**5. Conclusion**

Black Americans make use of a variety of coping strategies to respond to racism. These usually include problem-focused coping strategies such as social support and emotion-focused coping strategies like religion/spirituality. Gender differences exist in this response as Black American women tend to prioritize social support as well as religion/spirituality to cope. Also, similarities can be found between how Black Americans cope with physical and emotional stress. As presented in this paper, people typically rely on active strategies when coping with racism, and active strategies mitigate the loss of agency which is a hallmark of racism. Approaches to racism should deemphasize avoidance to synergize with successful coping strategies already being used by the Black community.

### **Funding**

This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program, Canadian Institutes of Health Research (CIHR) Grant Number 950-232127 (PI M. Williams).

### **Conflicts of interest**

The authors report no conflicts of interest.

## **Publication**

Effective Elimination of Structural Racism, IntechOpen E-Book, Erick Guerrero (Ed). (ISBN 978-1-83969-283-3).

*Gender Differences in Coping with Racism: African American Experience and Empowerment DOI: http://dx.doi.org/10.5772/intechopen.99930*


**2. Spectral responsivity scale in the visible range based on single silicon photodiodes.**

A spectral responsivity scale means that the responsivity is known at every wavelength within the response range of interest and it would be desirable to know it for all the other parameters associated with a beam: angle of incidence, divergence or polarization.

Aspectral responsivity scale in the visible range can be created by calibrating a silicon trap detector at several laser wavelengths against ahigh accuracy primary standard such as an electrically calibrated cryogenic radiometer. This method provides a very certain value for the responsivity at specific wavelengths as those of lasers (for instance 406.7 nm, 441.3 nm, 488.0 nm, 514.5 nm, 568.2 nm, 647.1 nm and 676.4 nm). From there single elements detectors, most suitable for some applications, can be calibrated against that trap detector at those wavelengths to define the working scale.

The spectral responsivity of silicon photodiodes is given by the well-known equation

$$R(\lambda) = (1 - \rho(\lambda))\varepsilon(\lambda)\frac{\lambda}{k} \tag{2}$$

This chapter describes the results obtained for the responsivity of the photodiodes by using a model to calculate the diode's reflectance from experimental measurements and a model for the internal quantum efficiency, which is also fitted to experimental values. Based on the mod‐ els, the fitting errors and the uncertainty of reflectance and responsivity measurements, the uncertainty of the responsivity scale is calculated according to the ISO recommendations.

#### **3. Reflectance evaluation of silicon photodiodes**

From the reflectance point of view, a silicon photodiode can be considered as a system formed by a flat transparent film over an absorbing medium. The flat film is the silicon ox‐ ide and the absorbing medium is the silicon substrate. The reflectance of such a system is given by [5]

$$\rho = \frac{\left[r\_{12}^2 + \rho\_{23}^2 + 2r\_{12}\rho\_{23}\cos(\phi\_{23} + 2\beta)\right]}{1 + r\_{12}^2\rho\_{23}^2 + 2r\_{12}\rho\_{23}\cos(\phi\_{23} + 2\beta)}\tag{3}$$

trap detector, the photodiodes' internal quantum efficiency can be calculated according to (2). Using a model based in physical laws rather than experimental equations allows obtain‐ ing the physical quantity for different circumstances, such as different angles of incidence,

Photodiodes as Optical Radiation Measurement Standards

http://dx.doi.org/10.5772/51462

177

**Figure 1.** Spectral reflectance values of photodiode CIRI and fitted values according to equation (3).

To spectrally know the internal quantum efficiency we have used the model developed by Gentile *et al* [2], based on that from Geist and Baltes [14] and improved by Werner *et al* [15].

where *Pf* is the collection efficiency at the front, *T* is the junction depth, *Pb* is the collection efficiency at the silicon bulk region, which starts at depth *D*, *h* is the photodiode's length, *R*back is the reflectance at the photodiode's back surface and *α* is the absorption coeffi‐ cient.According to Gentile *et al* [2] a simplified model can be used if the model is to be ap‐ plied to wavelengths shorter than 920 nm. This model is obtained from the previous equation by deleting the last two terms. Then, the quantum efficiency can be obtained from

