by Yu et al. [72], one dominant peptide was isolated from each of five MS CSF. Each peptide was patient‐specific, and in a given CSF, multiple OCBs (but not all) reacted against the peptide under study, confirming a restricted clonal IgG expression in CSF [72]. The specificity of CSF Ig against peptides was 10‐fold higher than from serum, suggesting a local affinity maturation of

*<IP_ADDRESS>. Generation of recombinant antibodies (rAbs) from clonally expanded plasma cells from CSF*

The major advantages of this technique are as follows: (a) absence of a priori knowledge of the putative targets, (b) revelation of the most representative targeted peptides, and (c) precise identification of the candidate protein. Its main drawbacks are that it samples very few clones from each CSF. The generated rAbs clones bound to neuronal or astrocyte targets but myelin array failed to identify any precise target in most of them [237]. Interestingly, the aligned

limited information is obtained from routinely measured antibodies [235].

variability of targets.

74 Trending Topics in Multiple Sclerosis

*12.3.1. A priori methods*

*12.3.2. Non‐a priori methods*

*<IP_ADDRESS>. Phage display*

antibodies.

Basically, protein microarray chips are high‐throughput parallelized enzyme‐linked immu‐ nosorbent assay (ELISA) enabling extensive analysis of the breadth of antibody response. Sera from 17 RR‐MS patients were compared to those from 15 controls on a protein microarray printed with more than 3101 candidate antigens that represented multiple antigen families [231]. Although numerous candidate proteins were identified, no clue was obtained about individual pattern variability. A larger microarray displaying 37,000 tagged proteins was created in which patterns of immunoreactivity differed between individual patients [232]. The main drawback of this screening method is the biased antibody profiling approach, since the technique does not account for posttranslational modifications of proteins and non‐proteic targets.

#### **12.4. Screening new targets associated with chronic CNS inflammation**

Comparison of CSF Ig recognition patterns against normal or MS brain tissue lysates revealed a profile of 16 bands highly predictive of MS, suggesting that new antigens are specifically expressed in inflamed brain [239]. Chronic inflammation was associated with the aberrant expression of carbohydrate markers, which were associated with a high level of specific antibodies in CSF [240].

Development of microarrays targeting myelin proteome or brain lipids allowed the confirma‐ tion of a diverse polyclonal reaction against these non‐protidic targets both in humans and in EAE [84, 113, 241, 242]. Unconventional lipid targeting may be common since the proportion of CSF B‐cells targeting lipids was as high as 27% in a study based on rAb [243] and injection of anti‐sulfatid monoclonal antibody causes more severe EAE [241]. Unfortunately, only a limited number of potential targets were printed on these pioneering microarrays and only a minute number of rAb clones were obtained from a single patient.

Sugar moieties are an integral part of normal glycoproteins and glycolipids. Glycosylation occurs in the intracellular compartment where the first added sugar moieties are further masked in the mature‐released glycoprotein. Such "cryptic" carbohydrate structures are potentially immunogenic as demonstrated in human immunodeficiency virus (HIV)‐1 and cancer models [240]. Finally, an antigen microarray platform was constructed to display a wide range of autoantigens (proteins of the myelin sheath, liposomes of varying lipid composition, and cryptic glycan epitopes) and confirmed that CSF antibodies directed against various non‐ proteinic structures are common in EAE and MS [240]. Although simultaneously targeting a limited number of structures, these pioneering studies opened perspectives for subsequent work based on larger microarrays [244, 245], thereby acknowledging the necessity to include various natural targets such as peptides, lipids, and glycans of both human and foreign origin and their posttranslational modifications. Interestingly, microbe‐derived polysaccharides are targeted by the host immune response and may share cross‐reactivity with normal human glycan structures [245]. In‐depth screening of foreign antigens including viral particles might be fruitful for deciphering a potential complex network of cross‐reactivity (**Figure 6**).

