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Luiz Augusto Gomes de Souza Instituto Nacional de Pesquisas da Amazônia: Inpa, Manaus, Amazonas, Brazil

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*Biodiversity of Fabaceae in the Brazilian Amazon and Its Timber Potential for the Future DOI: http://dx.doi.org/10.5772/intechopen.110374*


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# **Automatic Identification and Interpretation of Animal Sounds, Application to Livestock Production Optimisation**

Vasileios Exadaktylos, Mitchell Silva and Daniel Berckmans

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56040

## **1. Introduction**

In modern livestock production, biosecurity and improved disease monitoring are of great importance to safeguard public and animal health. In addition, citizens expect that farm animals have been reared and killed humanely with minimal environmental impact and that food from animals is safe. These global threats and concerns can be combated through surveillance and research networks for early detection of animal diseases and better scientific cooperation between countries and research teams. The use of information technology (IT) can provide new possibilities for continuous automatic monitoring (Fig. 1) and management of livestock farming, according to multiple objectives.

Surveillance relies on vast streams of data to identify and manage risks. Currently data collection nearly always depends on manual methods. While this might be acceptable in R&D projects, it is unrealistic when solutions are applied on the scale needed on commercial farms. Scoring of some animal-based information by human inspectors by manual methods is often inaccurate, time consuming and expensive when implemented at farm level. It is clear that a multidisciplinary and integrated approach, using modern IT systems, is needed to optimise use of expensive inspectors.

One current advantage provided by technology in livestock production is the development of sensors and sensing technologies to automatically monitor and evaluate data from processes in real-time. Collecting data from livestock and their environment is now possible using innovative, simple, low-cost IT systems; data are then integrated in real time by using knowledge-based computer models [1].

© 2014 The Author(s). Licensee InTech. 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.

**Figure 1.** Sound analysis can be used in a variety of applications. Pigs, chickens and chicken embryos are considered in this chapter.

The initial application of technology in livestock has been the growth of housed pigs and poultry though, in principle, this approach could be applied to any farmed species, including animals farmed extensively [2]. Stockmen routinely gather auditory, olfactory and visual information from their animals to evaluate health, welfare and productivity. New technology can aid this task, even with large flocks or herds, thanks to the (r)evolution in sensors and sensing techniques, e.g. developments in micro- and nano- electronics [3][4].

Other sensors include pedometers for monitoring oestrus behaviour in dairy cows [5]. Automatic weighing systems for broilers, laying hens and turkeys have been used for a number of years to estimate the average weight of a flock [6]. Telemetry sensors for measuring heart rate, body temperature and activity have also been developed [7]. Sensors for quantifying milk conductivity and yield of individual cows are available and may be used to optimise produc‐ tion and provide early detection of poor welfare in individuals [8]. The above examples are not exhaustive, but demonstrate the present and future possibilities in monitoring animal disease, welfare and performance.

The last decade a new field of research has appeared in relation to livestock production and animal welfare. Precision Livestock Farming (PLF) has emerged as a tool that uses continuous and automatic techniques in real-time in order to monitor and control/manage animal production, health, welfare and environmental load in livestock production. The first step in PLF is the measurement and interpretation of animal bioresponses. In this direction, sound has been extensively used as an important bioreponse that can provide useful information regarding the status of the animals.

In this chapter, three examples of monitoring animal sounds as a tool to determine animal status are presented, namely chicken embryos, chickens and pigs.

## **2. Interpretation of chicken embryo sounds**

Industrial egg incubators vary in size and capacity from 10.800 to 129.600 eggs, with the larger machines being commercially more attractive to hatchery managers due to the lower invest‐ ment and operational costs per egg. It has been shown in the relevant literature that the spread

of hatch of the eggs in an industrial incubator can be as long as 48h [9]. This has serious implications to the later life of the chicks because for operational reasons access to feed and water is delayed for the early hatchlings.

To study the effect further, techniques for monitoring the hatch window have been developed. For example, monitoring climate variables (e.g. temperature and humidity) along with biological variables (e.g. heart rate) have shown potential as estimators of the hatching process. More specifically [10] studied the CO2 balance as an indirect measure of thermoregulation. In [11] heart rate fluctuations were studied and were shown to have specific patterns during the pipping and hatching stages. The resonance frequency of incubating eggs was also linked to hatching and even a predictor of hatching could be made [12]. More recently, it was shown [13] that magnetic resonance imaging (MRI) can be used to monitor hatching.

The above techniques are valuable tools in a research environment. However, their invasive nature and/or high cost of implementation don't allow for use in an industrial or commercial setup. To overcome such problems, sound was used to study the hatching process [14]. Sound can be measured non-invasively and a single microphone can acquire information about the complete incubator which makes it attractive for the industry. At the same time, current technology makes it possible to use microphones in different environment at a low cost.

At the same time, research has been conducted in relation to reducing the hatch window (i.e. the time between the first and last chick that hatched) [15]-[17]. However, successful applica‐ tion of many of these techniques requires accurate identification of Internal Pipping (IP – the moment in which the embryo is entering the air cell inside the egg) which is an important milestone in the hatching process. Once this stage has been reached, chicks inside the egg start to vocalise.

To address this problem of identifying the IP stage, an algorithm has been developed where sound is recorded in an industrial incubator [18], is analysed in real-time using a Digital Signal Processor (DSP) and is able to detect when all the chicks in the incubator have reached the IP stage (i.e. IP100). Despite the fact that the collected sound is coming from the whole incubator, the developed technique is able to isolate the sounds coming from all the eggs and infer information about the embryo state. The algorithm is based on the observation that the frequency content of the embryo sounds during different hatching stages is significantly different [18]. This is visually illustrated in Fig. 2.

### **2.1. Algorithm description**

Although the vocalisations at different stages of the hatching process do show statistically significant differences in terms of the peak frequency, a successful classification algorithm requires a robust feature that will not (or minimally) be affected by other sounds that are acquired by the microphone. To account for this, the peak frequency of a sound is not used directly by the algorithm. From the results of Fig. 2 and by experimentation a ratio of the total energy in the 2-3 and 3-4 kHz frequency bands was able to provide a robust measure that can easily be calibrated as explained below. This difference is visualised in Fig. 3 where the mean spectra of the collected sounds are presented.

**Figure 2.** Boxplot showing the peak frequency of the collected sounds when the embryo is in Internal Pipping (IP), External Pipping (EP) and when it has hatched (HT). The box shows the 25th and 75th percentile of the data, the whisk‐ ers are the most extreme points not considered outliers and the crosses are the outliers.

**Figure 3.** Frequency content of the mean of vocalisations during Internal Pipping (dashed black line) and the mean of vocalisations during Hatch (solid red line). A line visualising the limit of 3 kHz is also shown (vertical blue dotted line)

The classification of the frequency ratio is based on an adaptive threshold. This threshold reflects the background noise characteristics during a period where IP has not started yet. During the beginning of the sound recording (e.g. Incubation Day 16) when the embryos have not penetrated the air cell yet, the algorithm is defining a baseline. The threshold is then set to 90% lower than this threshold. Once the algorithm output remains below this threshold value for more than 5 minutes continuously, IP100 has been reached an the algorithm is giving a signal. In the training dataset, the algorithm has shown to have an accuracy of ±3.1 hrs in the detection of IP100.