(*D* − *T* )*α*(*λ*) {exp −*Tα*(*λ*) −exp *Dα*(*λ*) } −

(4)

**5. Internal quantum efficiency of silicon photodiodes**

*α*(*λ*)*T* {1−exp −*Tα*(*λ*) } − 1 − *Pb*

The internal quantum efficiency is given by

*Pb*expexp *hα*(*λ*) + *Rback*exp *hα*(*λ*) *Pb*

1 − *Pf*

*ε*(*λ*)=*Pf* +

for instance.

where *r*12 is the amplitude of the reflection coefficient from air to silicon oxide, *ρ*23 is the amplitude of the reflection coefficient from silicon oxide to silicon, *φ*23 is the phase change at the interface silicon oxide–silicon and *β* = 2*πn*2*h* cos*(θ*2*)/λ*0, with *h* the thickness of SiO2, *n*2 the refractive index of SiO2 and *θ*2 the refraction angle at the air–oxide interface. These variables change with the angle of incidence and the light polarization, so the reflectance value will be known if the silicon oxide thickness, the angle of incidence, the refractive index and the light polarization status are known. This reflectance model has been already tested for another type of silicon photodiode from the same manufacturer [6].

Spectral values of the refractive index are available in the literature. In this work values have been obtained from those given in [7]. The index of refraction of silicon oxide has been inter‐ polated by fitting a polynomial to data; the real part of the refractive index of silicon has been obtained by fitting a polynomial in 1*/λ* and the imaginary part by fitting an exponen‐ tial decay in *λ*.Reflectance was measured with an angle of incidence of 4 in our reference spectrophotometer, using p-polarized light, at the laser wavelengths for which the diodes were calibrated against the trap: 406.7 nm, 441.3 nm, 488.0 nm, 514.5 nm, 568.2 nm, 647.1 nm and 676.4 nm. By fitting equation 3 to measurement results, the silicon oxide thickness was obtained for every photodiode, as shown in table 1. The fitting error in this table is the pa‐ rameter given by the fitting software.

**Table 1.** Silicon oxide thickness fitted to reflectance measurements

The fitting is very good for wavelengths longer than 500 nm, getting worse for shorter wavelengths, as can be seen in figure 1 for one of the photodiodes studied. The same results are obtained for the three photodiodes studied in this work.

This agrees also with [2]. Probably it is due to the measurement bandwidth. For conven‐ ience, the reflectance was measured in our reference spectrophotometer with a bandwidth of 5 nm in order to have a good signal-to-noise ratio at the shortest wavelengths. But in this region the first and second derivatives of reflectance are higher than in the middle visible, so the increased bandwidth produces an effective reflectance value that differs significantly from the spectral value. For this reason, reflectance values below 500 nm were not used in the final fitting process to obtain the thickness.

Using thickness values given in table 1, the reflectance of the photodiodes at normal inci‐ dence can be calculated, and from them and the responsivity values measured against the trap detector, the photodiodes' internal quantum efficiency can be calculated according to (2). Using a model based in physical laws rather than experimental equations allows obtain‐ ing the physical quantity for different circumstances, such as different angles of incidence, for instance.

*ρ* = *r*12 2 + *ρ*23

1 + *r*12 2*ρ*23

for another type of silicon photodiode from the same manufacturer [6].

rameter given by the fitting software.

176 Photodiodes - From Fundamentals to Applications

**Table 1.** Silicon oxide thickness fitted to reflectance measurements

the final fitting process to obtain the thickness.

are obtained for the three photodiodes studied in this work.

