*Chlamydia: The Female Reproductive System and Infertility DOI: http://dx.doi.org/10.5772/intechopen.111756*

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Section 5


*2.1. Sample Preparation*

Cherry wood (Prunus serotina; identification based on bark and wood), obtained locally in Pittsburgh, Pennsylvania, was selected as a model for this study. This relatively fast growing species is very common throughout the eastern United States and Canada and therefore has potential to be utilized as a biomass source. The wood samples used in this study were prepared by the following method (Figure 1). First, a 16-mm thick slice of wood was cut across the grain (longitudinal cell); second, an approximately 7 mm tangential section was split from the heartwood along an annual ring; and, finally, eighteen pieces having dimensions of about 16 mm × 7 mm × 5 mm (length, width, thickness) were split from the section. Samples were produced by splitting to avoid saw blade damage to the surfaces. The purpose of taking the samples from a single tangential section in the heartwood was to limit the effects of the natural variability of the wood and improve the reproducibility of results. For the eighteen samples (as illustrated in Figure 1), fourteen samples (1–7) (two sets) were used for biochemical composition/cell wall composition (1 set of 7), and proximate analysis and grindability (1 set of 7). Two samples (8–9) were used for the physical properties tests (weight, color, and size), and two samples (10–11) were used for the microstructure SEM analysis.

**Figure 1.** Sample preparation and sample arrangement.


**José Carmelo Maturana-Espinosa 1, Juan Pablo García-Ortiz 1, Daniel Müller 2 and Vicente González-Ruiz 1,***∗*

Received: 26 August 2019; Accepted: 11 September 2019; Published: 13 September 2019

**Abstract:** MCJ2K (Motion-Compensated JPEG2000) is a video codec based on MCTF (Motion-Compensated Temporal Filtering) and J2K (JPEG2000). MCTF analyzes a sequence of images, generating a collection of temporal sub-bands, which are compressed with J2K. The R/D (Rate-Distortion) performance in MCJ2K is better than the MJ2K (Motion JPEG2000) extension, especially if there is a high level of temporal redundancy. MCJ2K codestreams can be served by standard JPIP (J2K Interactive Protocol) servers, thanks to the use of only J2K standard file formats. In bandwidth-constrained scenarios, an important issue in MCJ2K is determining the amount of data of each temporal sub-band that must be transmitted to maximize the quality of the reconstructions at the client side. To solve this problem, we have proposed two rate-allocation algorithms which provide reconstructions that are progressive in quality. The first, OSLA (Optimized Sub-band Layers Allocation), determines the best progression of quality layers, but is computationally expensive. The second, ESLA (Estimated-Slope sub-band Layers Allocation), is sub-optimal in most cases, but much faster and more convenient for real-time streaming scenarios. An experimental comparison shows that even when a straightforward motion compensation scheme is used, the R/D performance of MCJ2K competitive is compared not only to MJ2K, but also with respect to other standard scalable video codecs.

**Keywords:** quantization (signal); video coding; channel allocation; scalable video coding


**8. Conclusions**

India is going to experience a multifold rise in the demand for urban transport in the coming years. A strategic approach is required to ensure that the growth momentum is maintained without adversely impacting the quality of environment to urban dwellers. A holistic planning mechanism consolidating urban transport and land use planning is essential for Indian cities, especially metropolitan cities, so that synergies between urban form and functions can be channelized. This will further augment the productivity and efficiency of cities. The present challenges of congestion, pollution, accidents, sprawl etc. can be mitigated by investing in public modes of transport and optimizing multimodal mobility patterns. Urban transport influences the spatial organization of cities. So, urban transport must be approached in a holistic manner integrating pricing, financing regulation and comprehensive land use.

This chapter suggests that TOD is a necessity in India in view of the urbanization, metropolitanization and motorization trends in cities, the numbers and densities therein, income distribution patterns and considerations of sustainability. The

country cannot afford auto-centric, sprawling, energy-intensive and an expensive process of urbanization. India needs to move to a public-transportation led, transitoriented, mixed use, and value capture financing-focused strategy of planned urban development with public transportation investment and transport-land use integration as the key drivers. This also calls for a robust financing strategy. Successful TOD policy requires a robust and integrated framework bestowed with financial independence, responsiveness and competence. TOD policy can be successful by ensuring transparency and accountability towards the users while augmenting the accessibility of Indian cities.


**6.1 Remote sensing and geographic information systems in disaster management**

Advances in remote sensing tools and techniques over the past few years have provided disaster managers, especially flood disaster managers with powerful tools in the acquisition of flood sense data, in forecasting and monitoring of flood occurrences and in the management of watersheds, rivers and wetland areas.

Remote sensing refers to the Science of obtaining information about objects, areas or phenomena from a distance [42]. Typically, these information are collected through sensors that are planted on aircrafts or satellites. In flood disaster management, remote sensing can be applied to monitor and map events such as changes

**33**

**Table 1.**

*reference to [44].*

*Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review*

in river volume, changes in coastline, map wetlands and flood prone zones and

A Geographic Information System(s) (GISs) refers to a framework for gathering, managing and analysing location-based data. This framework is used to analyse and organize several distinct layers of location-based information into concise visualizations through maps and 3D scenes. Ultimately, GISs present powerful capabilities that proffer deeper insights into data, which may include revelation of patterns and

Reliable flood maps are therefore produced using GIS techniques and remotely sensed data to manage floods. GIS tools aid in the preparations to Digital Elevation Models (DEMs) for high level hydrological modelling using sensors such as The

With the help of data interpretation techniques of GIS, remotely-sensed imageries are interpreted to create suitable flood risk mitigation frameworks and FDSSs. Although flood disasters have increased in scale and frequency in recent years, there has been a commensurate improvement in flood data capturing and analyses techniques, that when applied in time, can significantly mitigate the risks and impacts of floods. As summarised in **Table 1**, GIS and RS are of great importance in the pre

**6.2 Internet of Things (IoT) and Big Data in flood disaster management**

The Internet of Things (IoT) refers to a network of devices connected over the internet to sense, track and respond to issues. Patel and Patel [45] defines the IoT as "*a type of network to connect anything with the internet based on stipulated protocols through information sensing equipment to conduct information exchange and communications in order to achieve smart recognitions, positioning, tracing, monitoring and*

The network of physical objects are able collect data on a regular bases and in a structured form, perform high level analysis and predict changes, as well as initiate actions based on results from the analyses. IoT is hence a powerful technological tool that can provide a wealth of high level intelligence which is needed in planning

There are three levels of IoT. The first is people to people interconnectivity, the second is people to machine interconnectivity and the third being machine to

Flood preparedness As tools for planning evacuation routes. Designing centers for emergency

Planning distribution of relief items to flood victims

Preparation of flood prone maps. Management of large volume of flood

Integrating and Simulating live satellite data with other dataset to inform

*Relevance of GIS and RS*

early warning systems. Flood relief Planning and execution of search and rescue operations.