**Figure 6. Overview of antigen microarray principles**. Interesting antigens are selected from libraries of autoantigen peptides or foreign peptides (i.e., EBV proteins), but post‐translationaly modified proteins, lipids, or glycans are suita‐ ble. Dense arrays of antigens are spotted on prepared glass slides. Patient samples (serum, CSF) are dropped on the chip, relevant antibodies hybridize to target antigens, and fluorescent‐labeled antibodies are applied for detection. The specific antibody signature results in a pattern informative of disease (MS vs other) and stages (RR vs SP) and reveals the early dynamics of the antibody response [42, 93]. Future chips may include a large variety of antigens including atypical (i.e., lipids, glycans, oxidized) and foreign antigens (i.e., viral proteins).

#### **12.5. Posttranslational modifications**

About 200 posttranslational modifications for proteins and sharing common properties have been described to date: PTMs target protein hotspots, mostly exposed on the protein surface, they may occur in physiologic life or in reaction to pathology, and protein modifications are irreversible. Although PTM‐targeting antibodies occurring in vivo have been rarely described until now in autoimmune disorders, they are being increasingly recognized and all of them might exist since most PTMs are targeted by commercially available monoclonal antibodies raised for research purposes. "Normal" antigens may be masked by PTM and may generate new self‐antigens. A growing body of evidence suggests that PTM may play a central role in some inflammatory pathologies such as rheumatoid arthritis [246], and in inflammatory bowel diseases, type I diabetes, and atherosclerosis. Tolerance to self‐antigens may be broken when self‐antigens undergo protein modifications, whereby they are recognized by antibodies and T‐cells as foreign antigens. This kind of molecular (self‐)mimicry might trigger inflammation. Therefore, PTM may also be key players for specific autoantibody recognition in MS. This major hypothesis could account for the failure to uncover specific targets of the intrathecally synthesized antibodies screened using native protein microarrays. Screening of libraries of synthetically posttranslational‐modified peptides mimicking antigenic epitopes may allow the characterization of new targets.

glycan structures [245]. In‐depth screening of foreign antigens including viral particles might

**Figure 6. Overview of antigen microarray principles**. Interesting antigens are selected from libraries of autoantigen peptides or foreign peptides (i.e., EBV proteins), but post‐translationaly modified proteins, lipids, or glycans are suita‐ ble. Dense arrays of antigens are spotted on prepared glass slides. Patient samples (serum, CSF) are dropped on the chip, relevant antibodies hybridize to target antigens, and fluorescent‐labeled antibodies are applied for detection. The specific antibody signature results in a pattern informative of disease (MS vs other) and stages (RR vs SP) and reveals the early dynamics of the antibody response [42, 93]. Future chips may include a large variety of antigens including

About 200 posttranslational modifications for proteins and sharing common properties have been described to date: PTMs target protein hotspots, mostly exposed on the protein surface, they may occur in physiologic life or in reaction to pathology, and protein modifications are irreversible. Although PTM‐targeting antibodies occurring in vivo have been rarely described until now in autoimmune disorders, they are being increasingly recognized and all of them might exist since most PTMs are targeted by commercially available monoclonal antibodies raised for research purposes. "Normal" antigens may be masked by PTM and may generate new self‐antigens. A growing body of evidence suggests that PTM may play a central role in some inflammatory pathologies such as rheumatoid arthritis [246], and in inflammatory bowel diseases, type I diabetes, and atherosclerosis. Tolerance to self‐antigens may be broken when self‐antigens undergo protein modifications, whereby they are recognized by antibodies and T‐cells as foreign antigens. This kind of molecular (self‐)mimicry might trigger inflammation. Therefore, PTM may also be key players for specific autoantibody recognition in MS. This

atypical (i.e., lipids, glycans, oxidized) and foreign antigens (i.e., viral proteins).

**12.5. Posttranslational modifications**

76 Trending Topics in Multiple Sclerosis

be fruitful for deciphering a potential complex network of cross‐reactivity (**Figure 6**).

Citrullination is increased during EAE and MS and the targeted proteins are catabolized faster. Antibodies in rheumatoid arthritis mainly target citrullinated peptides. Methylation of arginine and lysine residues occurs in EAE brains and these PTMs are targeted in lupus erythematosus. Acetylation of intracellular proteins increases as MS evolves and acetylated MBP produces an immunodominant T‐cell epitope in mice that may trigger EAE [247].