2 + 2*r*12*ρ*23cos(*ϕ*23 + 2*β*)

where *r*12 is the amplitude of the reflection coefficient from air to silicon oxide, *ρ*23 is the amplitude of the reflection coefficient from silicon oxide to silicon, *φ*23 is the phase change at the interface silicon oxide–silicon and *β* = 2*πn*2*h* cos*(θ*2*)/λ*0, with *h* the thickness of SiO2, *n*2 the refractive index of SiO2 and *θ*2 the refraction angle at the air–oxide interface. These variables change with the angle of incidence and the light polarization, so the reflectance value will be known if the silicon oxide thickness, the angle of incidence, the refractive index and the light polarization status are known. This reflectance model has been already tested

Spectral values of the refractive index are available in the literature. In this work values have been obtained from those given in [7]. The index of refraction of silicon oxide has been inter‐ polated by fitting a polynomial to data; the real part of the refractive index of silicon has been obtained by fitting a polynomial in 1*/λ* and the imaginary part by fitting an exponen‐ tial decay in *λ*.Reflectance was measured with an angle of incidence of 4 in our reference spectrophotometer, using p-polarized light, at the laser wavelengths for which the diodes were calibrated against the trap: 406.7 nm, 441.3 nm, 488.0 nm, 514.5 nm, 568.2 nm, 647.1 nm and 676.4 nm. By fitting equation 3 to measurement results, the silicon oxide thickness was obtained for every photodiode, as shown in table 1. The fitting error in this table is the pa‐

> **Photodiode SiO2 thickness/nm Fitting error/nm** CIRI 29.58 0.19 SiN 28.84 0.17 Si1 29.93 0.19

The fitting is very good for wavelengths longer than 500 nm, getting worse for shorter wavelengths, as can be seen in figure 1 for one of the photodiodes studied. The same results

This agrees also with [2]. Probably it is due to the measurement bandwidth. For conven‐ ience, the reflectance was measured in our reference spectrophotometer with a bandwidth of 5 nm in order to have a good signal-to-noise ratio at the shortest wavelengths. But in this region the first and second derivatives of reflectance are higher than in the middle visible, so the increased bandwidth produces an effective reflectance value that differs significantly from the spectral value. For this reason, reflectance values below 500 nm were not used in

Using thickness values given in table 1, the reflectance of the photodiodes at normal inci‐ dence can be calculated, and from them and the responsivity values measured against the

2 + 2*r*12*ρ*23cos(*ϕ*23 + 2*β*) (3)

**Figure 1.** Spectral reflectance values of photodiode CIRI and fitted values according to equation (3).

#### **5. Internal quantum efficiency of silicon photodiodes**

To spectrally know the internal quantum efficiency we have used the model developed by Gentile *et al* [2], based on that from Geist and Baltes [14] and improved by Werner *et al* [15]. The internal quantum efficiency is given by

$$\begin{aligned} \varepsilon(\lambda) &= P\_f + \frac{1 - P\_f}{\alpha(\lambda)\Gamma} [1 - \exp[-T\alpha(\lambda)]] - \frac{1 - P\_b}{(D - T)\alpha(\lambda)} [\exp[-T\alpha(\lambda)] - \exp[D\alpha(\lambda)]] - \\ &- P\_b \text{expexp}\{ha(\lambda)\} + R\_{\text{back}} \exp[ha(\lambda)] P\_b \end{aligned} \tag{4}$$

where *Pf* is the collection efficiency at the front, *T* is the junction depth, *Pb* is the collection efficiency at the silicon bulk region, which starts at depth *D*, *h* is the photodiode's length, *R*back is the reflectance at the photodiode's back surface and *α* is the absorption coeffi‐ cient.According to Gentile *et al* [2] a simplified model can be used if the model is to be ap‐ plied to wavelengths shorter than 920 nm. This model is obtained from the previous equation by deleting the last two terms. Then, the quantum efficiency can be obtained from

$$\mathcal{L}\left(\boldsymbol{\lambda}\right) = \text{Pf} + (\mathbf{1} - \mathbf{P}\boldsymbol{\lambda}) / \mathcal{a}\left(\boldsymbol{\lambda}\right) \text{T} \left\{ \mathbf{1} - \exp\left[ -\mathbf{T}\boldsymbol{a}\left(\boldsymbol{\lambda}\right) \right] \right\} - (\mathbf{1} - \mathbf{P}\boldsymbol{b}) / \left( (\mathbf{D} - \mathbf{T})\boldsymbol{a}\left(\boldsymbol{\lambda}\right) \right) \left\{ \exp\left[ -\mathbf{T}\boldsymbol{a}\left(\boldsymbol{\lambda}\right) \right] - \exp\left[ \mathbf{D}\boldsymbol{a}\left(\boldsymbol{\lambda}\right) \right] \right\} \tag{5}$$