Rehabilitation planning.

*Showing GIS and Remote Sensing Application in flood disaster management. Source: Authors' Construct with*

Flood Prevention Capturing imageries for hazards and risks assessment.

sense data.

operations.

Flood rehabilitation Flood impact assessment.

*DOI: http://dx.doi.org/10.5772/intechopen.95048*

relationships for smarter decision making [43].

Light Detecting and Ranging (LiDAR) sensors.

and post disaster management processes.

*administration."*

and management.

*Phases in Flood Disaster Management*

boundaries of inundation.

*Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review DOI: http://dx.doi.org/10.5772/intechopen.95048*

in river volume, changes in coastline, map wetlands and flood prone zones and boundaries of inundation.

A Geographic Information System(s) (GISs) refers to a framework for gathering, managing and analysing location-based data. This framework is used to analyse and organize several distinct layers of location-based information into concise visualizations through maps and 3D scenes. Ultimately, GISs present powerful capabilities that proffer deeper insights into data, which may include revelation of patterns and relationships for smarter decision making [43].

Reliable flood maps are therefore produced using GIS techniques and remotely sensed data to manage floods. GIS tools aid in the preparations to Digital Elevation Models (DEMs) for high level hydrological modelling using sensors such as The Light Detecting and Ranging (LiDAR) sensors.

With the help of data interpretation techniques of GIS, remotely-sensed imageries are interpreted to create suitable flood risk mitigation frameworks and FDSSs. Although flood disasters have increased in scale and frequency in recent years, there has been a commensurate improvement in flood data capturing and analyses techniques, that when applied in time, can significantly mitigate the risks and impacts of floods. As summarised in **Table 1**, GIS and RS are of great importance in the pre and post disaster management processes.

## **6.2 Internet of Things (IoT) and Big Data in flood disaster management**

The Internet of Things (IoT) refers to a network of devices connected over the internet to sense, track and respond to issues. Patel and Patel [45] defines the IoT as "*a type of network to connect anything with the internet based on stipulated protocols through information sensing equipment to conduct information exchange and communications in order to achieve smart recognitions, positioning, tracing, monitoring and administration."*

The network of physical objects are able collect data on a regular bases and in a structured form, perform high level analysis and predict changes, as well as initiate actions based on results from the analyses. IoT is hence a powerful technological tool that can provide a wealth of high level intelligence which is needed in planning and management.

There are three levels of IoT. The first is people to people interconnectivity, the second is people to machine interconnectivity and the third being machine to

#### **Table 1.**

*Showing GIS and Remote Sensing Application in flood disaster management. Source: Authors' Construct with reference to [44].*

*Natural Hazards - Impacts, Adjustments and Resilience*

achieved.

The Flood Decision Support System (FDSS) refers to interactive computing environment designed for specific contexts which include interlinked models/ analytical tools, databases, graphical user interfaces and other systems. The FDSSs according to [41] have the potential to improve flood disaster assessment and mitigation through improved data collection and rapid dissemination of flood information to affected areas. For an effective FDSS on the technology aspect of disaster management, analysts have to ensure effective interoperability of the technologies. This will ensure that, all aspects of the technology that singularly may be responsible for data capture, storage, manipulation, analysis, retrieval or display of information, work in a smooth interwoven network and relay information to other parts of the system without technical hindrances to ensure the overall goal is

There are three main components to the Flood Disaster Support System. These

The second component of the FDSS are functions of analytics and modelling. Various analysis are carried out and the data in the database taking through several processes of manipulation. These processes of data manipulation and analysis differ in approach and are tailored to meet various goals in the decision making process. Prominent among the tools used at this stage is Geographic Information Systems (GIS) tools. Regarding flood modelling, advanced tools available to flood managers include advanced technological tools in soft computing, for instance, evolutionary computing, as well as probabilistic predictions techniques of inundation recurrence intervals [41]. These tools afford flood managers varieties of techniques that can be

include the Database component, the Modelling component, and the Display component also known as the Graphical User Interface (GUI) component. The Database component of the FDSS comprises the data used in the modelling functions. This component uses to tools to capture and store flood related data. Some data stored include historical rainfall data, geological data, soil and ecological data, population data, boundary and administrative data. Tools used in data capture for the Database varies depending on the data to be captured. For example, Remote sensing techniques are used to capture satellite data on flood zones, flood buffer zone monitoring. Sensors are also deployed to monitor flow, volume and carrying capacities of rivers while rain gauges capture precipitation volumes. These data may be complemented with census data on population and livelihoods of residents. All

these various data are kept in the Database component of the DSS.

applied in simulation, modelling, analysis and management of flood.

**6.1 Remote sensing and geographic information systems in disaster**

rences and in the management of watersheds, rivers and wetland areas.

The User Interface component of the FDSS provides flood decision makers an interactive graphical interface, enabling users to query the data stored in the system. It again enable users to display and visualise the models and reports from the manipulations of the data. This component of the advanced FDSS enables users to prepare and appreciate maps and animations of the hydrologic phenomena being

Advances in remote sensing tools and techniques over the past few years have provided disaster managers, especially flood disaster managers with powerful tools in the acquisition of flood sense data, in forecasting and monitoring of flood occur-

Remote sensing refers to the Science of obtaining information about objects, areas or phenomena from a distance [42]. Typically, these information are collected through sensors that are planted on aircrafts or satellites. In flood disaster management, remote sensing can be applied to monitor and map events such as changes

**32**

studied.

**management**

machine or things to things interconnectivity [45]. In all interconnectivity of things and people, the internet remains the main driver. This interconnectivity of Things, enables the swift transmission of meteorological, hydrological and geological data pertaining to flood events.

In flood disaster management, providing a quick feedback on the occurrence of floods can be a great step in preventing and mitigating flood disasters and their impact on livelihoods in society. Deploying IoT in flood management puts disaster managers at a position to create enhanced early warning systems that do not only measure the water levels and the speed of inundation, but early warning systems that could also send alerts to residents and flood managers through mobile phones and other personal electronic devices, and additionally, prescribe the best prevention and mitigation strategies based on data such as direction of runoff, speed of rise of water levels and the time at the disposal of residents to take necessary action.

