**Author details**

Behnam Bohluli1 and Payam Varedi2\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Oral and Maxillofacial Surgery, Buali Hospital, Islamic Azad University of Medical Sciences, Tehran, Iran, and Craniomaxillofacial Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Oral and Maxillofacial Surgery, Qazvin University of Medical Sciences, Qazvin, Iran, and Researcher, Department of Oral and Maxillofacial Surgery, Craniomaxillo‐ facial Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran


**Takeshi Yamaguchi \*,† and Hiroshi Yoshikawa †**

Department of Computer Engineering, College of Science and Technology, Nihon University,

7-24-1 Narashinodai, Funabashi-shi, Chiba 274-8501, Japan<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>Tel.: +81-47-469-5391

† These authors contributed equally to this work.

Received: 10 September 2018; Accepted: 13 October 2018; Published: 17 October 2018

**Abstract:** We have developed an output device for a computer-generated hologram (CGH) named a fringe printer, which can output a 0.35-μm plane-type hologram. We also proposed several CGH with a fringe printer. A computer-generated rainbow hologram (CGRH), which can reconstruct a full color 3D image, is one of our proposed CGH. The resolution of CGRH becomes huge (over 50 Gpixels) due to improvement of the fringe printer. In the calculation, it is difficult to calculate the whole fringe pattern of CGRH at the same time by a general PC. Furthermore, since the fine pixel pitch provides a wide viewing angle in CGRH, object data, which are used in fringe calculation, should be created from many viewpoints to provide a proper hidden surface removal process. The fringe pattern of CGRH is calculated in each horizontal block. Therefore, the object data from several view points should be organized for efficient computation. This paper describes the calculation algorithm for huge resolution CGRH and its output results.

**Keywords:** holography; computer-generated hologram; rainbow hologram; high resolution; fringe printer; wide viewing angle

#### **1. Introduction**

A hologram has 3D information such as the binocular parallax, convergence, accommodation, and so on. Therefore, a reconstructed image of a hologram provides natural spatial effects. A computer-generated hologram (CGH) whose interference fringes are calculated on a computer can reconstruct a virtual object. At the moment, since the hologram requires a fine pixel pitch that is almost the same as the wavelength of the light, the output device of CGH is not common. The electron beam writer provides an excellent quality CGH [1]. Furthermore, there were some papers whose CGHs were output by a laser lithography system [2]. However, both the equipment and running costs of the electron beam writer are very expensive. On the other hand, Sakamoto et al. had proposed a CGH printer with a CD-R writer [3]. Since the recording material is CD-R, the running cost of this CD-R printer is very inexpensive. However, the size of this CGH is restricted by the size of the CD-R.

We have developed the output device of the plane-type CGH, which is called the fringe printer [4]. This printer consists of a laser, an SLM and an X-Y translation stage with stepper motors. The pixel pitch of this printer is 0.35 μm, and the size of CGH depends on the X-Y translation stage. Our group has investigated several types of CGHs [5,6]. The Fresnel-type CGH is very popular in CGH [7–9]. However, all light is diffracted by the Fresnel fringe pattern, and it is difficult to reconstruct the full-color 3D image. Therefore, we proposed a computer-generated rainbow hologram (CGRH) [10]. The rainbow hologram, which was proposed by Benton [11], can reconstruct the full-color 3D image by sacrificing the vertical parallax. Due to the development of the fringe printer, the resolution of CGRH increases to over 50 Gpixels. The calculation system requires large memory when the entire fringe pattern is calculated. It is difficult to calculate on a common PC.

In this paper, we have proposed the large CGRH calculation. Since the computation of CGRH is separated on the vertical, it can be calculated independently. In addition, we have described the deterioration of image quality caused by this separation and proposed the solution. Moreover, the CGRH output by the fringe printer has a wide viewing angle, and the reconstructed image should have the correct occlusion. Therefore, object data created from different viewpoints are organized to fit the separate fringe pattern calculation. Furthermore, we have revealed the problem that is caused on the hologram plane and proposed the solution. As a result, we obtain a full-color reconstructed image from about a 56-Gpixel CGRH by a common PC.

#### **2. Rainbow Hologram**

The rainbow hologram [11] is well known as a full-color reconstructed image by white light. By sacrificing the vertical parallax, the rainbow hologram, which is a transmission hologram, can reconstruct the full-color image.