A block diagram description of the algorithm is shown in Fig. 4.

**Figure 4.** Block diagram of the algorithm for automatic identification of IP100

#### **2.2. Validation results**

The algorithm was developed and tested using data collected during 12 incubations in an industrial incubator (Petersime, Belgium). The algorithm had an absolute deviation of 3.2 hrs from the actual IP100 point. An example of the output of the algorithm is presented in Fig. 5.

As explained in the previous section, the threshold shown in Fig. 5 is automatically defined as 90% of the mean value during the initialisation of the algorithm. This value was chosen after experimentation with the data and allows for short variations of the algorithm output due to non-incubation sounds not to be considered as an alarm.

**Figure 5.** The output of the algorithm (black solid line) and the threshold that was automatically chosen (horizontal blue dotted line). Once the algorithm output crosses the threshold for more than 5 minutes, IP100 is detected.

For the validation, the algorithm was implemented on a DSP (TMS320C6416T by Texas Instruments) using the Real-Time Workshop® of the MATLAB® environment. Once it was giving the signal that IP100 has been reached, a sample of 300 eggs was manually checked for IP. This experimental setup is shown in Fig. 6. In total, 5 experiments were performed during 5 incubations and the results are shown in Table 1.

**Table 1.** Validation results of the algorithm for IP100 detection in industrial incubators

The results of Table 1 show that IP100 was identify correctly. Although this may seem an inaccurate claim (since the IP count was between 93% and 98%), it is important to note the nature of the validation trial. Once the algorithm is providing a signal, IP is manually checked in the incubator. In the hypothetical case that IP is shown to be 100% (i.e. all embryos have been through the IP stage), it is not certain when this has happened. It is perfectly possible that IP100 has been reached long before the manual count. However, if IP of 98% is manually checked, then this means that almost all the embryos have been in the IP stage and therefore from a practical point of view, it is very close to the actual IP100.

**Figure 6.** Schematic diagram of the experimental setup for the validation trial. The sound is acquired from a micro‐ phone placed in the left part of the incubator and the signal is directed to a Digital Signal Processor (DSP). The DSP is executing the algorithm and is providing a signal by means of LEDs as to whether IP100 has been achieved.

# **3. Monitoring of vocalisations of chickens**

Analysis and interpretation of vocalisations of chickens is a powerful tool when studying the behaviour and welfare. It has been presented in the literature that different types of calls (such as distress or happy calls) can be identified based on their frequency characteristics [19], [20]. More recently, attempts are made to automatically classify these calls and relate them to animal welfare [21].

In relation to thermoregulation, chicken vocalisations have been shown to have specific patterns when chicks are exposed to cold stress [22]. Furthermore, vocal solicitation of heat, forms an integral part of the development of the thermoregulatory system of young chickens [23]. Finally, the thermal comfort of chicks has been shown to have links with the amplitude and frequency of calls [24].

On a social level, chick vocalisations are directly linked to stress and welfare parameters. A dissociation of stress behaviour during social-separation of the chick is mediated by novelty while distress calls are mediated by isolation [25]. Lastly, a method has been developed that could be used for stress monitoring of a flock and is based on multiparametric sound analysis [26].

It should be noted that chickens, being living organisms, are Complex, Individually different, Time varying and Dynamic (CITD) [27]. As such, the characteristics of their vocalisations are expected to change with time. This is confirmed by experimental results in which the peak frequency of the chicken vocalisations for the same chicken at 1 day and at 20 days old was estimated to be 3603(±303) Hz and 3441(±289) Hz respectively. This parameters shows to be significantly different between the two groups.

It should be noted that no distinction was made between different types of vocalisations. For example, in [19] different types of calls (e.g. distress or pleasure calls) were studied and it was shown that there are differences in terms of frequency content. A more detailed analysis is needed in terms of the evolution of the frequency characteristics with age.

# **4. Pig cough identification for health monitoring**

Similar to the effect on humans, respiratory diseases in pigs result in coughing and in a different sound of coughing due to the different response of the respiratory system when contacting different pathogenic agents. In humans, an experienced physician can identify over 100 different respiratory diseases based on the sound timbre [28]. In animals, veterinarians use a similar approach to detect sick animals when they enter a farm. Their initial impression over the herd is based on visual and auditory observation when they collect information about the welfare, health and productive status of the animals. In this direction, pig vocalisations related to pain were studied in [29], while vocalisation analysis in livestock farms as a measure of welfare has been employed in [30], [31].

The frequency characteristics of pig vocalisation have been extensively studied under different conditions [32][33][34]. These approaches have been further extended to develop algorithms for automatic classification of pig coughing under commercial farming conditions. For this, both frequency [35] and time domain [36] sound analysis techniques have been used. Fur‐ thermore, the distinction between dry and productive coughs has been made to further enhance the capabilities of the algorithm. This latter has been studied in detail in [37] where the energy envelope of a sound was shown to be different in dry and productive cough. The system is therefore able to not only provide information about the number of coughing incidents, but also in relation to the quality of the produced cough.

### **4.1. Algorithm description**

As mentioned above, the objective of the algorithm is to detect productive and non-productive coughs in a commercial piggery. Sound is acquired in the pig house by a single microphone placed in the middle of the compartment at a height of 2 meters above the ground. The overall algorithm is shown in Fig. 7 and each block is subsequently described in more detail.

**Figure 7.** Block diagram of the algorithm to identify cough and distinguish between productive and dry cough.

The first step of the algorithm is to identify individual sounds before using a cough classifier. A procedure that is based on the energy envelope of the sound signal has been described in [36]. Briefly explained, this approach is identifying the parts of the signal with high enough energy that could potentially be cough incidents. An example of this is shown in Fig. 8 for a cough attack (a series of coughing events). It should be noted that each sound is identified as an event for further processing. There is no guarantee that the sound originates from a single animal while overlap of sounds is possible (especially during busy periods of the day).

It can be argued that a such a simple sound extraction algorithm would require accurate calibration of the microphone and would be prone to errors due to large background noise. To tackle this, depending on the situation, noise filters can be included preceding the appli‐ cation of the sound extraction algorithm in order to remove any structured and know back‐ ground noise. At the same time, an adaptive threshold procedure has also been integrated [36] that allows for accurate selection of only sounds events without including noise. It was further shown in [36] that despite the background noise, classification is still possible using this sound extraction method.

**Figure 8.** Extraction of individual sounds (bottom plot) from a cough attack (top plot). All 4 sound incidents are suc‐ cessfully extracted.