Big Data on the other hand, refers to "*the evolution and use of technologies that provide the right user at the right time with the right information from a mass of data that has been growing exponentially for a long time in our society*" [46]. Digital data collection has not only seen growth in volume but also in variety in storage formats, hence Big Data is often described as high-volume, high-velocity and/or high variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision making and process automation [47].

Big data typically defines data that exceeds the storage, processing and computing capacity of conventional database [46]. Hence Big Data analytics typically involves automated software that assist in the collection, organisation and analysis of the data being generated to discover trends, correlations and other useful results to prompt necessary action.

Through Big data process automation, precipitation data, soil moisture data, temperature data, water content data of water bodies, data on evapotranspiration, ground water data, etc., are collected and processed in real-time without human supervision to make predictions and early warnings about flood disasters' occurrence [47].


**DBD Plasma Combined with Di**ff**erent Foam Metal Electrodes for CO2 Decomposition: Experimental Results and DFT Validations**

**Ju Li 1,**†**, Xingwu Zhai 1,2,**†**, Cunhua Ma 1,\*,**†**, Shengjie Zhu 1, Feng Yu 1, Bin Dai 1, Guixian Ge 2,\*,**† **and Dezheng Yang 2,3,\***

Received: 28 August 2019; Accepted: 22 October 2019; Published: 11 November 2019

**Abstract:** In the last few years, due to the large amount of greenhouse gas emissions causing environmental issue like global warming, methods for the full consumption and utilization of greenhouse gases such as carbon dioxide (CO2) have attracted great attention. In this study, a packed-bed dielectric barrier discharge (DBD) coaxial reactor has been developed and applied to split CO2 into industrial fuel carbon monoxide (CO). Different packing materials (foam Fe, Al, and Ti) were placed into the discharge gap of the DBD reactor, and then CO2 conversion was investigated. The effects of power, flow velocity, and other discharge characteristics of CO2 conversion were studied to understand the influence of the filling catalysts on CO2 splitting. Experimental results showed that the filling of foam metals in the reactor caused changes in discharge characteristics and discharge patterns, from the original filamentary discharge to the current filamentary discharge as well as surface discharge. Compared with the maximum CO2 conversion of 21.15% and energy efficiency of 3.92% in the reaction tube without the foam metal materials, a maximum CO2 decomposition rate of 44.84%, 44.02%, and 46.61% and energy efficiency of 6.86%, 6.19%, and 8.85% were obtained in the reaction tubes packed with foam Fe, Al, and Ti, respectively. The CO2 conversion rate for reaction tubes filled with the foam metal materials was clearly enhanced compared to the non-packed tubes. It could be seen that the foam Ti had the best CO2 decomposition rate among the three foam metals. Furthermore, we used density functional theory to further verify the experimental results. The results indicated that CO2 adsorption had a lower activation energy barrier on the foam Ti surface. The theoretical calculation was consistent with the experimental results, which better explain the mechanism of CO2 decomposition.

**Keywords:** dielectric barrier discharge plasma; foam metal electrodes; CO2 decomposition; density functional theory

#### **1. Introduction**

Recently, the environmental impact of carbon dioxide (CO2) has been extensively studied. The contribution of CO2 to the greenhouse effect has been confirmed, so solutions to the problem of excessive CO2 need to be found. Nowadays, the greenhouse effect originated from CO2 emission is one of the main challenges for human beings. Meanwhile, CO2 is a ubiquitous and universally available C1 (a substance containing one carbon atom) resource in the world. In this regard, three major strategies have been proposed: carbon capture, storage, and utilization [1–5]. As one of the carbon utilization pathways, CO2 direct decomposition has aroused special interest, because it can convert the greenhouse gas into value-added carbon monoxide (CO), which can be used not only as a fuel, but also as a widely used chemical raw material [6].

CO2 thermal decomposition requires temperature excess of 2000 K to form CO and O2 [7]. Hence, this is a high consumption energy process. In recent years, some alternative processes for CO2 decomposition have been developed, among them non-thermal plasma (NTP) is an emerging alternative to CO2 decomposition that has many merits, such as exciting high-energy electrons for reaction under environmental conditions. Meanwhile, the gas temperature of NTP can be very low, which ensures a low energy cost due to reduced heat loss [8]. Therefore, various NTP configurations have been tested, such as corona discharge [9], glow discharge [10], gliding arc discharge [11], microwave discharge [12], and radio frequency discharge [13]. In the near future, dielectric barrier discharge (DBD) plasma will be used in CO2 conversion [14].

DBD is one of the NTPs, which can replace the catalytic chemical process under high-temperature operating conditions. There are numerous microdischarges in the DBD reaction tube. Microdischarges are composed of a number of small current filaments, which are generally short-lived, only a few nanoseconds, and evenly distributed around the high-voltage electrode [15]. In a DBD, the average temperature of energetic electrons is very high, over the range of 10,000–100,000 K, but the actual gas temperature is close to the environment temperature [16]. Except for the generation of high-energy particles, DBD also produces ultraviolet-visible light, ozone species, excited species, radicals, ions, etc. These reactive species are responsible for the efficient initiation and propagation of reactions [17,18].

In general, the DBD reactor performance depends on reactor configuration, flow velocity, power, ambient gas, and catalyst/packing material [19,20]. Hueso et al. [21] conducted a DBD technique to reform methane and directly decompose methanol under normal and low temperature conditions. Depending on the applied voltage, feed ratio, reactants' residence time, or reactor configuration, the conversion can reach 20–80% in the case of methane and 7–45% for CO2. Under similar experimental conditions, methanol direct decomposition is up to 60–100%. Tu et al. [22] observed shifts in CO2 conversion and process energy efficiency by changing DBD plasma processing parameters. Conclusion can be made that increasing the discharge power or lowering the gas flow velocity can improve CO2 conversion with other parameters unchanged, but with lower energy efficiency. Snoeckx et al. [23] conducted extensive and in-depth studies on DBD plasma decomposition of CO2. It can be found that the presence of N2 in 50% of N2 gas mixture has little effect on CO2 conversion and energy efficiency. However, a higher N2 fraction results in a decrease in CO2 conversion and energy efficiency. Michielsen et al. [24] reported CO2 decomposition in a packed-bed DBD reactor packed with glass wool, quartz wool, and SiO2, ZrO2, Al2O3, and BaTiO3 spherical beads of different sizes. Among the many filler materials studied, BaTiO3 has a maximum conversion of up to 25% and an energy efficiency of 4.5%. Uytdenhouwen et al. [25] used a DBD microplasma reactor to investigate the effect of gap size reduction combined with packing material on the conversion and efficiency of CO2 dissociation. The results were compared to a conventional size reactor as a reference. Even though the energy efficiency is low, decreasing the discharge gap can greatly enhance the CO2 conversion rate.