Figure 1 shows the schematic image of the rainbow hologram recording. The viewing area of the reconstructed image from the master hologram is limited by the horizontal slit and recorded as a transfer hologram. Figure 2 shows the reconstruction of the rainbow hologram with white light. The observer can see the monochromatic image from the same position of the horizontal slit. If the observer changes the viewing position on the vertical direction, the color of the reconstructed image also changes. The full-color image can be obtained with three slits corresponding to red, green and blue.

**Figure 1.** Recording of the rainbow hologram.

**Figure 2.** Reconstruction of the rainbow hologram by white light.

#### **3. Computer-Generated Rainbow Hologram**

The calculation algorithm of CGRH had been proposed in [10]. Therefore, this section elucidates the gist of the proposed method. The calculation flow can be divided into two steps. In the first step, the 1-D complex amplitude named the sub-line is calculated from sliced object data. Figure 3 shows the calculation geometry of the sub-line. Since the reconstructed image of CGRH only has horizontal parallax, one sub-line should be formed by a single horizontal slice of the object data. The object data are supposed as a collection to the self-illuminated points and can be divided by the position of each point.

**Figure 3.** Calculation geometry for the sub-line.

In the second step, we add an off-axis reference, which has a vertical incident angle, to the sub-line. Figure 4 shows the second step of CGRH computation. The 1-D hologram (sub-line) does not have vertical diffracted light. Therefore, the proposed calculation used a reference beam, which only changes the phase on the vertical direction, and part of fringe pattern named the holo-line was calculated. Since the proposed calculation uses the same sub-line in each holo-line, only 1-D object beam calculation is required.

**Figure 4.** Two steps to calculate the holo-line of a computer-generated rainbow hologram.

When the calculated CGRH is reconstructed, a cylindrical lens is required to converge the diffracted light to the viewing position. However, this is an undesirable reconstruction when using the lens. Therefore, we added the lens effect to the reference light. This change provides the wavelength selection of the rainbow hologram, and diffracted light by the entire calculated fringe pattern is observed at the same position.

#### *3.1. Calculation of the Fringe Pattern*

Computation of the holo-line follows the interference computation between the wavefront of the object beam and the wavefront of the reference beam. The intensity pattern on the hologram plane *I*(*x*, *y*) is described as:

$$I(x, y) = \left| \mathcal{O}(x) + \mathcal{R}(y) \right|^2,\tag{1}$$

where *O*(*x*) is the complex amplitude of the object beam on each horizontal slice plane and *R*(*y*) is the complex amplitude of the reference beam. Since the reference beam of the rainbow hologram is usually a collimated beam, *R* of Equation (1) only changes in the *y* direction.

The location of the *i*-th object point is specified as (*xi*, *yi*, *zi*). Each point has a real-valued amplitude *ai* and a relative phase *φi*. The object light of a single sub-line is defined as:

$$\mathbf{O}(\mathbf{x}) = \sum\_{i=1}^{N} \frac{a\_i}{r\_i} (\cos \phi\_i + j \cdot \sin \phi\_i),\tag{2}$$

$$r\_i = \sqrt{(x\_i - x)^2 + z\_i^2} \tag{3}$$

where *N* is the number of object points for the single sub-line and *ri* is the distance between the *i*-th object point and the point (*x*) on the hologram. The wavenumber is defined as *k* = 2*π*/*λ*, where *λ* is the free space wavelength of the light. The rainbow hologram displays a 3D image on or close to the hologram plane. The object point that is close to the hologram plane makes a high intensity fringe pattern according to Equation (2). Therefore, Equation (2) is changed into the following equation, which can avoid divergence.

$$O(x) = \sum\_{i=1}^{N} \frac{a\_i}{r\_i + a} (\cos \phi\_i + j \cdot \sin \phi\_i),\tag{4}$$

where *α* is a positive constant value.

The complex amplitude *R*(*y*) is determined by:

$$\mathcal{R}(y) = a\_{rcf} (\cos \phi\_r + j \cdot \sin \phi\_r),\tag{5}$$

$$
\phi\_r = ky \cdot \sin \theta\_{ref,} \tag{6}
$$

where *θref* is the vertical incident angle of the illumination beam and *φr* is the phase component of the reconstructed beam.