In the next instance, the algorithm is applying a classifier in order to identify coughs. At this stage it is not important to determine whether the cough is productive or not. For this, either the approach described in [34] or the approach described in [35] can be used. Both methods are based on frequency analysis and exploit the characteristics of the frequency content of the cough sounds in different frequency bands in comparison to other sounds that can occur in a pig house (e.g. screams, grunts, feeding systems, contact with doors, etc.). Table 2 provides an overview of the frequency bands an which of those are used by the two algorithms. It should further be noted that both algorithms have employed fuzzy c-means clustering to reach their results and have identified fixed thresholds for their classifiers.

**Table 2.** Frequency ranges used for the classification of cough. Differences between [34] and [35].

Once a sound has been identified as a cough, then the distinction between a productive and a non-productive cough has to be made. For this, the algorithms presented in [36] or [37] can be used. Both algorithms are based on time-domain characteristics of the coughs. The algorithm presented in [37] is focusing on the decay of the cough sound. To do so, the cough is described by it energy envelope and subsequently a mathematical model is made for the drop of the amplitude. It was shown that productive coughs have a longer decay (as expressed by the time constant) than dry coughs. This is visualised in Fig. 9. It has been hypothesised that this difference is due to differences in lung plasticity that is changing with the occurrence of a respiratory disease. However, further research is needed to confirm this hypothesis.

An alternative method to classify a cough as productive or not is to use the algorithm described in [36]. In this approach, a third order Auto-Regression (AR) model is used (e.g. [38]) to estimate the sound signal. This model has the following form:

$$y\_k = a\_1 y\_{k-1} + a\_2 y\_{k-2} + a\_3 y\_{k-3} \tag{1}$$

Where *yk* is the value of the signal at sample time k and *a*1 , *a*2 and *a*3 are the 3 model parameters. It was shown that productive coughs can be distinguished from dry coughs by using these three parameters. More specifically, a polyhedron is defined in the (*a*1 , *a*2 , *a*3 ) space that is subsequently used as a classifier for productive coughs. Alternatively, the centre of the dry cough can be defined and it can be assumed the coughs outside this cluster are productive coughs.

**Figure 9.** The difference in the decay between a dry (red dashed line) and a productive (black solid line) cough is shown as has been studied in [37]

In the literature it was shown [39] that the vocal tract can act as a first order filter that has the form:

$$
\Delta y\_k = \frac{b\_1}{1 + a\_1 z^{-1}} \mu\_k \tag{2}
$$

Where *z* -1 is the time-shift operator (i.e. *yk z* -1 = *yk* -1) and *uk* is the input to the system. Similarly, (1) can be re-written to have the form:

Since coughing can be considered involuntary in the studied cases, white noise can be considered the input to the system. From the above it can be hypothesised that the classifier was able to identify and exploit some higher order dynamics of the effect of the vocal tract on the produced sound response. Of course this claim has to be studied further.

During the calibration phase of the algorithm, the centre of the dry cough cluster has to be defined. For this, the first five dry coughs need to be identified and the average of their parameters should be used as the centre of the productive cough cluster (the edges of the cluster are defined by the variance of the parameters). It can be assumed that at the beginning of the growing period there is no respiratory disease present and therefore only dry coughs are present. So, the first five coughs that will be introduced are dry coughs and can be used to define the dry cough cluster.

#### **4.2. Validation results**

The algorithm has been applied on a continuous recording with a total of 671 sounds (291 productive coughs, 231 dry coughs and 149 other sounds) collected under laboratory conditions.

The following Table 3 shows the results of the classification power of the algorithm.

**Table 3.** Performance of the productive vs. Dry cough identification algorithm

The above algorithm has been integrated in a commercial system by SoundTalks nv. (Belgium – www.soundtalks.be).

# **5. Conclusion**

This chapter has presented three cases where sound has been used to identify and interpret animal vocalisations. The studied cases are focused on identifying the status of animals in livestock production that will allow the caretaker of the animals to quickly identify health, welfare and production issues and take action. On the positive side, sound can be acquired from a large space and without any contact with the animal. This latter is an important factor in the potential for such technology to be applied in practice. Issues such as cost for placement, loss of sensors due to misuse by the animals and acquisition of sensor at the slaughter line are immediately solved. On the negative side though, the acquired sound signal cannot immedi‐ ately be linked to a specific animal and it is the combination of all sounds and vocalisations in the area. As such, conclusions about a group of animals can be reached but not necessarily for individual ones.

Sound analysis is an excellent show case for the merits of Precision Livestock Farming (PLF) and can already demonstrate the benefit that PLF can have on intensive livestock production. Further research in the field should focus on identifying critical areas for livestock productions and work towards providing automatic tools for early diagnosis. In order for practical implementation, issues that are not related to animal vocalisations will also have to be dealt with. These include, but are not limited to room acoustics, positioning of the microphones for better sound acquisition, identification of vocalisations in a noisy environment, etc.

In the direction of implementation of PLF systems, an EU project (EU-PLF, project number 311825) is currently running with the objective of creating a Blueprint that describes the process of starting from a scientific idea, development of a commercial prototype, implementing a system on farm and creating value for the farmer. In this framework, the results that have been presented in section 4 of this chapter will be applied in a practical setting.

## **Acknowledgements**

We would like to thank the European Union, the IWT (agentschap voor Innovatie door Wetenschap en Technologie), the KU Leuven and Petersime nv. for funding parts of the research described in this chapter. We would also like to thank all the researchers and technicians involved in the research throughout the years.

## **Author details**

Vasileios Exadaktylos, Mitchell Silva and Daniel Berckmans

Division M-BIORES: Measure, Model & Manage Bioresponses, Department of Biosystems, KU Leuven, Belgium

## **References**


**2. Proposed Model**

The formulated electrothermal model of a diode–transistor switch is based on the concept described in the study [16,17] for such a switch including the power MOS transistor. The network representation of the proposed electrothermal model of a diode–transistor switch (DTS) with IGBT and a diode is shown in Figure 2.

**Figure 2.** Network representation of the electrothermal averaged model of a diode–transistor switch (DTS) including IGBT and a diode.

Terminals of the presented model denoted as 1, 2, 3 and 4 correspond to terminals of the real diode–transistor switch in the diagram shown in Figure 1. This model can be connected to other parts of the analyzed DC–DC converters through the mentioned terminals in accordance with the diagram of these converters. The control signal is not shown in the model, but frequency f and duty cycle d of this signal are parameters of the presented model that are used in descriptions of some controlled voltage and current sources existing in this model. The voltages on terminals TjT and TjD correspond to internal temperatures of IGBT and the diode, respectively.

The presented model consists of 4 blocks. These are: main circuit, aided block, thermal model and CCM/DCM. Blocks and all components contained in them are described below.