At present, in the literatures we have seen, most of the catalysts used for CO2 decomposition by plasma are metal oxides. However, a few literatures have reported researches on CO2 conversion by using foam metals as catalysts. In this work, we studied DBD plasma junctions and different packed foam metal materials (Fe, Al, and Ti) for CO2 conversion. Compared to metal oxide catalysts, the foam metals can not only serve as a carrier for energy transformation, but the foam metals distributed in the discharge gap also consume a part of O2 and O radicals, so that the reaction proceeds in the positive direction, promoting the decomposition of CO2. The effects of power, flow velocity, and other discharge characteristics are studied to better understand the influence of the filling catalysts on CO2

splitting. Furthermore, we use density functional theory (DFT) to verify the experimental results. The results show that CO2 adsorption has a lower activation energy barrier on the foam Ti surface.

#### **2. Experimental**

#### *2.1. Dielectric Materials*

In this paper, foam metals (Fe, Al, and Ti) are used as filler catalysts along the discharge region. All of these catalysts are commercially available and untreated.

#### *2.2. Experimental Setup*

The schematic diagram of an experimental device for CO2 splitting by DBD plasma is shown in Figure 1. The experimental device includes the following components: plasma generator, DBD reactor (Beijing Synthware glass), and a gas chromatograph (GC) for detecting the product. The coaxial cylindrical reactor has two electrodes, namely a high-voltage (HV) electrode and a low-voltage (LV) electrode, and they produce a discharge between the two electrodes. The HV electrode is a stainless steel rod, 2 mm in diameter, which is connected to an alternating current (AC) power source and fixed on the central shaft of the reaction tube. Condensed water at 20 ◦C is added to the reactor housing and the LV electrode is grounded with a wire. The discharge parameters were measured using a voltage probe, a current monitor, and an oscilloscope. The composition of the off-gas from the plasma reactor was determined by GC. The calculation of the discharge power is based on the area of the Lissajous figure displayed on the oscilloscope.

**Figure 1.** Schematic diagram of the experimental device for CO2 splitting.

#### *2.3. Gas Analysis and Parameter Definition*

Pure CO2 (99.995%) is used as the reaction gas and the inlet feed gas flow velocity was varied from 20 to 100 mL/min through the mass flow controller. After the GC had stabilized, the composition of the gas products was recorded by GC every 15 min. Helium (He) was used as its carrier gas.

CO2 conversion, CO yield, and selectivity are important parameters for evaluating the plasma process performance, as defined below:

$$\text{CO}\_2\text{ conversion } (\%) = \frac{\text{CO}\_2\text{ converted}}{\text{CO}\_2\text{ input}} \times 100,\tag{1}$$

$$\text{CO yield } (\%) = \frac{\text{CO formed}}{\text{CO}\_2 \text{ input}} \times 100,\tag{2}$$

$$\text{CO velocity} \left( \% \right) = \frac{\text{CO formed}}{\text{CO}\_2 \text{ converted}} \times 100. \tag{3}$$

Furthermore, specific input energy (SIE) is another important evaluation of system performance, defined as follows:

$$\text{SIE} = \frac{\text{discharge power}}{\text{CO}\_2 \text{ flow rate}} \times 60. \tag{4}$$

The energy efficiency (EE) of the plasma splitting CO2 process is defined as the molar number of CO2 decomposed per unit plasma power and is formulated by the following equation:

$$\text{SEE}\left(\%\right) = \frac{\Delta\text{H}\_{298\text{K}} \times \text{CO}\_2 \text{ converted}}{\text{SIE} \times 24.5} \times 100,\tag{5}$$

where ΔH represents the reaction enthalpy of one mole CO2 splitting.

#### **3. Results and Discussion**

#### *3.1. E*ff*ect of Packing Materials on Discharge Characteristics*

Figure 2 presents the variation of the Lissajous figure with discharge voltage. From the picture, we can observe that the area of the Lissajous figure increases with increasing voltage. The impact of different filled foam metals on the discharge characteristics at the same input power of 70 W is shown in Figure 3. Compared with non-packing materials, there are much less filamentary discharges in the reactor packed with foam Fe, Al, and Ti. It is apparent that the filling of foam metals in the reactor causes changes in discharge characteristics and discharge patterns, from the original filamentary discharge to both the current filamentary discharge and surface discharge. The results demonstrate that the introduction of the packed foam metals into the DBD reactor can promote the discharge characteristics. We have discussed similar phenomena in our previous studies. For example, when ZrO2 or foam Ni catalyst is introduced into the reactor, the discharge type changes and the filamentation discharge decreases dramatically [26–28]. Moreover, at the same input power, the Lissajous pattern of the electrode made of foam Ti has a larger area than the other two, which means that the foam Ti can further enhance the discharge power. Furthermore, from the slopes of the graph, it can be concluded that the foam Ti electrode transfers more charges than the other two foam metals. Filling the materials into the tube can generate a strong electric field. Near the particle contact points, the field intensity and the number of high-energy electrons increase.

**Figure 2.** Lissajous diagram of a dielectric barrier discharge (DBD) reactor at different voltages.

**Figure 3.** Lissajous figure of CO2 decomposition in the DBD reactor with or without foam metals.

From the voltage and current waveforms of the CO2 discharge, changes of the discharge characteristics in the reactor with and without the packing materials can be seen. A typical filamentous discharge can be seen in the empty tube, which can be verified by a plurality of burr peaks in the current signal of Figure 4a. On the contrary, as is exhibited in Figure 4b–d, filling the foam metals into the discharge region produces a typical packed-bed effect and results in the discharge characteristics to change from a filamentary discharge to a combination of surface discharge and filamentary discharge, because the spike discharge signal is reduced as demonstrated. In the packed bed DBD reactor, the filamentary discharge can only be emerged in a small gap between the particle-particle and the particle-tube walls, while the surface discharge can be generated on the particle surfaces near the particles.

**Figure 4.** Discharge characteristics of CO2 in the DBD reactor without or with filling foam metals: (**a**) Without packing; (**b**) Foam Fe; (**c**) Foam Al; (**d**) Foam Ti.

#### *3.2. E*ff*ect of Packing Materials on CO2 Conversion and CO Yield*

The CO2 conversions and CO yield when different filling foam metals were placed in the discharge gap are displayed in Figure 5. Clearly, packed bed DBD that was filled with three foam metals shows higher CO2 conversion and CO yield than DBD without packing. Notably, CO2 conversion and CO yield are greatly increased in the foam Ti-packed reactor, which are approximately three times more than that of the CO2 conversion and CO yield in the non-packed tube. In the foam Fe-packed tube, CO2 conversion and CO yield are lower than the foam Ti-filled tube, but higher than the foam Al-packed tube.