Enzymatic glycosylation of protein is a common PTM and polymorphism of N‐glycan‐ branching enzyme (MGAT5) is associated with the risk of MS and spontaneous EAE. The MOG fragment (aa30‐50) bears an N‐glycosylated asparagine at position 31 that is responsible for its antigenic properties and this PTM peptide is mimicked by the synthetic peptide CSF114(Glc) [248]. Another interesting candidate is the glucose‐based antigen GAGA4 [Glc(α1,4)Glc(α)] [249]. Both peptides are associated with specific serum antibodies in many MS patients.

Oxidization generates a large range of by‐products since reactive oxygen species (ROS) are able to oxidize nearly all the biological components according to their oxidation potential as follows:

Considering the wide range of possibilities to modify self‐antigens by oxidation, this process is an attractive candidate to initiate an "oxidative PTM intolerance" [255].

#### **12.6. Designing microarrays of PTM proteins for multiplex screening with antibodies from CSF of MS patients**

Most of the PTM described above are induced by enzymatic catalysis or a chemical reaction occurring under stringent conditions, and their biologic synthesis is not facilitated by the large range of parameters involved. The question is whether it is possible to print large microarrays of antigens able to express all or most of the possible combinations of PTM. Technical consid‐ erations and successful experimentation on a small scale suggest that various PTMs may be successfully applied on immobilized proteins [255, 256] and that the development of com‐ mercial PTM protein microarrays holds much promise for the future.


**Acknowledgements**

We thank the Pennsylvania Game Commission, Pennsylvania Department of Conservation and Natural Resources, Wild Resources Conservation Program, State Wildlife Grants Program, Northeast Association of Fish and Wildlife Agencies, Penn State Altoona, and Wilkes University for financial support of our research. We thank John Young, U.S. Geological Survey, for creating **Figure 1**. In addition, we thank Gregory Turner, Pennsylvania Game Commission, and Dr. Peter Hudson, Huck Institutes, Penn State University, for creative leadership and insight into the interactions of wildlife and parasites.


**1. Introduction**

Article 10 of the Constitution of the Republic of Korea proudly stipulates and guarantees the right to pursue happiness, stating that "everyone has the right to pursue happiness." However, the reality is not so easy because young Koreans living in the flood of the word "happiness" do not feel happy. Statistics Korea [1, 2] surveyed and announced the happiness level of college students, and college students scored 6.1 points out of 10, lower than China with 7.5 points, the United States with 7.2 points, and Japan with 6.2 points. In Korea, welfare policies for young people are insufficient, and the social awareness that efforts should be made to increase the happiness of young people is relatively low [3]. The results of a survey conducted by Dong-A Ilbo and Job Korea on 607 young people aged 20–29 from March 5 to 19, 2021 found out how difficult times young people are having with depression and frustration amid the recent COVID-19 crisis. A total of 501 people (82.5%) answered that they usually experience depression or frustration, and 51.3% of them cited the difficulty in finding employment as the biggest reason.

As 72.5% of high-school graduates in 2020 [4] went on to university, most young people in their 20s in Korea can be said to be college students. However, young people using expressions, such as "Hell Chosun, 88 Dollar Generation, N-Give-up Generation, and Spoon Social Rank Theory" show a sense of despair rather than happiness. It is said to be the most prosperous economic environment since King Dangun (the legendary founder of Korea), but the reality that young people feel is not so [5].

"Hell Chosun" is a new Internet term in Korea that became famous in the 2010s [6]. It is a compound word of "Hell" and "Chosun," meaning "Korea is close to hell and there is no hope at all." It also has the meaning of "Korea is a bad country to live in, comparable to hell."

The term 880,000 Won Generation is derived from the title of the book by Woo and Park [7] and is an expression that refers to the generation of young people who live or plan to live as non-regular workers. According to the authors' analysis, in 2007, people earning about 880,000 won in their 20s were likely to become non-regular workers, so this term was given. This is a term that expresses the economic situation of Italian youth and is reminiscent of the novel "Thousand Euro Generation" (Milleuristi), published in 2005.