The main circuit includes two controlled voltage sources *ER* and *ET* modeling average voltage between output terminals of IGBT and controlled current source GD modeling the average value of diode current. Voltage source V1 of zero value is used to monitor the average value of diode current. Values of currents *I*1*av* and *I*2*av*, as well as voltages *V*1*av* and *V*2*av* in the main circuit, are average values of diode and transistor voltages and currents. The connected in series controlled voltage sources *ER* and *ET* model a voltage drop between transistor output terminals. Controlled current source *GD* models diode current. The output values of these sources are described with elaborated formulas using the concept presented in the papers [16,17]. According to this concept, in each period of the control signal, current flows through the transistor for a time equal to d·T, and through the diode—for a time equal to (1-d)·T, wherein T is period of the control signal. Diode current–voltage characteristic and transistor output characteristic are described by piecewise linear functions. Parameters describing such piecewise linear functions depend on internal temperatures of the diode and IGBT. Values of these parameters are computed in the aided block, whereas internal temperatures of semiconductor devices are computed in the thermal model. The equivalent duty cycle *Veu*, adequate for operation of the DC–DC converter containing the modeled diode–transistor switch, is computed in CCM/DCM block shown in Figure 2. The value of *Veu* depends on duty cycle and frequency of the control signal, inductance of the inductor contained in the analyzed DC–DC converter and output current of this converter.

Controlled voltage and current sources existing in the main circuit are described as follows

$$E\_T = \frac{1 - V\_{eu}}{V\_{eu}} \cdot (V\_{2av} + V\_D) \tag{1}$$

$$E\_R = \frac{V\_{IGBT}}{V\_{cu}} + \frac{I\_{1av} \cdot R\_{IGBT}}{V\_{cu}} + \frac{I\_{1av} \cdot R\_D}{V\_{cu}^2} \cdot (1 - V\_{cu}) \tag{2}$$

$$G\_D = \frac{1 - V\_{cu}}{V\_{cu}} \cdot I\_{1av} \tag{3}$$

where current *I*1*av* and voltage *V*2*av* are denoted in Figure 2, *Veu* is voltage on controlled voltage source *Eu*, *VD* voltage and *RD* resistance are described by a piecewise linear characteristic of forward biased diode, whereas *VIGBT* voltage and resistance *RIGBT* are described by a piecewise linear output characteristic of switched-on IGBT. Parameters *VIGBT*, *VD*, *RD* and *RIGBT* depend on the device internal temperature (TjT for IGBT and TjD for the diode) and on current flowing through these devices. Values of the mentioned parameters correspond to voltages on controlled voltage sources EVIGBT, EVD, ERD and ERIGBT, respectively. These sources are included in the aided block. Descriptions of the mentioned parameters are as follows:

$$V\_D = \begin{cases} V\_{D1} \cdot \left(1 + \left(T\_{jD} - T\_0\right) \cdot \alpha\_{VD1}\right) \text{ if } I\_{2uv} < a\_1\\\ V\_{D2} \cdot \left(1 + \left(T\_{jD} - T\_0\right) \cdot \alpha\_{VD2}\right) \text{ if } a\_1 \le I\_{2uv} < a\_2\\\ V\_{D3} \cdot \left(1 + \left(T\_{jD} - T\_0\right) \cdot \alpha\_{VD3}\right) \text{ if } I\_{2uv} \ge a\_2 \end{cases} \tag{4}$$

*Energies* **2020**, *13*, 3033

$$R\_D = \begin{cases} R\_{D1} \cdot \left(1 + \left(T\_{jD} - T\_0\right) \cdot a\_{RD1}\right) \text{ if } I\_{2uv} < a\_1\\ R\_{D2} \cdot \left(1 + \left(T\_{jD} - T\_0\right) \cdot a\_{RD2}\right) \text{ if } a\_1 \le I\_{2uv} < a\_2\\ R\_{D3} \cdot \left(1 + \left(T\_{jD} - T\_0\right) \cdot a\_{RD3}\right) \text{ if } I\_{2uv} \ge a\_2 \end{cases} \tag{5}$$

$$V\_{IGBT} = \begin{cases} V\_{IGBT1} \cdot \left(1 + \left(T\_{jT} - T\_0\right) \cdot \alpha\_{VIGBT1}\right) \text{if } I\_{1av} < b\_1\\\ V\_{IGBT2} \cdot \left(1 + \left(T\_{jT} - T\_0\right) \cdot \alpha\_{VIGBT2}\right) \text{if } b\_1 \le I\_{1av} < b\_2\\\ V\_{IGBT3} \cdot \left(1 + \left(T\_{jT} - T\_0\right) \cdot \alpha\_{VIGBT3}\right) \text{if } I\_{1av} \ge b\_2 \end{cases} \tag{6}$$

$$R\_{IGBT} = \begin{cases} R\_{IGBT1} \cdot \left(1 + \left(T\_{jT} - T\_0\right) \cdot \alpha\_{RIIGHT1}\right) if \ I\_{1av} < b\_1\\ R\_{IGBT2} \cdot \left(1 + \left(T\_{jT} - T\_0\right) \cdot \alpha\_{RIIGHT2}\right) if \ b\_1 \le I\_{1av} < b\_2\\ R\_{IGBT3} \cdot \left(1 + \left(T\_{jT} - T\_0\right) \cdot \alpha\_{RIIGHT3}\right) if \ I\_{1av} \ge b\_2 \end{cases} \tag{7}$$

In Equations (5) and (6) symbols *VD*1, *VD*2, *VD*3, *RD*1, *RD*2, *RD*3, α*VD*1, α*VD*2, α*VD*3, α*RD*1, α*RD*2, α*RD*3, *a*1 and *a*2 denote parameters of a piecewise linear model of diode DC characteristic. In turn, in Equations (6) and (7) symbols *VIGBT*1, *VIGBT*2, *VIGBT*3, *RIGBT*1, *RIGBT*2, *RIGBT*3, α*VIGBT*1, α*VIGBT*2, α*VIGBT*3, α*RIGBT*1, α*RIGBT*2, α*RIGBT*3, *b*1 and *b*2 denote parameters of a piecewise linear model of IGBT output characteristics. Values of temperatures *TjD* and *TjT* are computed in the thermal model, whereas *T*0 represents reference temperature.

In the thermal model values of internal temperature of IGBT (*TjT*) and the diode (*TjD*) are computed with self-heating phenomena taken into account. The classical electrical analog of a DC compact thermal model described, e.g., in [11,16,17,22,26,27] is used. In this analog temperature corresponds to voltage in selected nodes of this analog, whereas dissipated power is represented by current sources. The ability to remove heat generated in the diode and in IGBT is characterized by thermal resistance. In the proposed model voltage source *VTa* represents ambient temperature, resistors *RthT* and *RthD* denote thermal resistance of IGBT and the diode, respectively. Average values of power dissipated in the considered semiconductor devices are represented by controlled current sources GTT and GTD. Currents flowing through these sources are described by the following formulas

$$\mathbf{G}\_{TD} = \left(V\_D + \frac{R\_D \cdot I\_{2uv}}{1 - V\_{cu}}\right) \cdot \frac{I\_{2uv}}{1 - V\_{cu}}\tag{8}$$

$$G\_{TT} = \left(V\_{IGBT} + \frac{R\_{IGBT} \cdot I\_{1av}}{V\_{cu}}\right) \cdot \frac{I\_{1av}}{V\_{cu}}\tag{9}$$

The proposed model can be used for computations of characteristics of DC–DC converters operating in CCM or DCM. In both mentioned modes of operation in each period of a control signal the transistor is turned on in time equal to the product of duty cycle d and period T. In turn, the diode is turned on in time equal to (1-d)·T in CCM and this time is shorter in DCM. In order to take into account influence of duty cycle d, frequency f of the control signal and inductance L of the inductor included in the tested DC–DC converter on voltage *Veu*, CCM/DCM block is included in the model. This block includes controlled current source *Ga*, voltage source *Va* of zero value, resistor *Ra* and controlled voltage sources *Eu* and *Ex*.