the calculated values and those measured against the trap is excellent as can be seen in fig‐ ure 3 for one of the photodiodes studied. This result is just a check of the consistency of the method. Nevertheless, it can be seen that most calculated values are smaller than the meas‐ ured ones. This might be due to the independent fitting of reflectance and quantum efficien‐ cy values and their functional forms, but it may also be due to the presence of a systematic error in the measurements. Some research will have to be done in the future to clarify this.

Photodiodes as Optical Radiation Measurement Standards

http://dx.doi.org/10.5772/51462

179

**Figure 3.** Spectral Difference between calculated and measured spectral responsivity values for photodiode CIRI as a

**7. Spectral responsivity scale in the near IR range based on single InP/**

As in the visible range, semiconductor photodiodes are the best choice for establishing spec‐ tral responsivity scales in the near IR range. The first attempt was to use germanium photo‐ diodes, since its gap allowed to obtain a device responding to wavelengths lower than 1.6 μm, approximately, depending on temperature. However germanium photodiodes have got a rather high dark current and lower shunt resistance than silicon, then they are not so use‐ ful for optical radiation detection. Since optical communications were demanding better de‐ tectors to enlarge their use, other photodiodes were developed in this spectral region of great interest. Since no other single element semiconductor was possible, semiconductor hetero-junctions were developed. A hetero-junction is a junction formed between two semi‐ conductors with different band-gaps. Of course building such devices is not straightforward since the lattice parameters have to be matched, but this is not the subject of this chapter and

function of wavelength.

**InGaAs photodiodes**

many good references may be found in literature [17].

This model has been fitted to the calculated internal quantum efficiency values by a non-lin‐ ear squared method.

The parameters' initial values were taken from Gentile *et al* [2]. The goodness of the fit can be seen in figure 2, where values for one of the studied photodiodes are shown. The same results are obtained for the three photodiodes studied in this work. The main difference be‐ tween the fitted values of the internal quantum efficiency and those calculated from the re‐ sponsivity and reflectance measurements is about 10-3, which agrees well with results given by other authors, e.g.[2,9].

**Figure 2.** Experimental internal quantum efficiency values ofphotodiode SiN and fitted values according to equation (5) againstthe absorption coefficient.

Another point that can be discussed is how far the internal quantum efficiency can be ex‐ trapolated. Using this simplified model and fitting with values corresponding to wave‐ lengths shorter than 700 nm, quantum efficiency values continue to increase very slightly to 900 nm at least. This is not what really happens in the photodiode, so there will be an upper limit for the extrapolation. This limit will depend on the uncertainty allowable to the re‐ sponsivity value and will be discussed in the following section.

#### **6. Spectral responsivity values of silicon photodiodes**

Responsivity of detectors has been calculated with the model described previously and the parameters obtained by the fitting process by using (2), (3) and (5). The agreement between the calculated values and those measured against the trap is excellent as can be seen in fig‐ ure 3 for one of the photodiodes studied. This result is just a check of the consistency of the method. Nevertheless, it can be seen that most calculated values are smaller than the meas‐ ured ones. This might be due to the independent fitting of reflectance and quantum efficien‐ cy values and their functional forms, but it may also be due to the presence of a systematic error in the measurements. Some research will have to be done in the future to clarify this.

e l

ear squared method.

by other authors, e.g.[2,9].