N-Give-up Generation is a neologism that refers to the generation of people who have given up N things. It started with the first 3-Things-Give-up generation and expanded to the "N-Things-Give-up generation." 3-Things-Give-up generations are those who have given up on three things, dating, marriage, and childbirth, and 5-Things-Give-up generations are those who have given up on five things, including home and career [8].

"Spoon Social Rank Theory" refers to the idea that individuals in a country can be classified into different socioeconomic classes according to their parents' wealth and income levels, and as a result, success in an individual's life is entirely born from a wealthy family. This theory is believed to have originated from the famous English idiom "Born with a silver spoon in one's mouth." This means "to be born in a wealthy environment" or "to be born with good luck." In the past, European aristocrats often used silver bowls, and nannies could use silver spoons to feed their children to know the wealth of the family [9].

Four out of five young people answered that they had ever thought of wanting to leave Korea. It is said that young people who should be the driving force for national growth and development want to leave the country where they were born and raised. This is the opinion of young people who account for 81% of the respondents [10]. Jeong et al. [10] surveyed 1003 young people aged 18 to 30 in 2017. The following is the reason why there have been times when you have thought of wanting to leave Korea (**Table 1**).

### **Table 1.**

*Reasons for wanting to leave Korea (%).*

University life was not easy due to COVID-19, etc., and finding a job is becoming more difficult. It can be said that it is a matter of national importance to improve the living conditions of college students during a special period and their happiness and satisfaction.


**Author details**

Nahide Seray Kahraman1 and Haluk Hamamci1,2\*

1 Department of Food Engineering, Middle East Technical University, Ankara, Turkey

2 Department of Gastronomy and Culinary Arts, Alanya Hamdullah Emin Pasa University, Antalya, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 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.

*Determining Glucose Isomerase Activity in Different Wine Environments to Prevent Sluggish… DOI: http://dx.doi.org/10.5772/intechopen.100499*


**Appendix A**

**Table A1.** Explained variance and rotated principal component loadings for the first eight nutrient patterns (NPs) identified by the analysis of nutrients.

Absolute loadings greater than 0.3 are shown in bold.

**Table A2.** Median (IQR) nutrient values by nutrient pattern (NP).

**Table A2.** *Cont.*


**1. Introduction**

Since the onset of pandemics, our world has witnessed over 500 million confirmed cases of COVID-19 and over 15 million related (direct and indirect) deaths till date [1]. With the progression of the disease, severe and more complex processes like acute respiratory distress syndrome, cytokine storm, and NETosis may develop [2, 3]. This is the tip of the iceberg. Our knowledge about the disease manifestation is increasing day by day. A wide spectrum of illnesses vary from a simple cold and fever to multisystemic diseases. It has been reported that a hypercoagulable state, damage of renal tubule cells, and heart muscles are also associated with the development of COVID 19 [4–6].

Besides respiratory insufficiency, neurological complications like seizures, loss of consciousness, encephalitis, Guillain-Barre syndrome, acute necrotizing, hemophagocytic lymphohistiocytosis, acute ischemic cerebrovascular syndrome, anosmia, or ageusia as well as neuropsychiatric symptoms like headaches, nausea, dizziness, hallucinations, and depression have emerged as a significant cause for COVID-related morbidity and mortality [6–8]. Of note, these damages may significantly increase the incidence rate of other neurodegenerative diseases and foster dementia (**Figure 1**) [8].

 So far, various studies have been done to establish the link between viral infections and neurodegeneration disease. The most eminent of them is the 1918 influenza pandemic (Spanish flu) which coexisted with an increased rate of encephalitis lethargica, followed by postencephalitic Parkinsonism [10]. In recent times, multiple studies have indicated a possible relation between onset and/or worsening progression of PD and viral infections. Although the detailed mechanism of viral infections–induced neurodegeneration is still unclear, the role of the immune system or the direct effect of zoonosis cannot be overruled. Neurodegenerative diseases like PD and Alzheimer's disease (AD) are

#### **Figure 1.**

*Schematic representation of COVID-19-related symptoms. Primary pathogenesis associated with COVID-19 are shown in inner circle (in red). The outer circle (in blue) depicts the neurological manifestation related to COVID-19.*