Voltage *Veu* on voltage source *Eu* is described by the formula

$$V\_{\rm cu} = \begin{cases} \; 0 \; if \; V\_x < 0\\ \; V\_x \; if \; 0 < V\_x \; : \; 1\\ \; 1 \; if \; V\_x > 1 \end{cases} \tag{10}$$

where voltage *Vx* on voltage source *Ex* is described as follows

$$V\_x = \text{LIMT}\left(\text{MAX}\left(d\_\prime \frac{d^2}{d^2 + 2 \cdot L \cdot f \cdot \frac{I\_{Va}}{V\_{2av} + V\_D}}\right), 0, 1\right) \tag{11}$$

In Equation (11) LIMIT (·) and MAX (·) are SPICE standard functions described, e.g., in the book [28] and *IVa* denotes current flowing through controlled current source *Ga* described with the formula of the form:

$$G\_{\rm d} = MAX(I\_{1av}, 0) \tag{12}$$

Resistor *Ra* must be included in this block due to formal rules of SPICE. Voltage source *Va* is used to monitor the value of current *IVa*.


**3.2 Features of peripheral blood response in slow consolidation of the mandible**

In this group of patients, before surgery, differences in immunological parameters from data obtained on the bone consolidation without complicating osteomyelitis were revealed. The number of monocytes (**Table 2**) was lower, than in the group with the slow regeneration of bone tissue by 59.3% (p < 0.05). There was a significant attenuation of the bactericidal activity of mechanisms: the level of lactoferrin was reduced 3.5 times (p < 0.05) was only 33.6% compared with the normal values. The same can be mentioned for complement activity-30.2% of the lower limit of normal and below 4.2 times (p < 0.05) than in the group with normal bone regeneration. Increased concentration of 37.4% IgM (p < 0.05) in peripheral blood also exceeded 49.0% and the normal values (**Table 2**). There was increased functional activity of CD45+ CD3+ -cells and it was more than 3.5 times (p < 0.05) differences in the recorded response of leukocyte migration inhibition (**Table 2**). Serum lysozyme was reduced by 5.8 times. Interestingly, the concentration of the receptor antagonist of IL-1ra concentration was lower for IL-1α (**Table 2**) that was not observed in patients with normal bone consolidation. IL-8 levels were reduced by 1.8-fold (p < 0.05) (**Table 2**) in comparison with patients with the other group.

The postoperative period was characterized by leukocytogenesis activation: leukocytosis was marked with the change in the ratio of group cells to the segmented cells. On day 3, a reduction in the number of T cells was shown (according to the dynamics of CD45+ CD3+ -cells) 40.8% (p < 0.05), confirming previously published data [60]. Recovery of these cell populations occurred in 10 days (**Table 2**). Perhaps this change in the amount of T cells in the early postoperative period resulting, ultimately, in disruption of bone regeneration, because it is known that both by generating INF-γ, and through prostaglandin mechanism of these cells involved in suppressing bone destruction and the formation of osteoclasts. Decrease in the relative number of B cells (based on dynamics of CD45+ CD19+ -cells) by 55.6% (p < 0.05) was observed later, on day 10 (**Table 2**).

Important changes were noted by the indicators characterizing the phagocytic activity of neutrophils. Amplification reactions were detected at 3 days after surgery when the spontaneous and stimulated superoxide radical production by neutrophils'superior control values was in the subgroup of 1.6- to 1.7-fold (p < 0.05). It can be assumed that the changes in neutrophil phagocyte system in the blood reflect the processes occurring in bone tissue. Of interest for the study was the dynamics of the content of lactoferrin at the stages of bone tissue consolidation (**Table 2**). As noted earlier, prior to the operation, its level was significantly reduced. Due to the fact that lactoferrin causes proliferation of osteoblasts and bone growth, it is not excluded that lowering the level of lactoferrin in the blood is also one of the reasons for slow regeneration of bone tissue. Later, after the operation, the lactoferrin content was always significantly lower in patients with delayed bone formation compared with patients who have bone fusion that took place at the usual time.

Slow bone formation was accompanied by significant increase (72.0%) in IgM levels (**Table 2**). The revealed changes were statistically significant differences between these patients with normal bone tissue regeneration. Inflammatory response was characterized by pronounced dynamics of slow-reacting acute-phase proteins. Specifically, on day 3, after surgery, ceruloplasmin concentration was higher by 1.9-fold (p < 0.05) than in patients with normal bone regeneration. The functional activity of T cells throughout the observation period was 2.0- to 3.3-fold higher (p < 0.05) than in patients with normal consolidation, while it exceeds the normal value of 1.5 times (**Table 1**). Active cationic proteins and myeloperoxidase neutrophilic granulocytes were higher than in patients with complication of osteogenesis, respectively, by 33.6% (p < 0.05) and 3.1-fold (p < 0.05).

**Laboratory**

**167**

TNF-α, pg/ml

IL-8, pg/ml

IL-10, pg/ml Ceruloplasmin,

C-reactive protein, mg/l

*\**

*p < 0.05 compared with preoperative*

*#*

*р < 0.05 compared with*

**Table 2.**

*Immunological*

 *parameters*

 *of peripheral*

 *blood at slowing bone formation.*

 *levels.*

*uncomplicated*

 *osteogenesis.*

 g/l

 **parameters**

**Normal**

**Before**

**3 days**

 **10 days**

 **1 month**

 **3 months**

**surgery**

**values**

0–50

0–50

<1.0

0.24–0.42

0–8

 9.09

0.22

 6.01

1.12\*

0.14

0.03\*,#

0.12

0.03\*,#

0.14

0.04\*,#

*Immunological Monitoring of Osteogenesis Disorder DOI: http://dx.doi.org/10.5772/intechopen.92099*

> 0.50

0.02

 0.75

0.16#

0.66

0.08

 0.51

0.12

 0.99

0.09\*,#

 2.19

0.07

 0.08

0.02\*

0.04

0.01\*

0.04

0.01\*

0.85

0.06\*

 24.15

4.78#

90.32

3.66\*,#

180.74

14.85\*,#

55.41

6.74\*,#

41.65

8.97\*

 22.65

6.58

 62.50

11.54\*,#

75.12

9.67\*,#

100.33

9.89\*,#

102.61

10.24\*,#

**Table 2.**

*Immunological parameters of peripheral blood at slowing bone formation.*


**3. Multiplicity of homologs**

176 Selected Topics in DNA Repair

Shereda, et al., 2008). These SSB complexes occlude up to 65 and 35 nucleotides of ssDNA, respectively (Filipkowski & Kur, 2007; Meyer & Laine, 1990; Shereda, et al., 2008). Other, less common SSB configurations have also been identified, including an alternative homopentameric Deinococcal SSB (Norais, et al., 2009; Sugiman-Marangos & Junop, 2010). In general, the N-terminal region of the SSB contains the oligomerization domain while the C-terminal region is implicated in heterologous protein interactions (Shereda, et al., 2008).