(5) againstthe absorption coefficient.

a l

178 Photodiodes - From Fundamentals to Applications

( ) = +- Pf 1 Pf / T 1 exp T 1 Pb / D T exp T exp D ( )

( ) } ( ) (( )

This model has been fitted to the calculated internal quantum efficiency values by a non-lin‐

The parameters' initial values were taken from Gentile *et al* [2]. The goodness of the fit can be seen in figure 2, where values for one of the studied photodiodes are shown. The same results are obtained for the three photodiodes studied in this work. The main difference be‐ tween the fitted values of the internal quantum efficiency and those calculated from the re‐ sponsivity and reflectance measurements is about 10-3, which agrees well with results given

**Figure 2.** Experimental internal quantum efficiency values ofphotodiode SiN and fitted values according to equation

Another point that can be discussed is how far the internal quantum efficiency can be ex‐ trapolated. Using this simplified model and fitting with values corresponding to wave‐ lengths shorter than 700 nm, quantum efficiency values continue to increase very slightly to 900 nm at least. This is not what really happens in the photodiode, so there will be an upper limit for the extrapolation. This limit will depend on the uncertainty allowable to the re‐

Responsivity of detectors has been calculated with the model described previously and the parameters obtained by the fitting process by using (2), (3) and (5). The agreement between

sponsivity value and will be discussed in the following section.

**6. Spectral responsivity values of silicon photodiodes**

al

 al

( ) ) { é ùé ù

 al

ë ûë û - - ( ) ( ) } (5)

( ) { - - -- - é ù ë û a l

**Figure 3.** Spectral Difference between calculated and measured spectral responsivity values for photodiode CIRI as a function of wavelength.


**2. Genetic counseling in chromosomal congenital anomalies**

Carrier and aneuploidy screening and diagnostic testing have expanded intensely over the past two decades [2], which is justifiable given the estimate of 5.3% of the neonates affected by a genetic disorder. Despite ultrasound and biochemistry reasons for recommending a prenatal diagnosis, genetic testing in pregnancy is optional. Decisions about undergoing testing should be expressed, consented, and based on individual patient's values and needs and guided by the geneticist during counseling sessions.

Other chromosomal anomalies are structural, such as deletions, duplications, inversions, insertions, translocations, etc. (**Figures 2** and **3**). They are rare and require different diagnosis

Prenatal Genetic Counseling in Congenital Anomalies http://dx.doi.org/10.5772/intechopen.74394 445

Possible alternatives for screening ("Prenatal Biochemical and Ultrasound Markers in Chromosomal Anomalies") and diagnosis ("Genomic Testing for Prenatal Clinical Evaluation of Congenital Anomalies") are presented in detail in different chapters, due to their marked

In the current section, we aim to cover several genetic counseling concepts in a few hypothetical situations of congenital anomaly with underlying chromosomal cause. Pre- and posttesting counseling are a prerequisite of all genetic counseling, but the genetic consult comprises also of a detailed assessment of medical history, psychosocial assessment, and family history,

The possible mechanism by which the chromosomal anomalies occur is usually due to errors in the cell division cycles: nondisjunction in the maternal meiotic division I, and, less fre-

The etiology is mostly unclear, but the probability of chromosomal anomalies increases with maternal age [4], and this is one of the most common etiological factors. Predisposition to

strategies, counseling, and management of the case.

importance in genetic testing and counseling.

**Figure 1.** Karyotype 45,X—monosomy X.

which we are not focusing on here [3, 6, 7].

quently, paternal origin [8] or meiosis II [9].

Congenital anomalies can be caused by chromosomal, monogenic, and multifactorial disorders [4]; out of which, chromosomal anomalies have a significant impact given their combined frequency of 1 in 153 pregnancies [5] and the reserved prognosis for many of them.

Aneuploidies are the most frequent chromosomal anomalies. Aneuploidies are numerical disorders (**Figure 1**)—the number of chromosomes differs to the normal state, called euploidy. Any of the chromosomes, autosomes, or heterosomes can be affected. Aneuploidies can be complete, involving the whole chromosome, or partial. From a single fertilized egg, more populations of cells of different genotypes can develop—this abnormal situation is called mosaicism. Due to their high incidence, three complete trisomies bear significance for the prenatal diagnosis: trisomy 21 (T21—Down syndrome), 18 (T18—Edwards syndrome), and 13 (T13—Patau syndrome).

**Figure 1.** Karyotype 45,X—monosomy X.

also suggest other possible confirmatory or complementary tests or alternatives [2] and unconditionally support the patient's options, respecting the autonomy of his/her choice.