The intensity pattern of Equation (1) becomes:

$$I(\mathbf{x}, y) = |\mathbf{O}(\mathbf{x})|^2 + |\mathbf{R}(y)|^2 + 2 \cdot \text{Re}\{\mathbf{O}(\mathbf{x})\} \cdot \text{Re}\{\mathbf{R}(y)\} + 2 \cdot \text{Im}\{\mathbf{O}(\mathbf{x})\} \cdot \text{Im}\{\mathbf{R}(y)\},\tag{7}$$

where Re{*C*} and Im{*C*} take the real and imaginary part of the complex number *C*, respectively. The first and second term are the DC term, which does not contribute to the reconstructed image. For the calculation of CGRH, the only the third and fourth term are calculated.

To converge the diffracted light to the horizontal slit, the phase component of the reference beam in Equation (6) is changed into the following equation.

$$
\phi\_r = kr \cdot (\sin \theta\_{ref} - \sin \theta\_s),
\tag{8}
$$

$$\theta\_s = \tan^{-1} \frac{y}{V\_d} \,\tag{9}$$

where *θs* is the convergence angle to the horizontal slit and *Vd* is the distance between the hologram plane and the viewpoint.

#### *3.2. Calculation Area*

The maximum diffraction angle of the CGH depends on the pixel pitch of output device. In this paper, we used a fringe printer, which can output a 0.35-μm pixel pitch CGH for the output device. Since the object point that is close to the hologram requires a fine pixel pitch, the calculation area on the hologram plane should be limited by the coordinate of the object point according to the following equation.

$$d \le \frac{\lambda}{2(\sin \theta\_{obj} - \sin \theta\_{ref})} \tag{10}$$

where *d* is the pixel pitch of the CGH and *θobj* and *θref* are the incident angles of the object beam and the reference beam, respectively. In the actual calculation, we employed the virtual window (as shown in Figure 5), which only passes through the object light, satisfying Equation (10).

**Figure 5.** Addition of the calculation area by the virtual window.

#### **4. Point Cloud Creation**

The object to be recorded is approximated as a collection of self-illuminated points. Each point has *x*, *y*, *z* coordinates, a phase component and a real-valued amplitude of R, G, B. Since CG data are usually used as the object, we have to convert CG data to the point cloud data [7].

For the creation of the point cloud from CG data, the OpenGL library is employed as the 3D-API [12]. By using a color buffer and a z-buffer, which are calculated by OpenGL, the point cloud is calculated. However, the color buffer of the calculated point cloud is made from a single viewpoint. Therefore, when the viewer changes position, the absence of the hidden surface's data causes a problem: hole and overlap. In order to solve this problem, we employed multi-view rendering, which makes multiple point cloud data from several viewpoints. Accordingly, the reconstructed image shows the correct occlusion in every viewpoint.

#### *4.1. Shift Point Data*

Since the object point that is close to the hologram plane is calculated by Equation (4) to avoid divergence, the intensity of the object beam becomes weak, and the reconstructed image has dark bands on the hologram plane, as shown in Figure 6c. Figure 6a is a CG object used in this research, and the CG object is placed as shown in Figure 6b; the horizontal white arrow shows the hologram plane. Therefore, the object points that are close by less than a certain distance from the hologram plane need to be shifted a bit far from the hologram plane, as shown in Figure 7. Figure 8 shows the original point cloud data and the shifted point cloud data. As is evident from Figure 8, the shifted point cloud data seem almost the same as the original data when the observer sees from front.

a) CG object front view

b) CG object top view

**Figure 6.** CG object and simulated image including the dark bands problem.

**Figure 8.** Point cloud data with and without shift.

#### *4.2. Reallocation of the Point Cloud*

According to the performance improvement of the output device, the resolution of CGH becomes very high (over 50 Gpixels). It is difficult to calculate the whole fringe pattern of CGRH at the same time by a general PC. In the calculation of CGRH, each holo-line is independent, as described in Reference [10]. If the object points are only divided with the vertical coordinate, the reconstructed image has distortion. When the hologram is reconstructed, the observer sees diffraction light from the point of the hologram plane, which has to be linearly aligned with the viewpoint and object point. Therefore, the object points are divided with the straight line connecting the viewpoint with the each edge of the holo-line, as shown in Figure 9.