Fig. 1. RPA and SSB structures from eukaryotes, bacteria, and archaea. (A) The canonical eukaryotic RPA comprises three protein subunits, each containing OB-folds designated DBD

monomer. (C) Archaeal RPAs are diverse and display characteristics of both eukaryotic RPA and bacterial SSB. Examples from *H. salinarum* are shown. RFA6 resembles bacterial SSB, whereas RFA1 is uniquely archaeal. The genes encoding RFA2 and RFA7, which are found

The eukaryotic RPA is a heterotrimer composed of a large RPA1 (~72 kDa), medium RPA2 (~32 kDa), and small RPA3 (~14 kDa) subunit. RPA1 and RPA2 tend to be conserved subunits with a variable RPA3, though considerable variation can occur throughout domains (Lin, et al., 2008; Wold, 1997). RPA1 contains four OB-folds referred to as DNA binding domains (DBD). DBD A and B are centrally located, DBD C is in the C-terminal region, and DBD F is in the N-terminal region. RPA2 contains a single centrally located OBfold, DBD D, and RPA3 a single centrally located OB-fold, DBD E (Binz, et al., 2004; Oakley, et al., 2009; Pretto, et al., 2010). ssDNA binding occurs primarily through interaction with DBD A-D, which together are capable of occluding approximately 30 nucleotides (Blackwell & Borowiec, 1994; Iftode, et al., 1999; C. Kim, et al., 1992; Theobald, et al., 2003). A binding polarity of the complex is achieved through decreased ssDNA affinity from DBD A to D (Oakley, et al., 2009; Pretto, et al., 2010). The N-terminal domain of RPA1 is implicated in heterologous protein interactions and regulation, particularly through DBD F, while the Cterminal domain is involved in heterotrimer structure interactions (Broderick, et al., 2010;

A-F. Yellow OB-folds are DNA interacting domains, blue domains are involved in heterologous protein interactions, and gray domains are involved in maintenance of RPA structure. The N-terminus of RPA2 is the primary phosphorylation domain, denoted by red Ps. (B) The canonical bacterial SSB functions as a homotetramer and contains a single OBfold (yellow) and a C-terminal heterologous protein interacting domain (blue) per

in an operon, likely function as a multimeric protein, as do RFA3, RFA8, and RAL.

The highly conserved nature of the OB-fold has allowed identification of potential RPA/SSB homologs in the ever-expanding genome sequence database. To our knowledge, there have been no reports of genomes lacking an RPA/SSB homolog, with the possible exception of the crenarchaea *Pyrobaculum aerophilum* and *Aeropyrum pernix* (Luo, et al., 2007).

There are numerous excellent and extensive reviews covering the history, genetics and biochemical characterization of *E. coli* SSB and its encoding gene (Lohman & Ferrari, 1994; Meyer & Laine, 1990; Shereda, et al., 2008). The single gene, *ssb*, is located adjacent to, but divergently transcribed from, the *uvrA* gene (Brandsma, et al., 1985). In *Bacillus subtilis*, there are two SSB genes, *ssb* and *ywpH* (Lindner, et al., 2004). Although the amino acid sequences of the two proteins are similar, YwpH lacks the C-terminal region found in both the *E. coli* and *B. subtilis* SSBs. *B. subtilis ssb* is essential, but unlike in *E. coli*, is found between ribosomal protein genes. Of 87 bacterial genomes analyzed, all contained at least one SSB gene (Lindner, et al., 2004). Based on gene organization, four groupings were proposed: Group I organisms display *B. subtilis-*type gene organization and more than one SSB. Group II organisms contain only one gene for SSB. *E. coli* represents the type organism for Group III bacteria, where the single *ssb* gene is divergently transcribed from the adjacent *uvrA* gene. Group IV consists of organisms whose SSB organization does not resemble either *B. subtilis* or *E. coli*. *Thermotoga maritima* and *T. neopolitana* likely fall into Group IV, whereas *Thermoanaerobacter tengcongensis* is in Group I (Olszewski, et al., 2010; Olszewski, et al., 2008).

Recently, a novel homopentameric SSB, DdrB, was identified in *Deinococcus radiodurans*, which may challenge previous notions regarding canonical SSB structure (Norais, et al., 2009; Sugiman-Marangos & Junop, 2010). Restricted to the Deinococcal lineage, it may represent an adaptation related to the unusually high DNA repair capacity of this organism. However, it should be kept in mind that other SSB proteins that deviate from the traditional consensus may yet be identified (Norais, et al., 2009; G. Xu, et al., 2010).

Three separate genes encode the canonical eukaryotic RPA, which is the subject of several recent review articles (Binz, et al., 2004; Broderick, et al., 2010; Richard, et al., 2009; Wold, 1997). However, in higher plants each subunit of RPA may be represented by multiple genes whose proteins function in distinct processes (Sakaguchi, et al., 2009).

Both *Saccharomyces cerevisiae* and *Schizosaccharomyces pombe* contain a single gene for each of the three RPA subunits. All three are essential in *S. cerevisiae*, but RPA3 appears to be nonessential in *S. pombe* (Brill & Stillman, 1991; Cavero, et al., 2010; Dickson, et al., 2009; Maniar, et al., 1997). Given that RPA3 is the most variable, and the essential role that the intact protein plays in replication, an additional RPA3 gene may yet be identified that carries out this role in *S. pombe*. In addition to heterotrimeric RPA, a protein complex, Stn1/Ten1, resembling an RPA2/RPA3 dimer has been found to be necessary for telomere maintenance

Role of RPA Proteins in Radiation Repair and Recovery 179

one additional homolog that shows different domain structure, as seen in Figure 1 (Komori & Ishino, 2001; Pugh, et al., 2008; Robbins, et al., 2005). Other euryarchaea, such as *Methanococcus jannaschii, Methanobacter thermoautotrophicus*, and the Methanosarcinae, have proteins with four OB-folds, which may act as monomers, or in a complex with an adjacently encoded RPA2 homolog (Chedin, et al., 1998; Kelly, et al., 1998; Komori & Ishino, 2001; Lin, et al., 2008; Robbins, et al., 2004). In addition, *M. jannaschii* contains a two-OB-fold homolog which appears to function as a homotrimer (Robbins, et al., 2005). The *Pyrococcus furiosis* RPA more closely resembles eukaryotic RPA, with three distinct subunits that function as a heterotrimer and whose genes compose an operon (Komori & Ishino, 2001). In *H. salinarum*, Rfa3, Rfa8, and Ral, and Rfa2 and Rfa7 resemble the *P. furiosis* RPA with respect to operon structure and sequence homology to eukaryotic RPA (Figure 1). Rfa6 resembles the crenarchaeal SSB/RPA in gene and OB-fold structure, while Rfa1 appears more uniquely archaeal, with three OB-folds (DeVeaux, et al., 2007; Robbins, et al., 2005).