One of the most encumbering tasks of genetic counseling is presenting a family with the fact that their child has a genetic condition or birth defect. Most of the test results face the couple

As part of the informed decision-making process, the couple must be informed in detail on the clinical presentation and prognosis of the disorder identified. This is often problematic in chromosomal disorders: (1) genotypic variability—the phenotype will vary depending on the extent of the genetic defect and (2) phenotypic variability—the evolution of a case can vary

These questions are perhaps among the most frequent during the counseling session and we will try to answer them briefly below. The probability reoccurrence is called "recurrence risk." Recurrence risk assessment and counseling is based on a combination of theoretic risk assessment and empiric data. Families and patients should be informed on the assumptions

This chapter is focused on genetic counseling in congenital anomalies, caused by chromosomal, monogenic, or plurifactorial anomalies, as well as on preimplantation genetic diagnosis.

Carrier and aneuploidy screening and diagnostic testing have expanded intensely over the past two decades [2], which is justifiable given the estimate of 5.3% of the neonates affected by a genetic disorder. Despite ultrasound and biochemistry reasons for recommending a prenatal diagnosis, genetic testing in pregnancy is optional. Decisions about undergoing testing should be expressed, consented, and based on individual patient's values and needs and

Congenital anomalies can be caused by chromosomal, monogenic, and multifactorial disorders [4]; out of which, chromosomal anomalies have a significant impact given their combined frequency of 1 in 153 pregnancies [5] and the reserved prognosis for many of them.

Aneuploidies are the most frequent chromosomal anomalies. Aneuploidies are numerical disorders (**Figure 1**)—the number of chromosomes differs to the normal state, called euploidy. Any of the chromosomes, autosomes, or heterosomes can be affected. Aneuploidies can be complete, involving the whole chromosome, or partial. From a single fertilized egg, more populations of cells of different genotypes can develop—this abnormal situation is called mosaicism. Due to their high incidence, three complete trisomies bear significance for the prenatal diagnosis: trisomy 21 (T21—Down syndrome), 18 (T18—Edwards syndrome), and

with a termination/no-termination decision.

444 Congenital Anomalies - From the Embryo to the Neonate

involved and the limitations of such estimates.

guided by the geneticist during counseling sessions.

13 (T13—Patau syndrome).

greatly, even between carriers of the same type of anomaly.

**1.2. "Why did it happen? Will it happen again? What can be done?"**

**2. Genetic counseling in chromosomal congenital anomalies**

Other chromosomal anomalies are structural, such as deletions, duplications, inversions, insertions, translocations, etc. (**Figures 2** and **3**). They are rare and require different diagnosis strategies, counseling, and management of the case.

Possible alternatives for screening ("Prenatal Biochemical and Ultrasound Markers in Chromosomal Anomalies") and diagnosis ("Genomic Testing for Prenatal Clinical Evaluation of Congenital Anomalies") are presented in detail in different chapters, due to their marked importance in genetic testing and counseling.

In the current section, we aim to cover several genetic counseling concepts in a few hypothetical situations of congenital anomaly with underlying chromosomal cause. Pre- and posttesting counseling are a prerequisite of all genetic counseling, but the genetic consult comprises also of a detailed assessment of medical history, psychosocial assessment, and family history, which we are not focusing on here [3, 6, 7].

The possible mechanism by which the chromosomal anomalies occur is usually due to errors in the cell division cycles: nondisjunction in the maternal meiotic division I, and, less frequently, paternal origin [8] or meiosis II [9].

The etiology is mostly unclear, but the probability of chromosomal anomalies increases with maternal age [4], and this is one of the most common etiological factors. Predisposition to

oocyte aneuploidy is also seen in young women, gene expression alteration due to environmental factors and the influence of follicle-stimulating hormone (FSH) being possible culprits [10]. It is yet uncertain if the paternal age contributes to the risk of aneuploidy, if at all [11]. The contribution of different occupational or environmental factors is insufficiently

Prenatal Genetic Counseling in Congenital Anomalies http://dx.doi.org/10.5772/intechopen.74394 447

Aneuploidies are most frequent causes of mental retardation and pregnancy loss [9]. It comes as no surprise that the chance of reoccurrence is one of the most relevant aspects of genetic