**Figure 9.** Split object data for each horizontal calculation block.

### **5. Normalization of the Fringe Pattern**

The calculated fringe pattern should be normalized by the entire fringe pattern. Otherwise, each segment reconstructs the difference in the contrast of the image. Especially since the horizontal block of CGRH is calculated for different numbers of object points, the difference in the contrast of the image is very large. In this research, the calculated fringe pattern is stored as the floating-point number and normalized by a range of 3*σ* after the calculation of the whole fringe pattern [13]. This *σ* is the standard deviation calculated from the intensity of the fringe pattern.

#### **6. Results**

#### *6.1. Fringe Printer*

Figure 10 shows the schematic image of the fringe printer. The fringe printer consists of a laser, an X-Y translation stage, liquid crystal on silicon (LCoS) as the spatial light modulator and optical parts. A fractional part of the entire holographic fringe is displayed on the LCoS, and its demagnified image is recorded on a holographic plate. Then, the plate is translated by the X-Y stage to expose the next segment of the fringe. Table 1 shows the fringe printer's parameter. Holographic material VRP-M, which is manufactured by Slavich, is used as the holographic recording material.VRP-M is a silver halide material.After development, VRP-M is bleached to improve the diffraction efficiency.

**Table 1.** Parameters of the improved fringe printer.

**Figure 10.** Schematic of the fringe printing system.

#### *6.2. Reconstructed Images*

Table 2 shows the parameter of CGRH. For the full-color reconstruction, we calculate three fringe patterns, which use different wavelengths, as shown in Table 2, from point cloud data. The calculated fringe patterns are synthesized to an 8-bit gray-scale bitmap pattern. We used a Windows 10 64-bit PC, which has Intel Core i7 7700 K CPU and 32 GB memory. By using the proposed method, an over 50-Gpixel CGRH was calculated with uniform contrast. Since the normalization had to be calculated after the entire fringe pattern calculation, the previous method only created about a quarter size of the memory size, because the fringe pattern is calculated as a float value. After the point cloud data creation, it took 14.5 min to process and to prepare the fringe calculation such as the shift of object points, the addition of the calculation area and sorting the point cloud data for each holo-line. The computation time of the fringe pattern was approximately 50 min. Figure 11 shows the reconstructed images from several viewpoints. A white LED light, called HOLOLIGHT [14], was used as the illumination light. HOLOLIGHT can easily make light that is close to the collimated light. Reconstructed images show good color reproduction compared with the original CG in the horizontal direction. In the change of the viewpoint in vertical direction, the color spectrum changes from red to blue, as shown in Figure 11a,e.

Figure 12 shows the numerical reconstruction for with and without shift object points, which are placed on or close to the hologram plane. To apply shift object data, the dark bands of Figure 12b are solved in Figure 12a. Figure 13 shows the reconstruction from the different normalizations. To change the normalization of each holo-line to the entire hologram, the contrast of the reconstructed image became uniform. These numerical simulations employed the algorithm of [15].

**Table 2.** Parameter of the hologram.

a) Top view

b) Left view c) Front view d) Right view

e) Bottom view

**Figure 11.** Reconstructed images from several viewpoints.

a) w/ shift object point b) w/o shift objet point

**Figure 12.** Comparison of with and without shift object data (numerical reconstruction).

a) Normalization as a whole b) Normalization as a each Holo-line

**Figure 13.** Comparison of the normalizations (numerical reconstruction).

#### **7. Conclusions**

In this research, we have calculated and output an over 50-Gpixel CGRH that reconstructs a full-color 3D image. The reconstructed images shows good color reproduction. For the calculation of the large CGRH, we proposed split calculation for each holo-line. To apply this calculation model, several object data, which were created from different viewpoints, were managed. In addition, to employ the shift object data and the normalization as a whole, the output CGRH provides good reconstructed images.