**Figure 5.** Effect of packing materials on: (**a**) CO2 conversion; (**b**) CO yield.

#### *3.3. E*ff*ect of Discharge Power and Gas Flow Rate on CO2 Conversion*

The influence of discharge power and gas flow velocity on CO2 decomposition is depicted in Figure 6. One of the critical factors affecting the CO2 conversion by DBD plasma is the discharge power. The discharge can change the electric field of the plasma reactor, thereby changing the amount of active electrons and active radicals [29]. For an empty tube, it can be noted that increasing the discharge power from 30 W to 90 W results in an improvement in CO2 conversion. However, it drops as the discharge power is further increased to 110 W. When the flow rate is fixed at 20 mL/min, the empty tube reaches a maximum conversion of 21.15% at a power of 90 W. The CO2 conversion tends to be saturated and slightly decreased if the discharge power is continuously increased over 90 W, and 90 W may be the optimum power under this condition, so from the viewpoint of energy-saving, an appropriate discharge power range is required. Similarly, for foam Fe, when the flow rate is 20 mL/min, the maximum conversion rate of 44.84% is achieved at a power of about 70 W. However, further increases in discharge power have no obvious impact on CO2 conversion, because the discharge filaments cover the electrode surface and the quantity of microdischarges is not obviously increased [30].

**Figure 6.** Effect of discharge power and gas flow velocity on CO2 conversion: (**a**) Without packing; (**b**) Foam Fe; (**c**) Foam Al; (**d**) Foam Ti.

However, for foam Al and Ti, increasing the discharge power increases CO2 conversion. As the plasma power changes from 30 to 110 W, CO2 conversion increases from 26.58% and 30.16% to 44.02% and 46.61%, respectively. Increasing the plasma discharge power will increase the amplitude and number of current pulses, as depicted in Figure 7. A higher input power means more energy is put into the reaction system, which leads to a higher current density, producing more cation CO2+ and anion O2− in DBD plasma [31–33]. This phenomenon indicates that, as the discharge power increases, the quantity of microdischarges are effectively increased, which suggests that more chemical reaction channels and chemically active species are generated for CO2 processing [22,34]. Sufficient energy will activate the electrons and reactant molecules to increase the chances of mutual collision frequency between the active materials, so more chemical bonds will be destroyed and more active species will be produced, resulting in a higher CO2 conversion [32,35].

**Figure 7.** Effect of discharge power on the current signals for empty tube.

It has been proved that the feed flow velocity is also one of the important factors affecting the CO2 conversion. It can be noticed from Figure 7 that the CO2 conversion rate is the largest at the minimum CO2 flow rate of 20 mL/min, whether it is an empty tube or a filling foam metal tube. Similar phenomena have been reported in a non-packed DBD reactor [22]. When the other parameters are not changed, enhancing the CO2 flow rate decreases the residence time in the discharge area, which reduces the possibility of CO2 conversion caused by collision with high-energy electrons and chemically reactive species, and thus reduces the CO2 conversion rate. For example, when the CO2 flow velocity varies within a range of 20 to 100 mL/min, CO2 conversion is reduced from 46.61% to 33.98% at 110 W for foam Ti.

#### *3.4. E*ff*ect of Discharge Power and Gas Flow Rate on Energy E*ffi*ciency*

Energy efficiency is a big concern in plasma-assisted CO2 decomposition processes. Among various plasmas, microwave plasma decomposition can achieve higher energy efficiency, owing to the selective excitation of the vibration level of CO2 [36]. However, the energy efficiency of CO2 splitting is generally around 10% in DBD plasma, because the active particle impact of CO2 is a vital procedure in the plasma [12,37].

Figure 8 denotes the influencing factors of energy efficiency, namely, discharge power and gas flow velocity. The energy efficiency in filled foam metal tubes is higher than that of empty tubes over the input power range of 30 to 110 W that was studied. From the figure, we can conclude that energy efficiency drops with increasing discharge power. For foam Ti, the higher the power, the higher the CO2 conversion; the energy efficiency decreased from 8.85% to 5.96%, while the plasma discharge power is varied from 30 to 110 W.

**Figure 8.** Effect of discharge power and gas flow velocity on energy efficiency: (**a**) Without packing; (**b**) Foam Fe; (**c**) Foam Al; (**d**) Foam Ti.

The influence of feed flow velocity and discharge power on the energy efficiency of CO2 splitting by plasma exhibits the opposite behavior. A higher feed flow rate results in lower CO2 conversion, but more energy efficient. For example, by increasing the velocity of feed flow from 20 to 100 mL/min, the energy efficiency increases from 3.88% to 8.85%. Moreover, the reason why energy efficiency decreases with increasing discharge power may be attributed to energy loss, which is released in the form of heat, and it can be verified from an increase in condensate temperature. Under the same plasma operating conditions, the CO2 conversion and energy efficiency cannot simultaneously be taken into account during discharge process. Thus, there is a need to balance the relationship between CO2 decomposition and energy efficiency during the experiment.

#### *3.5. E*ff*ect of SIE on CO2 Conversion and Energy E*ffi*ciency*

According to the definition of SIE (Equation (4)), the variation of SIE is determined by the feed flow velocity and discharge power. Generally speaking, SIE is regarded as one of the important individual factors for CO2 transformation process [6,38,39]. Obviously, CO2 conversion and energy efficiency have opposite trends with changes in SIE. As a consequence, there is a demand to investigate the impact of the optimal SIE calculated by different combinations on the CO2 transformation process [40].

Figure 9a shows the impact of SIE on CO2 conversion. The introduction of foam metal catalysts into the DBD reactor has a significant effect on the CO2 decomposition rate, which increases with increasing SIE. Compared with the results of without packing, the foam Fe, Al, and Ti prominently raise CO2 conversion by 2.14, 2.42, and 2.63 times, respectively. Among the three catalysts, foam Ti catalyst has the best catalytic effect.

The effect of SIE on energy efficiency is shown in Figure 9b. The energy efficiency of all the reactors decreases with increasing SIE [41]. For instance, for foam Ti, when the SIE is increased from 42 to 210 kJ/L, the energy efficiency is decreased from 7.11% to 2.33%. Therefore, the interaction effects of power and CO2 flow velocity should be taken into account while using a suitable SIE to achieve high CO2 conversion and energy efficiency simultaneously.

**Figure 9.** Effect of specific input energy of different foam metals on: (**a**) CO2 conversion; (**b**) energy efficiency.