*COVID-19 and Its Impact on Onset and Progression of Parkinson's and Cognitive Dysfunction DOI: http://dx.doi.org/10.5772/intechopen.105667*

mainly protein aggregation diseases in which specific proteins, such as α-synuclein (α-Syn) in PD and tau and Aβ peptide in AD aggregate together to form amyloids. Once triggered, the aggregation process begins to spread from cell to cell and continues to form and deposit amyloids that in turn hamper the brain function [9–11]. A detailed study on molecular pathogenesis of the acute and delayed neurological manifestations and establishment of the link between SARS-CoV-2 infections and the development of PD will be helpful to design the new therapeutic approach.

### **2. Brain expression of SARS-CoV-2 receptor and molecular pathogenesis**

The beta-coronaviruses are large enveloped non-segmented positive-sense RNA viruses. Like its related family members MERS-CoV (exploits dipeptidyl peptidase 4), and SARS-CoV-1, SARS-CoV-2 utilizes its specific proteins, in particular, Spike (S) protein, to bind to a number of host proteins (virus receptors) that assist in its entry [12]. Distributions of host receptors on various tissues are generally believed to decide the virus tropisms within the host cell. For an efficient host cell entry similar to SARS-CoV-1, SARS-CoV-2 uses angiotensin converting enzyme-2 (ACE2) type 1 transmembrane receptors as the major docking receptor followed by proteolytic processing of the spike protein by transmembrane protease serine 2 (TMPRSS2) [13]. Targeting of different cell types by the viral protein has been partially attributed to the distribution ACE2 receptors on the endothelial and epithelial cells of the respiratory system, as well as on immune cells. Along with that, expression of ACE2 is widely found in lung parenchyma, vasculature, heart, kidney, and the gastrointestinal tract [14, 15]. Expression of ACE2 receptors is widespread within brain structures, such as the central nervous system, in human brain vessels, pericytes and smooth muscle cells in the vascular wall, hypothalamus, and visual tracts, which are associated with the various neurological symptoms in coronavirus disease 2019 (COVID-19) infection [10, 11, 14]. However, data mining study of human brain single-nuclear RNA sequencing (RNA-seq) data has also found the expression of ACE2 receptors in the choroid plexus and neocortical neurons, in less amount [16]. Even presence of non-canonical SARS-CoV-2 receptors in other brain cell types makes them vulnerable to the virus.

Interaction with viral S protein and ACE2 receptors on the vascular endothelial cells leads to disruption of the blood-brain barrier, resulting in consequent cerebral edema and microhemorrhages. In addition to this, SARS-CoV-2 may expend direct neuronal damage due to the affinity of the spike S1 protein toward ACE2 receptors expressed on neurons. In short, the virus can utilize two possible pathways for invasion into the brain, either through retrograde axonal transport (olfactory route) or by crossing the blood-brain barrier [17]. Furthermore, production of SARS-CoV-2-associated cytokines, such as interleukin (IL)-6, IL-17, IL-1b, and tumor necrosis factor (TNF), are able to disrupt the BBB [18] and could facilitate the viral entry. Even in some studies SARS-CoV-2 has been predicted to induce infection in cerebral endothelial cells as well as inflammation in peripheral vessels [19], but direct evidence has not been far provided. Co-morbidity factors like cardiovascular risk factors and/or pre-existing neurological diseases could alone or in combination with cytokines intensify the rate of BBB permeability [18]. Nonetheless, viruses are able to enter the brain by carried by infected immune cells that also act as a reservoir [20]. Neutrophils T cells and Monocytes, may traffic into the brain through the vasculature, whereas the meninges and the choroid plexus [21], could be considered as entry points for infected immune cells. In COVID-19 loss of smell is considered a frequent

neurological manifestation that is consistent with infection of the olfactory system. The internalization of the virus in nerve terminals by endocytosis, transportation retrogradely, and spread trans-synaptically to other regions of brain, has been studied in other coronaviruses [22]. Detection of ACE2and TMPRSS2 in the nasal mucosa at both RNA and protein levels increases the chance of involvement of olfactory neurons in viral transmission. The hypothalamus could contribute to the dysregulation of the immune cells. In COVID-19, upregulation of cytokines like IL-6, IL-1b, and TNF act as the activators of the hypothalamic-pituitary-adrenocortical (HPA) axis. This HPA axis acts as the center of the systemic immune activity regulation and is activated by BBB dysfunction and neurovascular inflammation [23].