**Author Contributions:** Conceptualization, T.Y. and H.Y.; methodology, T.Y. and H.Y.; software, T.Y.; validation, T.Y. and H.Y.; formal analysis, T.Y. and H.Y.; investigation, T.Y.; resources, T.Y. and H.Y.; data curation, T.Y.; writing—original draft preparation, T.Y.; writing—review and editing, T.Y. and H.Y.; visualization, T.Y.; supervision, T.Y.; project administration, T.Y.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank Yayoi Nakaguchi for CG data creation.

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

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **References**

c 2018 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/).


**Georg J. Seifert**

Department of Applied Genetics and Cell Biology, University of Natural Resources and Life Science, Muthgasse 18, 1190 Vienna, Austria<EMAIL_ADDRESS>Tel.: +431-47654-94144

Received: 9 April 2018; Accepted: 17 May 2018; Published: 31 May 2018

**Abstract:** The Fasciclin 1 (FAS1) domain is an ancient structural motif in extracellular proteins present in all kingdoms of life and particularly abundant in plants. The FAS1 domain accommodates multiple interaction surfaces, enabling it to bind different ligands. The frequently observed tandem FAS1 arrangement might both positively and negatively regulate ligand binding. Additional protein domains and post-translational modifications are partially conserved between different evolutionary clades. Human FAS1 family members are associated with multiple aspects of health and disease. At the cellular level, mammalian FAS1 proteins are implicated in extracellular matrix structure, cell to extracellular matrix and cell to cell adhesion, paracrine signaling, intracellular trafficking and endocytosis. Mammalian FAS1 proteins bind to the integrin family of receptors and to protein and carbohydrate components of the extracellular matrix. FAS1 protein encoding plant genes exert effects on cellulosic and non-cellulosic cell wall structure and cellular signaling but to establish the modes of action for any plant FAS1 protein still requires biochemical experimentation. In fungi, eubacteria and archaea, the differential presence of FAS1 proteins in closely related organisms and isolated biochemical data suggest functions in pathogenicity and symbiosis. The inter-kingdom comparison of FAS1 proteins suggests that molecular mechanisms mediating interactions between cells and their environment may have evolved at the earliest known stages of evolution.

**Keywords:** arabinogalactan proteins; cellulose; pectin; matricellular proteins; SOS5; periostin; Mpb83


**2. Absolute Distance Measurement by optical interferometry**

Absolute Distance Measurements (ADM) can be performed by several optical methods. Generally, one has non-interferometric methods for large ranges with moderate accuracies and interferometric methods for high accuracies and moderate ranges, limited by the coherence length of the source. ADM can also be performed on the basis of low-coherence or white light interferometry but, in this case, the measuring range is limited to a few millimetres. Lately, the measuring range of interferometric methods increased due to the increase of the coherence length and the development on new coherent sources.

ADM interferometric techniques are typically based on the use of multiple or time varying wavelength sources. Single wavelength interferometry is a powerful tool in displacement measurement when high accuracy is required, however, as a major drawback, is only applicable to relative measurements. To measure an absolute distance it is necessary to use incremental interferometry: to measure a displacement it is necessary to fix a reference position and to carefully displace a suitable reflector from that position to the final one, and basically counting each interferometer fringe event. This must be done avoiding any optical misalignment that would result in a loss of the interference signal and therefore of the measure.

Multiple Wavelength Interferometry (MWI) is a technique were the use of (at least) two different wavelengths allows the generation of a synthetic wavelength much longer than the two individual optical wavelengths, actually increasing the non-ambiguity range (NAR) for interferometry (Dändliker et al., 1988; Dändliker et al., 1995). In MWI, each synthetic wavelength (one or several) is generated using two different and very accurately known optical wavelengths. By selecting a small synthetic wavelength it is possible to achieve high resolution (at the micrometre level or even smaller). However the intrinsic ambiguity of the measurement limits the measurement range. To overcome the ambiguity, a chain of increasing synthetic wavelengths is generated (or a combination with another sensor) to cover the required range (Salvadé et al., 2000). For large ranges, several wavelengths are required and MWI can become indeed a very complex solution.