#### *3.6. E*ff*ect of Di*ff*erent Packing Materials on CO Selectivity*

The influence of different packed foam metals on CO selectivity at different discharge powers is exhibited in Figure 10. For foam Fe and Al, the CO selectivity during the whole reaction process is maintained at about 88–96%. Compared with the empty tube discharge, the difference in CO selectivity between the two at different discharge powers is not significant under our experimental conditions. It can be inferred that, for foam Fe and Al, discharge power plays a secondary role in CO selectivity. However, for foam Ti, the CO selectivity varies between 91% and 97%, which is much higher than the other two foam metals, indicating that the foam Ti has a good catalytic effect. The CO selectivity based on carbon atoms (Equation (3)) is close to 100%, which means that the stoichiometric conversion of CO2 to CO has been achieved in this study, and the generation of CO is mainly due to CO2 dissociation. The electron collision dissociation of CO2 is likely to lead to ground state CO and both the ground state and metastable O atoms, whereas studies have revealed that CO bands are observed in experiments, indicating that CO can also be formed in the excited state [42].

**Figure 10.** CO selectivity of different foam metals at different discharge powers.

#### *3.7. DFT Validations*

A prerequisite for highly efficient catalysts is the adsorption of CO2 on the catalysts' surface. To gain a clear insight into the adsorption performance of catalysts, an in-depth investigation of the CO2 adsorption was carried out. According to X-ray diffraction (XRD) analysis, the (101) plane of Ti, (111) plane of Al, and (110) plane of Fe are investigated carefully. Based on the above results, we performed DFT calculations to investigate the adsorption of CO2 on catalysts' surface as shown in Figure 11. The results indicate that the adsorption energy of CO2 on the (101) plane of Ti is −2.49 eV (Figure 11a), which is lower than that observed for the (111) plane of Al (Figure 11b) and (110) plane of Fe (Figure 11c). This indicates that CO2 would have more favorable adsorption on the (101) plane of Ti rather than the (111) plane of Al and (110) plane of Fe. These findings explain the origin of the high catalytic activity of the (101) plane of Ti. Accordingly, the (101) plane of Ti is superior to the (111) plane of Al and (110) plane of Fe for the CO2 conversion process.

**Figure 11.** Side views of the optimized geometric structures of CO2 adsorption on: (**a**) (101) plane of Ti; (**b**) (111) plane of Al; (**c**) (110) plane of Fe.

#### **4. Conclusions**

In this study, we investigated a coaxial DBD plasma junctions and different packed foam metal materials (foam Fe, Al, and Ti) placed into the discharge gap for CO2 conversion. The effects of power, flow velocity, and other discharge characteristics were studied to better understand the influence of the filling catalysts on CO2 decomposition. The filling of foam metals in the reactor caused changes in discharge characteristics and discharge patterns, from the original filamentary discharge to both the current filamentary discharge and surface discharge. Compared with the maximum CO2 conversion of 21.15% and energy efficiency of 3.92% in the reaction tube without the foam metal electrode, a maximum CO2 decomposition rate of 44.84%, 44.02%, and 46.61% and energy efficiency of 6.86%, 6.19%, and 8.85% were obtained in the reaction tubes packed with foam Fe, Al, and Ti, respectively. The results showed that the CO2 conversion rate for the filled foam metal electrode was obviously enhanced when compared with the non-packed tube. It was seen that the foam Ti had the best CO2 decomposition rate among the three foam metals.

Furthermore, we used density functional theory (DFT) to further verify the experimental results. According to X-ray diffraction (XRD) analysis, the (101) plane of Ti, (111) plane of Al, and (110) plane of Fe were carefully investigated. Based on the above results, we performed DFT calculations to investigate the adsorption of CO2 on catalysts surface. The results indicated that the adsorption energy of CO2 on the (101) plane of Ti was −2.49 eV, which was lower than the (111) plane of Al and (110) plane of Fe. The results showed that CO2 adsorption had a lower activation energy barrier on the foam Ti surface. The theoretical calculation is consistent with the experimental results, which better explains the mechanism of CO2 decomposition.

**Author Contributions:** Conceptualization, C.M., G.G., D.Y., B.D. and F.Y.; methodology, C.M., G.G., D.Y., and F.Y.; formal analysis, C.M., G.G., D.Y., F.Y., J.L., X.Z. and S.Z.; investigation, C.M., G.G., D.Y., F.Y., J.L., and X.Z.; data curation, C.M., G.G., D.Y., and F.Y.; writing—original draft preparation, C.M., G.G., D.Y., and F.Y.; writing—review and editing, J.L., X.Z., C.M., G.G., D.Y., and F.Y.; supervision, C.M., G.G., D.Y., and F.Y.

**Funding:** This research was funded by the National Natural Science Foundation of China (No.21663022) and Science and Technology Innovation Talents Program of Bingtuan (No.2019CB025).

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

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


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**3. HPV in carcinogenesis**

squamous intraepithelial lesions. The distinction of these alterations from HPV-related lesions is based on well-defined morphologic criteria; however, in certain lesions the distinction is less straightforward, and ancillary techniques can be of help, leading to a more precise diagnosis and increased diagnostic reproducibility. Regressing LSILs may also cause a diagnostic problem [33]. On the other hand, ruling out invasion can be difficult in certain high-grade lesions. The next parts of the present chapter are going to describe the ancillary techniques,

120 Human Papillomavirus and Related Diseases – From Bench to Bedside A Diagnostic and Preventive Perspective

**Figure 1.** a-c). The above lesions represent a spectrum of alterations in cervical biopsies, ranging from low-grade to

The precursor lesion of cervical adenocarcinoma, that is adenocarcinoma in situ (AIS), was introduced as a concept in 1953 and is now acknowledged to be the precursor to most invasive cervical adenocarcinomas [34]. AIS is less common than SIL, with a ratio of AIS/HSIL ranging

Adenocarcinoma in situ is characterized by glands with nuclear hyperchromasia and atypia, increased nuclear:cytoplasmic ratio, pseudostratification or stratification, mitoses and apop‐ totic bodies (Fig.2). It may coexist with SIL and can also be multifocal. It may show a variety of cellular differentiation, and several subtypes have been described, including endocervical,

The diagnosis of glandular dysplasia has been used for intraepithelial alterations of glandular epithelium less pronounced than AIS. However, it has low reproducibility, and it has been suggested that this term should no longer be used in the clinical setting [35], especially since glandular epithelium does not support a productive infection by HPV [33]. It has been suggested that problematic endocervical glandular atypias should be evaluated with special studies [34,35]. The term cervical glandular intraepithelial neoplasia (CGIN), with high grade

Cervical endometriosis, tubal and endometrioid metaplasia, and reparative changes have to be distinguished from AIS. Arias-Stella reaction, atypia due to irradiation, atypical forms of microglandular hyperplasia, as well as other viral infections, specifically Cytomegalovirus and

which allow for a more precise diagnosis in some of the problematic cases.