However, apart from ACE2, SARS-CoV-2 can utilize neuropilin-1 (NRP1) and basigin (BSG; CD147) as docking receptors, whereas a variety of proteases such as cathepsin B and L,TMPRSS11A/B, and furin (FURIN) have been shown to promote viral cell entry as well as replication within the host cell [24–26]. Exposure of brain tissue to COVID-19-related injuries like hypercoagulable states, inflammation, hypoxia, immune response (cytokine storm), or dyselectrolytemia is thought to play the main role in the cerebral pathomechanism of viral damage and cause all the neurodegenerative conditions like AD and PD [27].

### **3. Molecular link between COVID-19 and Parkinson's**

Parkinson's disease (PD) is a neurodegenerative disorder, defined as α-synucleinopathy that affects 1% of the population aged above 60 years with an annual incidence of 15 per 100,000 populations. It is a disorder of the central nervous system (progressive loss of dopamine neurons) that mainly affects the motor system, particularly, the nigrostriatal pathway. Therefore, the major PD symptoms include tremor, bradykinesia/akinesia, rigidity, and postural instability. The clinical manifestations of PD also include non-motor symptoms (NMS) such as dementia, anxiety, depression, fatigue, and others [28]. The major pathological features of PD include selective loss of dopaminergic neurons in the substantia nigra pars compacta and aggregation of protein (called Lewy neurites and Lewy bodies) consisting mainly of α-syn proteins present in neurons [29, 30].

Alpha-synuclein (α-syn), is a small protein (forms an α-helix-rich tetramer) that comprises of 140 amino acids, and the human *SNCA* gene encodes them. Expression of α-syn takes place in the central nervous system (CNS) and is mainly localized in synapses and nuclei. Although the exact function of α-syn is not clearly explained yet, various studies have shown that maintaining synaptic plasticity, vesicle trafficking, and interaction with synaptic vesicles as well as physiological regulation of vesicle recycling is regulated by α-syn [30–32]. The major biological function of α-syn is employed through the non-amyloid-beta component (NAC), N-terminal, and C-terminal domains. The KTKEGV motif is present in the N-terminus that maintains tetramerization of α-syn, and mutations in this motif lead to neurotoxicity. NAC is a highly hydrophobic domain and was first identified in patients with AD. It forms a β-sheet structure for α-syn aggregation. The C-terminus of α-syn is a proline-rich region domain that helps in interaction with other proteins [30, 33]. Misfolded or unfolded α-syn protein forms fibrillar aggregates that generate insoluble inclusions in the affected neurons and glial cells. Aggregated α-syn can induce other pathological features, such as mitochondrial dysfunction, dysregulation of calcium homeostasis, endoplasmic reticulum (ER) stress, neuroinflammation, Golgi fragmentation,

#### *COVID-19 and Its Impact on Onset and Progression of Parkinson's and Cognitive Dysfunction DOI: http://dx.doi.org/10.5772/intechopen.105667*

lysosomal dysfunction, and impaired protein quality control that lead to neuronal toxicity [30, 33–35]. The non-neuronal cells present in the brain are called Gila cells that play a critical role in maintenance of the neuronal system. Glia cells are comprised of microglia, astrocytes, and oligodendrocytes in the CNS. The glial cells comprise a majority of brain cells, and they regulate neurogenesis and synaptogenesis. Furthermore, glial cells influence the development and function of brain-blood barrier (BBB) by interactions with endothelial cells and neurons to protect the brain from pathogenic attacks [30, 33]. Although the major function of astrocytes and microglia involves the immune response but under pathological conditions, they seem to be activated by specific stimuli. Upon activation, microglia and astrocytes can release pro-inflammatory cytokines like tumor necrosis factor-alpha (TNF-α), interleukin-1 (IL-1), IL-2, IL-4, interleukin-6 (IL-6), and also causes reduced levels of neurotrophins, like nerve growth factor and brain-derived neurotrophic factor (BDNF) that lead to the reactive oxidative stress (ROS) production followed by BBB dysfunction. Intercellular crosstalk between these factors induces neuronal cell death and engenders neurodegenerative diseases such as AD or PD [30, 33, 36].