In a first approach, FSI is equivalent to MWI. In FSI, the generation of the synthetic wavelength is based on frequency sweeping the laser source within a given sweep range. The technique is not new, has strong similarities to radar, dating back to the 80's (Kikuta et al., 1986), but it was not studied extensively until the development of tunable lasers and the emergence of External Cavity Diode Lasers (ECDL) (Hecht, 2001). As frequency sweeps, detection electronics counts synthetic wavelength fringes (temporal "synthetic fringes") without ambiguity, thus making it particularly interesting for large measurement ranges (Thiel et al., 1995; Stone, 1999; Edwards et al., 2000; Coe et al., 2004; Cabral & Rebordão, 2005; Swinkels et al., 2005). While in Double Wave interferometry (DWI) – which is a particular case of MWI - we have a fixed synthetic wavelength, while in FSI, as the frequency is being swept, the value of the synthetic wavelength is decreasing down to a value defined by the total sweep range.

In contrast to MWI, FSI does not require independent stabilized and well known laser sources and relies only on a tunable laser and a frequency sweep range measurement subsystem, normally based on a Fabry-Pérot interferometer (FP).

As in DWI, the synthetic wavelength Λ is inversely proportional to the frequency sweep range Δν (see next section). While in MWI we measure only the fractional part of the synthetic wavelength fringe, in FSI both the fractional and integer number of synthetic fringes can be measured. Thus, the absolute value of the OPD (between the two arms of a Michelson interferometer) will be determined without ambiguity.

It is not possible to elaborate an objective comparison between different techniques without an a priori definition of the sensor requirements: maximum range, accuracy, technological complexity, reliability, cost, etc. Nevertheless, a trade-off between the two basic methods, FSI and MWI, can be done in a qualitative way, considering the three main parameters: measurement distance, measurement uncertainty and overall complexity of the sensor subsystem.

FSI main advantage is the capability to perform large range measurements due to the nonambiguity nature of the technique, limited only by the coherence length of the tunable laser (a few hundred metres). FSI main drawback is the sensitivity to drift that, even with a compensation method, will limit the accuracy at the micrometre level. With an affordable complexity in the frequency sweeping (i.e. synthetic wavelength) measurement sub-system, for large distances, FSI can achieve a relative accuracy around 10-5. With a high degree of complexity (locking the laser to a resonant cavity) accuracies can be improved by one or two orders of magnitude.


**4. Bone remodeling (bone turnover) shift**

Orthopedic infection and excessive production of pro-inflammatory cytokines [15] influence the bone remodeling balance. Such hyperproduction can be triggered by infection itself and can increase when the pathogen persists inside the osteoblasts. Furthermore, some pathogens can additionally increase the inflammatory response because of paracrine action of their factors, interfering with the cytokine balance [26]. As it will be shown later, the inflammatory response tends to turn into a hyperergic response because of excessive activation of the cell immunity, which is an important factor for chronization of the process.

One of the most well-studied bacterial pathogens that is convenient for illustration is *S. aureus*. Many authors [14, 15, 27] show that *S. aureus* influences both parts of the bone turnover-bone resorption and ossification. During infection, *S. aureus* not only destroys (damages) osteoblasts through necrosis and apoptosis, but also lowers their proliferation rate. Influence on bone resorption is based on rapid maturation and additional activation of the osteoclasts by this pathogen. This mechanism is based on excessive synthesis of prostaglandin Е2 (PGE2) by osteoblasts and persisting of *S. aureus* inside the osteoblasts, as well as auto and paracrine regulatory mechanisms in which PGE2 plays its biochemical role. The synthesis of osteoprotegerin (OPG) is also impaired because of lowering of the level of corresponding mRNA; [28] as a result, more RANK-ligand (RANKL) molecules remain unbound and act as osteoclast activators. Excessive activation of the osteoclasts and lowering of the number and proliferation rate of the osteoblasts lead to obvious shift of the bone turnover to bone resorption. As a result, the loss of bone tissue and impairment of its mineralization at the site of orthopedic infection (bone infection) can lead to difficulties in bone consolidation in patients with bone defects and/or fractures (**Figure 3**).

"Red blocks" of this part include the OPG and RANK-ligand (RANKL) level changes.

"Yellow blocks" include the pre-existing low level of the osteoprotegerin. An example is a case of chronic osteomyelitis development in a patient with preexisting low-speed bone turnover and associated osteoporosis with OPG synthesis impairment.

These markers are rarely used in routine clinical practice but can be of a certain value when an appropriate laboratory is available.

*Clinical Implementation of Bone Regeneration and Maintenance*

destruction of osteoblasts.