(a) (b) (c)

**2.2. Precursor lesions of cervical adenocarcinoma**

endometrioid, intestinal, tubal, and stratified [34].

CGIN equating to AIS, is being used in several laboratories [20].

in most series between 1:26 and 1:237 [33].

high-grade lesions.

Human papillomavirus is estimated to comprise a causal agent in 5% of human cancers and is associated with more human cancers than any other virus [36]. Among them, it is associated with the vast majority of cervical cancer cases. In contrast to several other infectious agents, which act as indirect carcinogens by inducing immunosuppression or by preventing apoptosis, high-risk HPVs (HR-HPVs) act mainly as direct carcinogenic factors [3]. Persistent infection by HR-HPVs correlates with increased risk of cervical cancer. However, infection by low-risk HPV types (LR-HPVs), carries a negligible risk of malignant progression. Additionally, other factors, related to the host or the environment, contribute to the development of neoplasia.

Several studies have revealed the complex intracellular interactions, which take place among oncoproteins encoded by human papillomaviruses and their cellular target proteins [3,37-39]. Their complexity is reflected in the long interval between infection and invasive carcinoma detection, often spanning a period of 15 to 25 years [3]. These interactions have offered to investigators the opportunity to study important cellular pathways related to the carcinogenic process, while several participating proteins have been studied for their possible use as markers of HPV infection in biopsy or cytology specimens. These biomarkers are presented in the next part of the present chapter.

#### **3.1. HPV in carcinomas of the anogenital tract**

Cervical cancer represents today a relatively well-studied prototype of a human tumor related to a viral infection, as well as a model for multi-step carcinogenesis. The revealed strong association led to the suggestion that human papillomavirus is not only the main cause of cervical cancer, but also a necessary cause [6].

In addition to cervical cancers, a significant percentage of vaginal, vulvar, penile, anal and perianal carcinomas are HPV-positive [7,40-42], while a fraction of carcinomas in other sites of the human body has also been linked to high-risk [HR] HPV infections. Percentages of HPV positivity observed in carcinomas of the anogenital area are presented in Table 1.

and transformation. High-risk HPV E6 proteins target p53 for proteasomal degradation through association with the cellular ubiquitin ligase E6AP [48,52]. Low-risk HPV E6 proteins can also associate with E6AP; however, high-risk HPV proteins target p53 for ubiquitination. Activation of telomerase is another important facet of E6 functions, which is augmented by

Ancillary Techniques in the Histopathologic Diagnosis of Squamous and Glandular Intraepithelial Lesions…

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HPV E7 proteins interact with the retinoblastoma tumor suppressor protein, pRB, which controls S-phase entry through association with E2F transcription factor family members. They also interact with the related pocket proteins, p107 and p130. High-risk HPV E7 targets pRB for proteasomal degradation, while low-risk HPV E7 binds pRB with lower efficiency (ap‐ proximately 10-fold lower) than the former [47,53]. E7 proteins cause aberrant activation of cdk2 (cyclin-dependent kinase 2), which is associated with cyclins E and A, as well as cdk inhibitors, mainly p21CIP1 and p27KIP1. E7 expression results in dysregulated expression of cyclins E and A [48,54]. Through its multiple interactions, E7 can uncouple keratinocyte differentiation from cell cycle progression and retain differentiating keratinocytes in a DNA

The above interactions form the basis for the application of some important biomarkers used nowadays in many laboratories worldwide. These include p16, a cyclin-dependent kinase inhibitor, often exhibiting increased expression in HPV-related intraepithelial lesions, as well as cyclin E, as will be discussed in the following. Furthermore, proliferation markers, like Ki67, show increased/altered expression, in comparison to non-HPV-infected epithelium of the

In addition, HR-HPV E6 and E7 proteins cooperate to generate mitotic defects and aneuploidy through induction of supernumerary centrosomes and multipolar mitoses in epithelial cells [55], while genomic instability results in the addition of molecular alterations. The detection of abnormal mitoses is a useful morphologic indicator of high-risk HPV-associated lesions [32]. Finally, integration of HPV genome into host chromosomes is an important event in cervical carcinogenesis [56,57]. Integration occurs frequently during malignant progression and may result in dysregulation of E6/E7 expression due to disruption of E2, with associated loss of the

Except for the above interactions, several other factors contribute to the development of neoplasia, and these are related to the host or the environment. Smoking, the use of oral contraceptives, high parity and Chlamydia are associated with a relative risk of 2 to 4 [7,9,58-60]. Immunity plays an important role, and this is reflected in data concerning cervical

**4. Immunohistochemical stains in the diagnosis of cervical intraepithelial**

The role of the pathologist examining a cervical biopsy suspicious for an intraepithelial lesion consists of: a) confirming or excluding the presence of a lesion, b) excluding other entities

lesions in HIV-infected individuals and in transplant-recipients.

E7-induced interactions.

synthesis competent state.

uterine cervix.

inhibitory E2 action.

**lesions**

**Table 1.** Percentage of HPV detection in carcinomas of the anogenital region other than cervical carcinoma [4,7].

The distinction of HPV types into low- and high-risk is based on their association with carcinomas, and this distinction is sometimes challenging, especially in the case of rare/weakly carcinogenic viral types. The most common HPV types detected in cervical carcinomas include HPV 16, 18, 45, 31, 33, 52, 58, and 35, belonging to the high-risk group [43-45]. Low-risk viral types may confer risk reflecting an "at risk behavior" [14].

The fraction of cervical squamous cell carcinomas attributable to HPV16 and HPV18, which comprise the two most common high-risk viral types, is estimated at about 70%, while the respective fraction of cervical adenocarcinomas is 86%. As expected, in low-grade squamous intraepithelial alterations the respective percentages differ, since a significant number of these lesions contain HPVs which do not belong to the most common high-risk types.

#### **3.2. Human papillomavirus oncoproteins and their main interactions with cellular pathways**

HPVs are epitheliotropic double-stranded DNA viruses, whose replication is dependent on the terminally differentiating epithelial tissue. Their circular genome includes several open reading frames (ORFs) encoding for proteins which control early (E) and late (L) viral functions (E1, E2, E4, E5, E6, E7, L1, and L2) [46].