In patients with PD/parkinsonism, the COVID-19 pandemic has had an indirect and negative impact that might be explained by the dopamine-dependent adaptation hypothesis. Due to the pandemic, there is a change in daily life and routine; therefore, flexibility in cognitive (and motor) functions is required to adapt to such changes. Even pharmacodynamic effects, social isolation, stress, and anxiety as well as prolonged immobility have detrimental effects on motor and non-motor symptoms and quality of life in PD. In patients with PD, damage to nigrostriatal dopamine neurons results in lower cognitive as well as motor neuron flexibility. Such patients often experience confusion and increased psychological stress, which can lead to the worsening of parkinsonism symptoms as well as mental illnesses such as anxiety and depression [37]. The development of permanent or transient Parkinsonism followed by a viral might occur through different mechanisms:

There are fundamental clinical and anatomopathological differences present in each of these instances [37, 38].

The onset of transient parkinsonism has been associated with many viral infections including West Nile Virus, Japanese Encephalitis, Western Equine virus, Coxsackie virus, Epstein Barr virus, HIV, and currently SARS- CoV-2. Expression of high level ACE2 receptor on the midbrain dopamine neurons could facilitate entry of SARS-CoV-2 that can alter the expression of alpha-synuclein [39–41]. Since elevated alpha-synuclein levels can promote aggregation of the protein, this could predispose an infected patient to PD down the line. Various experimental models also suggest that SARS-CoV-2 may interact with different proteins in age-related pathways (lipid

metabolism, proteostasis, mitochondrial function, and stress responses) [37, 40]. Dysfunction of these pathways could lead to alpha-synuclein aggregation and selective neurodegeneration. Even elevated cytokines (the primary mediators of inflammation in SARS-CoV-2) can accelerate the neurodegeneration in PD [41]. Studies revealed that the release of cytokines may activate the resident immune cells in the CNS. Activation of immune cells leads to their infiltration including activated T cells and microglia from the periphery that may kill neurons, astrocytes, and vascular cell types. Elevated levels of pro-inflammatory cytokines, such as TNF and IL-1beta, are also associated with increased risk of PD [41].

A current study has established the possible mechanism of triggering PD followed by COVID-19 infection. Virus-initiated amyloid-formation of α-synuclein acts as the main cell-toxic agent in the death of dopamine-producing neurons in the brain. By interacting with amyloidogenic regions with nucleocapsid protein (that encapsulates the RNA genome inside the virus), SARS-COV-2 speeds up the formation of amyloid fibrils. In the context of Alzheimer's disease, it has been speculated that amyloid fibrils are formed as an immune response to an infection, and neutralizing pathogens. A similar mechanism may play a role in progression of PD. In a current study, test tube experiments have shown that SARS-CoV-2 spike protein (S-protein) has no effect on α-synuclein aggregation, whereas SARS-CoV-2 nucleocapsid protein (N-protein) considerably speeds up the aggregation process That results in formation of multiprotein complexes and eventually amyloid fibrils that disturb the α-synuclein proteostasis and increase the rate of cell death (**Figure 2**) [42, 43].

As the cases of PD rises sharply in the older age group, particularly in those over the age of 80 years, a personalized approach to the clinical management of PD patients affected by COVID-19 is need of the hour. In addition, disturbance of α-synuclein proteostasis might be considered the first step toward nucleation of fibrils. Direct

#### **Figure 2.**

*Schematic representation of neuroinflammation and neurodegeneration due to SARS-CoV-2 infection. A immunological crosstalk between different organs. Created with BioRender.com.*

interaction between the N-protein of SARS-CoV-2 and α-synuclein establishes a molecular link between virus infections and Parkinsonism. This piece of puzzle thus suggests that SARS-CoV-2 infections may have prolonged implications and consider N-protein as an attractive alternative target in designing novel vaccination strategies.