**3. Osteoblast cell death and proliferation lowering**

One of the most obvious effects of bacterial pathogen on a bone tissue is the

Destruction of osteoblasts includes necrotic and apoptotic pathways. Papers contributing to the study of *S. aureus'* influence on the osteoblast culture show that these two pathways work independently. Apoptosis is triggered by ligand TRAIL

*Pathogenetic stages and processes associated particularly with osteoblast cell death and proliferation lowering. The link between bone matrix destruction processes and associated decrease in osteoblast proliferation rate (will*

*be shown and illustrated in the corresponding part of the chapter).*

**140**

**Figure 2.**

#### **Figure 3.**

*Pathogenetic stages and processes associated particularly with bone turnover shift.*

OPG is a soluble glycoprotein synthesized by different cell types, including osteoblasts [29]. This compound can be found in monomeric (50 kDa) or dimeric (120 kDa) form (the dimeric form contains disulfide bonds between monomers). Dimeric form has a higher affinity to RANKL than a monomeric one, and the chemical analogues of OPG should have the features of dimeric form.

RANKL (RANK-ligand) is a compound synthesized by several cell types. It can be expressed in three different molecular forms: transmembrane trimer, primary (secreted) form, and cell ectodomain [30]. RANKL serves as a ligand for RANK receptor that activates osteoclasts. RANKL is a target for osteoprotegerin; if OPG is secreted in low amounts, more RANKL remains unbound and the intensity of resorption increases.

Therefore, a "pathogenetic chain" is as follows: infection-osteoblast cell death and proliferation rate decrease-OPG synthesis decrease-RANKL inhibition decrease-osteoclastogenesis activation-bone resorption increase-shift of bone remodeling toward a resorption prevalence-decrease in bone tissue quality and quantity in the infection site. The resulting clinical effect is slowing of the consolidation of the bone tissue defects, pseudarthroses, and related conditions.

#### **5. Matrix destruction**

The matrix destruction in infection can be explained by several factors. The "starting point" in pathogenesis is the infection itself. *S. aureus* is, as shown by

**143**

**Figure 4.**

*Metabolic Disorders in Patients with Chronic Osteomyelitis: Etiology and Pathogenesis*

several authors [14, 31, 32], capable of persisting in osteoblasts and that leads to additional stimulation of inflammatory process that stepwise turns into local hyperergic reaction. It is important to take into account that pathogens also play a role in matrix destruction—particularly *S. aureus* can adhere to matrix elements and

The hyperergic reaction leads to excessive synthesis of matrix metalloproteases (MMPs). Among them, MMP-2 (gelatinase A) and MMP-9 (gelatinase B) [34] play the main role. Excessive synthesis of MMP is a key point in the process because it

• Osteoblast cell death and lowering of the proliferation rate; this effect weakens

• MMPs damage the matrix by themselves, being synthesized in excessive

*Pathogenetic stages and processes associated particularly with bone matrix destruction.*

*DOI: http://dx.doi.org/10.5772/intechopen.92052*

the regenerative potential of bone tissue.

destroy them [33].

has two major effects:

amounts [34, 35].


**Author details**

Sunshine Arnold1 and Debra Esernio-Jenssen2

1 Team Coordinator of the Child Protection Team, University of Florida, USA

2 Medical Director of the Child Protection Team, Department of Pediatrics, University of Florida, USA

## **References**

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**7. Conclusions**

118 Telemedicine

**Author details**

Sunshine Arnold1

Florida, USA

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1 Team Coordinator of the Child Protection Team, University of Florida, USA

2 Medical Director of the Child Protection Team, Department of Pediatrics, University of

[1] Menard, K. S, & Ruback, R. B. Prevalence and processing of child sexual abuse: a multi-data-set analysis of urban and rural counties. Law Hum Behav. (2003). , 27(4),

[2] Tsai, S. H, Kraus, J, Wu, H. R, Chen, W. L, Chiang, M. F, Lu, L. H, Chang, C. E, & Chiu, W. T. The effectiveness of video-telemedicine for screening patients requesting emergency air medical transport (EAMT). Journal of Trauma (2007). , 62, 504-511.