The roles of the RPA-like homologs in *H. salinarum* will be discussed further below.

*E. coli* SSB mutants were first isolated in a screen for DNA replication mutants (Meyer & Laine, 1990). Although the two best-characterized of these temperature-sensitive (ts) mutations, *ssb-1* and *ssb-113*, fail to grow at 42o C due to inability of the labile SSB to participate in DNA replication, *ssb-1* mutants have essentially normal phenotypes at the permissive temperature. The mutation (H55Y) resides in the OB-fold and affects the ability of the protein to form homotetramers (Shereda, et al., 2008). In contrast, even at the permissive growth temperature, *ssb-113* mutants are severely compromised in their ability to survive numerous DNA damaging agents, and display recombination deficiencies (Chase, et al., 1984). The extremely pleiotropic phenotype conferred by the amino acid change (P176S of 177) in *ssb-113* suggests that the protein interaction capabilities of the C-terminal region are necessary for more than just replication. A protein lacking only 10 amino acids from the Cterminus is non-functional (Curth, et al., 1996). Indeed, mutation of the terminal phenylalanine is lethal, and disrupts protein-protein interactions (Genschel, et al., 2000). These phenotypes provided support not only for the commonality of protein function in recombination, repair, and replication, but also early evidence that these roles were separable within an individual protein. In contrast, a *B. subtilis* mutant in which SSB lacks the Cterminal 35 amino acids, involved in interaction with the helicase PriA, is viable, demonstrating that this conserved region is not essential in all bacteria (Lecointe, et al., 2007). In the budding yeast *S. cerevisiae*, all three subunits of RPA are essential (Brill & Stillman, 1991; Maniar, et al., 1997). Like *E. coli ssb-113* mutants, ts-mutants of RPA1 are profoundly UV- and ionizing radiation (IR)-sensitive at the permissive temperature (Parker, et al., 1997). Several ts-mutants display a mutator phenotype, which is likely related to defects in replication rather than repair (Chen, et al., 1998). Small insertions in either the RPA1 DNAbinding domain or the N-terminal protein-interaction domain result in a marked decrease in survival after UV exposure; however, only the latter is defective in the cell-cycle response to such damage. In addition, only the DNA-binding mutant is defective in repair of UVinduced lesions (Longhese, et al., 1996; Teng, et al., 1998). The N-terminal domain is required for interactions with the clamp loader. This binding is abolished in the *rfa1-t11* mutation, which contains a single change (K45E) (H. S. Kim & Brill, 2001; Majka, et al., 2006; Umezu, et al., 1998). This same allele is deficient in many pathways requiring interactions

**4. Characterized mutations of RPA and SSB**

and protection in *S. cerevisiae*, *S. pombe* and *Candida albicans* (Sun, et al., 2009). Together with Cdc13, the Stn1/Ten1 complex may represent a highly conserved telomere binding RPA.

Mammals are thought to have only one nuclear RPA protein, which functions in both replication and repair (Fanning, et al., 2006; Iftode, et al., 1999; Wold, 1997). However, an alternative RPA2 homolog (RPA4) found only in mammals has been identified (Keshav, et al., 1995). RPA4 binds with the single RPA1 and RPA3 subunits to form an alternative RPA that does not interact with DNA polymerase α, and consequently, does not support replication (Haring, et al., 2010; Mason, et al., 2009; Mason, et al., 2010). Rather, its role is restricted to repair, particularly in quiescent cells. A mitochondrial SSB has also been identified in many eukaryotes, which has sequence and structural similarities to *E. coli* SSB (Curth, et al., 1994). In addition, two human proteins, hSSB1 and hSSB2, which structurally resemble bacterial SSB, have been reported. hSSB1 has been characterized, and appears not to be involved in replication, but is required for genome stability and DNA repair processes (Richard, Cubeddu, et al., 2011; Richard, Savage, et al., 2011).

Although much less is known about RPA genes in plants, the recent sequencing of higher plant genomes has revealed a large diversity of homologs (Sakaguchi, et al., 2009). Rice contains three genes each for RPA1 and RPA2, but only a single gene for RPA3 (Ishibashi, et al., 2006; Shultz, et al., 2007). Each RPA1 associates with a particular RPA2, and RPA3 is common among the different complexes. One complex is unique to chloroplasts, whereas the other two are nuclear. Like rice, *Arabidopsis* contains multiple homologs for the RPA1 and RPA2 subunits, but also has two RPA3 genes (Shultz, et al., 2007). In both plants, one of the large subunits has been shown to be non-essential for vegetative growth, but is required for meiosis, demonstrating specialization of function among the multiple species of protein (Chang, et al., 2009; Osman, et al., 2009). This homolog is involved in response to DNA damage as well as maintenance of telomeres (Takashi, et al., 2009). The degree of similarity among the multiple homologs suggests that they arose through duplication of ancestral genes after the establishment of the plant lineage.

By far the most diversity in RPA/SSB homologs is found in the archaea. Although many hypothetical RPA proteins have been identified in the numerous genomes analyzed, there is no quaternary structure that is common to all. In the crenarchaea, the RPA from *Sulfolobus solfataricus* is encoded by a single gene and exists as a monomer or a homotetramer (Haseltine & Kowalczykowski, 2002; Kerr, et al., 2001; Kerr, et al., 2003; Wadsworth & White, 2001). The different quaternary structures confer distinct binding capabilities (Rolfsmeier & Haseltine, 2010). Despite the similarity to bacterial SSB, and the ability of the gene to complement the lethality of an *E. coli ssb* mutation, the DNA-binding domain of this protein more closely resembles those of eukaryotic RPA1 (Haseltine & Kowalczykowski, 2002; Kerr, et al., 2003). Although the genomes of two related crenarchaea, *P. aerophilum* and *A. pernix*, have been reported to contain no obvious RPA or SSB homolog (Luo, et al., 2007), a previous study found a *Sulfolobus*-like SSB in *A. pernix* (Haseltine & Kowalczykowski, 2002). Given the recent identification of a novel SSB in *D. radiodurans*, and the fundamental role that ssDNA binding proteins play in replication and repair, it is likely that a protein carrying out these essential functions will be found in all organisms.