High-risk mucosal HPVs encode three transforming proteins: E5, E6 and E7, which exhibit multiple biological activities. These have been extensively studied in the last few decades; however, several aspects remain to be elucidated [47-48].

HPV E5 is able to transform mouse fibroblasts and keratinocytes in culture [49]. It is believed to contribute to early stages of carcinogenesis and works in concert with E6 and E7 [50-51]. These latter proteins, which often act synergistically, are necessary for the induction and maintenance of the transformed phenotype. They inhibit the function of tumor suppressors p53 and pRb, respectively, whereas their expression enables cells to bypass normal cell cycle checkpoints.

E6 and E7 are required for both the development of precursor lesions of cervical carcinoma, and for maintaining the malignant phenotype of cervical cancer cells [3]. E6 and E7 proteins play critical roles, being able to immortalize human keratinocytes and induce cell proliferation and transformation. High-risk HPV E6 proteins target p53 for proteasomal degradation through association with the cellular ubiquitin ligase E6AP [48,52]. Low-risk HPV E6 proteins can also associate with E6AP; however, high-risk HPV proteins target p53 for ubiquitination. Activation of telomerase is another important facet of E6 functions, which is augmented by E7-induced interactions.

In addition to cervical cancers, a significant percentage of vaginal, vulvar, penile, anal and perianal carcinomas are HPV-positive [7,40-42], while a fraction of carcinomas in other sites of the human body has also been linked to high-risk [HR] HPV infections. Percentages of HPV

positivity observed in carcinomas of the anogenital area are presented in Table 1.

122 Human Papillomavirus and Related Diseases – From Bench to Bedside A Diagnostic and Preventive Perspective

**Table 1.** Percentage of HPV detection in carcinomas of the anogenital region other than cervical carcinoma [4,7].

The distinction of HPV types into low- and high-risk is based on their association with carcinomas, and this distinction is sometimes challenging, especially in the case of rare/weakly carcinogenic viral types. The most common HPV types detected in cervical carcinomas include HPV 16, 18, 45, 31, 33, 52, 58, and 35, belonging to the high-risk group [43-45]. Low-risk viral

The fraction of cervical squamous cell carcinomas attributable to HPV16 and HPV18, which comprise the two most common high-risk viral types, is estimated at about 70%, while the respective fraction of cervical adenocarcinomas is 86%. As expected, in low-grade squamous intraepithelial alterations the respective percentages differ, since a significant number of these

**3.2. Human papillomavirus oncoproteins and their main interactions with cellular pathways** HPVs are epitheliotropic double-stranded DNA viruses, whose replication is dependent on the terminally differentiating epithelial tissue. Their circular genome includes several open reading frames (ORFs) encoding for proteins which control early (E) and late (L) viral functions

High-risk mucosal HPVs encode three transforming proteins: E5, E6 and E7, which exhibit multiple biological activities. These have been extensively studied in the last few decades;

HPV E5 is able to transform mouse fibroblasts and keratinocytes in culture [49]. It is believed to contribute to early stages of carcinogenesis and works in concert with E6 and E7 [50-51]. These latter proteins, which often act synergistically, are necessary for the induction and maintenance of the transformed phenotype. They inhibit the function of tumor suppressors p53 and pRb, respectively, whereas their expression enables cells to bypass normal cell cycle

E6 and E7 are required for both the development of precursor lesions of cervical carcinoma, and for maintaining the malignant phenotype of cervical cancer cells [3]. E6 and E7 proteins play critical roles, being able to immortalize human keratinocytes and induce cell proliferation

lesions contain HPVs which do not belong to the most common high-risk types.

Vaginal carcinomas 60-91% Vulvar carcinomas 50% Penile carcinomas 30-50% Anal and perianal carcinomas 60-94%

types may confer risk reflecting an "at risk behavior" [14].

however, several aspects remain to be elucidated [47-48].

(E1, E2, E4, E5, E6, E7, L1, and L2) [46].

checkpoints.

HPV E7 proteins interact with the retinoblastoma tumor suppressor protein, pRB, which controls S-phase entry through association with E2F transcription factor family members. They also interact with the related pocket proteins, p107 and p130. High-risk HPV E7 targets pRB for proteasomal degradation, while low-risk HPV E7 binds pRB with lower efficiency (ap‐ proximately 10-fold lower) than the former [47,53]. E7 proteins cause aberrant activation of cdk2 (cyclin-dependent kinase 2), which is associated with cyclins E and A, as well as cdk inhibitors, mainly p21CIP1 and p27KIP1. E7 expression results in dysregulated expression of cyclins E and A [48,54]. Through its multiple interactions, E7 can uncouple keratinocyte differentiation from cell cycle progression and retain differentiating keratinocytes in a DNA synthesis competent state.

The above interactions form the basis for the application of some important biomarkers used nowadays in many laboratories worldwide. These include p16, a cyclin-dependent kinase inhibitor, often exhibiting increased expression in HPV-related intraepithelial lesions, as well as cyclin E, as will be discussed in the following. Furthermore, proliferation markers, like Ki67, show increased/altered expression, in comparison to non-HPV-infected epithelium of the uterine cervix.

In addition, HR-HPV E6 and E7 proteins cooperate to generate mitotic defects and aneuploidy through induction of supernumerary centrosomes and multipolar mitoses in epithelial cells [55], while genomic instability results in the addition of molecular alterations. The detection of abnormal mitoses is a useful morphologic indicator of high-risk HPV-associated lesions [32].

Finally, integration of HPV genome into host chromosomes is an important event in cervical carcinogenesis [56,57]. Integration occurs frequently during malignant progression and may result in dysregulation of E6/E7 expression due to disruption of E2, with associated loss of the inhibitory E2 action.

Except for the above interactions, several other factors contribute to the development of neoplasia, and these are related to the host or the environment. Smoking, the use of oral contraceptives, high parity and Chlamydia are associated with a relative risk of 2 to 4 [7,9,58-60]. Immunity plays an important role, and this is reflected in data concerning cervical lesions in HIV-infected individuals and in transplant-recipients.


**5. Conclusions**

The productivity, Chl (*a*, *b*, and total), protein, and carbohydrate content of *I. zhangjiangensis* can be regulated by different light wavelengths. White light increased the productivity and protein content, and red light increased the specific growth rate. Pigment content was higher under the green light but possibly does not provide adequate amounts of energy for biomass synthesis. The blue light highly promoted carbohydrate content, suggesting that the light influence is a complex phenomenon that is still far from being completely understood. This study is important in terms of the selection of light at the appropriate wavelength to increase the number of metabolites desired in indoor production levels, with the possibility of adaptation to other culture systems that require artificial illumination.