[3] MacLeod KJMarcin JP, Boyle C, Miyamoto S, Dimand RJ, Rogers KK. Using teleme‐ dicine to improve care delivered to sexually abused children in rural, underserved

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[5] Alexander, R, & First, K. Telemedicine and child abuse examinations. Pediatric An‐

Telemedicine has proved to be a very useful method in conducting child abuse assessments to rural areas in the absence of local child abuse experts. The increases in child abuse reporting and lack of experts qualified to medically assess abuse cases further support the use of telemedicine. A variety of applications and uses provides a wide range of possibilities at varying costs that can be adapted by other child welfare programs throughout the world and have been demonstrated nationwide in the U.S. Telemedicine assessments and evidence obtained during the examinations have been successfully used in criminal and civil court without legal challenge. The use of telemedicine in child abuse will ensure accurate diagnosis by an expert, appropriate treatment and follow up, and reduce risk of future maltreatment.

**Section 2**

**Telemedicine: Present and the Future**

**Telemedicine: Present and the Future**

**Chapter 7**

**Update on the Most Rural American Telemedicine**

As previously published (1) the Avera Health system launched its telemedicine program by offering consultation by video connectivity from the main tertiary hospital in the largest city of the multi-state North Central Region of the United States to some of its smallest partner clinics and hospitals. Between 1993 and 2004, medical providers and patients learned what it was like to practice medicine and receive care via a telemedicine connection. A major growth spurt in Avera's rural telemedicine program came in 2004 after the initiation of a virtual ICU service staffed by intensivist physicians and critical care nurses; Avera *e*ICU CARETM1. Since 2004, Avera has initiated and rapidly expanded multiple other telemedicine programs to meet demand for additional services and coverage. These around-the-clock, always available services are unique as stand-alone programs, but combined provide one of the most robust

In the previous report, the goals, expectations and consequences of the Avera *e*ICU CARE program were described. Avera *e*ICU CARE initially started with the system's tertiary hospital, Avera McKennan Hospital & University Health Center, serving as the hub location for the provision of twenty-four hour per day remote patient care and monitoring of seriously ill patients in three medium-sized rural hospitals. Over time it evolved to include several more hospitals of that size called "Rural Regional Hospitals." Additionally, remote Critical Access Hospitals (CAHs) began to request *e*ICU coverage. Intensivist-led medical supervision and

> © 2013 Zawada Jr et al.; licensee InTech. This is an open access article 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.

© 2013 Zawada Jr et al.; licensee InTech. This is a paper 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.

**Program — The Present and Future**

Edward T. Zawada Jr, Dana Buus, Don Kosiak,

Additional information is available at the end of the chapter

**1.1. Review of previous experience and accomplishments**

Pat Herr and Michael Heisler

http://dx.doi.org/10.5772/56589

telemedicine platforms on the planet.

1 Avera eICU CARE is a registered trademark of VISICU, Inc.

**1. Introduction**


9. Conclusion

Lignocellulosic materials are the promising substrate for bio-hydrogen and methane production. The main compositions of lignocellulosic materials are cellulose, hemicellulose, and lignin. In order to obtain the underlying monosugar, the appropriate pretreatment methods are recommended to hydrolyze hemicellulose or to remove lignin. Physical pretreatment can be used to increase the accessible surface area of lignocellulosic materials prior to the subsequent hydrolysis. Chemical hydrolysis by means of dilute acid pretreatment is an effective method to hydrolyze both hemicellulose and cellulose in lignocellulosic materials. Though, this method can slightly degrade lignin. Lignin can be effectively removed by alkaline pretreatment. Biological pretreatment by microorganisms and enzymes can be used to pretreat the lignocellulosic materials before enzymatic hydrolysis. Combined physical, chemical, and biological pretreatment process is more effective than a sole process. Bio-hydrogen and methane production process is greatly affected by the environmental factors. The ranges of these factors mainly depend on, but not limited to, the types of feedstocks and microorganisms in the process. Dark fermentation followed by photo-fermentation is more efficient in producing hydrogen from lignocellulosic materials than the single-stage fermentation. Methane production from lignocellulosic materials is an environmentally friendly process for producing bioenergy and managing the waste at the same time.

Bio-hydrogen and Methane Production from Lignocellulosic Materials DOI: http://dx.doi.org/10.5772/intechopen.85138