RPAs in euryarchaea present a multitude of OB-fold conformations that presumably provide unique functions (Lin, et al., 2005; Robbins, et al., 2005). Genomes of several euryarchaea, including *Thermoplasma acidophilum*, *Archaeoglobus fulgidus, Ferroplasma acidarmanus*, and *Halobacterium salinarum*, contain genes with similar organization to the crenarchaeal SSB/RPA gene, with one OB-fold; however, in each case, there is also at least

and protection in *S. cerevisiae*, *S. pombe* and *Candida albicans* (Sun, et al., 2009). Together with Cdc13, the Stn1/Ten1 complex may represent a highly conserved telomere binding RPA. Mammals are thought to have only one nuclear RPA protein, which functions in both replication and repair (Fanning, et al., 2006; Iftode, et al., 1999; Wold, 1997). However, an alternative RPA2 homolog (RPA4) found only in mammals has been identified (Keshav, et al., 1995). RPA4 binds with the single RPA1 and RPA3 subunits to form an alternative RPA that does not interact with DNA polymerase α, and consequently, does not support replication (Haring, et al., 2010; Mason, et al., 2009; Mason, et al., 2010). Rather, its role is restricted to repair, particularly in quiescent cells. A mitochondrial SSB has also been identified in many eukaryotes, which has sequence and structural similarities to *E. coli* SSB (Curth, et al., 1994). In addition, two human proteins, hSSB1 and hSSB2, which structurally resemble bacterial SSB, have been reported. hSSB1 has been characterized, and appears not to be involved in replication, but is required for genome stability and DNA repair processes

Although much less is known about RPA genes in plants, the recent sequencing of higher plant genomes has revealed a large diversity of homologs (Sakaguchi, et al., 2009). Rice contains three genes each for RPA1 and RPA2, but only a single gene for RPA3 (Ishibashi, et al., 2006; Shultz, et al., 2007). Each RPA1 associates with a particular RPA2, and RPA3 is common among the different complexes. One complex is unique to chloroplasts, whereas the other two are nuclear. Like rice, *Arabidopsis* contains multiple homologs for the RPA1 and RPA2 subunits, but also has two RPA3 genes (Shultz, et al., 2007). In both plants, one of the large subunits has been shown to be non-essential for vegetative growth, but is required for meiosis, demonstrating specialization of function among the multiple species of protein (Chang, et al., 2009; Osman, et al., 2009). This homolog is involved in response to DNA damage as well as maintenance of telomeres (Takashi, et al., 2009). The degree of similarity among the multiple homologs suggests that they arose through duplication of ancestral

By far the most diversity in RPA/SSB homologs is found in the archaea. Although many hypothetical RPA proteins have been identified in the numerous genomes analyzed, there is no quaternary structure that is common to all. In the crenarchaea, the RPA from *Sulfolobus solfataricus* is encoded by a single gene and exists as a monomer or a homotetramer (Haseltine & Kowalczykowski, 2002; Kerr, et al., 2001; Kerr, et al., 2003; Wadsworth & White, 2001). The different quaternary structures confer distinct binding capabilities (Rolfsmeier & Haseltine, 2010). Despite the similarity to bacterial SSB, and the ability of the gene to complement the lethality of an *E. coli ssb* mutation, the DNA-binding domain of this protein more closely resembles those of eukaryotic RPA1 (Haseltine & Kowalczykowski, 2002; Kerr, et al., 2003). Although the genomes of two related crenarchaea, *P. aerophilum* and *A. pernix*, have been reported to contain no obvious RPA or SSB homolog (Luo, et al., 2007), a previous study found a *Sulfolobus*-like SSB in *A. pernix* (Haseltine & Kowalczykowski, 2002). Given the recent identification of a novel SSB in *D. radiodurans*, and the fundamental role that ssDNA binding proteins play in replication and repair, it is likely that a protein

RPAs in euryarchaea present a multitude of OB-fold conformations that presumably provide unique functions (Lin, et al., 2005; Robbins, et al., 2005). Genomes of several euryarchaea, including *Thermoplasma acidophilum*, *Archaeoglobus fulgidus, Ferroplasma acidarmanus*, and *Halobacterium salinarum*, contain genes with similar organization to the crenarchaeal SSB/RPA gene, with one OB-fold; however, in each case, there is also at least

(Richard, Cubeddu, et al., 2011; Richard, Savage, et al., 2011).

genes after the establishment of the plant lineage.

carrying out these essential functions will be found in all organisms.

one additional homolog that shows different domain structure, as seen in Figure 1 (Komori & Ishino, 2001; Pugh, et al., 2008; Robbins, et al., 2005). Other euryarchaea, such as *Methanococcus jannaschii, Methanobacter thermoautotrophicus*, and the Methanosarcinae, have proteins with four OB-folds, which may act as monomers, or in a complex with an adjacently encoded RPA2 homolog (Chedin, et al., 1998; Kelly, et al., 1998; Komori & Ishino, 2001; Lin, et al., 2008; Robbins, et al., 2004). In addition, *M. jannaschii* contains a two-OB-fold homolog which appears to function as a homotrimer (Robbins, et al., 2005). The *Pyrococcus furiosis* RPA more closely resembles eukaryotic RPA, with three distinct subunits that function as a heterotrimer and whose genes compose an operon (Komori & Ishino, 2001). In *H. salinarum*, Rfa3, Rfa8, and Ral, and Rfa2 and Rfa7 resemble the *P. furiosis* RPA with respect to operon structure and sequence homology to eukaryotic RPA (Figure 1). Rfa6 resembles the crenarchaeal SSB/RPA in gene and OB-fold structure, while Rfa1 appears more uniquely archaeal, with three OB-folds (DeVeaux, et al., 2007; Robbins, et al., 2005). The roles of the RPA-like homologs in *H. salinarum* will be discussed further below.


*Article* **Composite Fibers Based on Polycaprolactone and Calcium Magnesium Silicate Powders for Tissue Engineering Applications**

**Cristina Busuioc 1 , Andrada-Elena Alecu 1, Claudiu-Constantin Costea 1, Mihaela Beregoi 2 , Mihaela Bacalum 3 , Mina Raileanu 3, Sorin-Ion Jinga 1 and Iuliana-Mihaela Deleanu 1,\***

	- 2 National Institute of Materials Physics, RO-077125 Magurele, Romania
	- 3 National Institute of Physics and Nuclear Engineering, RO-077125 Magurele, Romania
	- **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The present work reports the synthesis and characterization of polycaprolactone fibers loaded with particulate calcium magnesium silicates, to form composite materials with bioresorbable and bioactive properties. The inorganic powders were achieved through a sol–gel method, starting from the compositions of diopside, akermanite, and merwinite, three mineral phases with suitable features for the field of hard tissue engineering. The fibrous composites were fabricated by electrospinning polymeric solutions with a content of 16% polycaprolactone and 5 or 10% inorganic powder. The physico-chemical evaluation from compositional and morphological points of view was followed by the biological assessment of powder bioactivity and scaffold biocompatibility. SEM investigation highlighted a significant reduction in fiber diameter, from around 3 μm to less than 100 nm after the loading stage, while EDX and FTIR spectra confirmed the existence of embedded mineral entities. The silicate phases were found be highly bioactive after 4 weeks of immersion in SBF, enriching the potential of the polymeric host that provides only biocompatibility and bioresorbability. Moreover, the cellular tests indicated a slight decrease in cell viability over the short-term, a compromise that can be accepted if the overall benefits of such multifunctional composites are considered.

**Keywords:** polycaprolactone; diopside; akermanite; merwinite; electrospinning; bone scaffolds