(a) Complete chromosomal, especially autosomal trisomies, when parents are not carriers of

Recurrence risk in the absence of parent translocations follows the empirical risk—the risk measured in the general population, generally evaluated around 1% for the most common trisomy [12] and increases with age for trisomy 21. For other trisomies, the recurrence risk seems lower. Recurrence rates are rather difficult to estimate in sexual aneuploidies. Subjects with Down syndrome are generally infertile, but they have a significant risk of aneuploidy

(b) Chromosomal trisomies with one of the parents being a carrier of a chromosome 21

Down syndrome translocations are present in less than 4% of the cases. Translocations can occur de novo. For transmitted translocations, the recurrence risk depended on the affected parent: for instance, depending on the involved translocated chromosomes, if the mother is the balanced carrier, the risk is to that of the father, without any known reason for the discrep-

Generally, mosaicism cases have the lowest frequency contributions to the total of the trisomies. Mosaicism can occur de novo in the offspring, but parental germ line mosaicism contributes to the recurrence risk [15]. Reduced mosaic [16], meaning low percentage of modified cell lines, or partial trisomy [17], equivalent with duplication, generally has a better prognosis

The couple must be informed that there is no prophylaxis or treatment to correct the aneuploidy, but genetic counseling can provide the support for medically informed decisions to

If the couple wishes to keep a pregnancy with chromosomal disorder, they must be informed on the obstetrical complications that may arise, the life expectancy, and the natural history of

A trisomy prenatal case, especially 13 or 18, may present with different obstetrical challenges: miscarriage and stillbirth are more frequent than compared to the general population. Structural anomalies of the fetuses lead to a negative prognosis after birth and low life

by comparison with a homogenous complete trisomy, but this is not a rule [18].

ancy. A balanced translocation t(21,21) has 100% recurrence risk [14].

documented.

counseling.

translocations

translocation

(c) Mosaicism

expectancy [19].

recurrence in their offspring [13].

guide the management of the case.

the disease neonatally and into adulthood.

**Figure 2.** Karyotype 45,XX,rob(13;22)(q10;q10)—Robertsonian translocation.

**Figure 3.** Karyotype 46,XY,t(1;15)(p36.3;q26.1)—reciprocal translocation.

oocyte aneuploidy is also seen in young women, gene expression alteration due to environmental factors and the influence of follicle-stimulating hormone (FSH) being possible culprits [10]. It is yet uncertain if the paternal age contributes to the risk of aneuploidy, if at all [11]. The contribution of different occupational or environmental factors is insufficiently documented.

Aneuploidies are most frequent causes of mental retardation and pregnancy loss [9]. It comes as no surprise that the chance of reoccurrence is one of the most relevant aspects of genetic counseling.

(a) Complete chromosomal, especially autosomal trisomies, when parents are not carriers of translocations

Recurrence risk in the absence of parent translocations follows the empirical risk—the risk measured in the general population, generally evaluated around 1% for the most common trisomy [12] and increases with age for trisomy 21. For other trisomies, the recurrence risk seems lower. Recurrence rates are rather difficult to estimate in sexual aneuploidies. Subjects with Down syndrome are generally infertile, but they have a significant risk of aneuploidy recurrence in their offspring [13].

(b) Chromosomal trisomies with one of the parents being a carrier of a chromosome 21 translocation

Down syndrome translocations are present in less than 4% of the cases. Translocations can occur de novo. For transmitted translocations, the recurrence risk depended on the affected parent: for instance, depending on the involved translocated chromosomes, if the mother is the balanced carrier, the risk is to that of the father, without any known reason for the discrepancy. A balanced translocation t(21,21) has 100% recurrence risk [14].


*2.2. Microbiota Analysis*

Fecal samples were shipped frozen to the George Washington University Genomics Core for processing. Each sample underwent DNA and RNA extraction in parallel from 250 mg of fecal material using a ZymoBIOMICS DNA/RNA Miniprep Kit (Zymo Research Corporation, Irvine, CA, USA). The resulting DNA was quality controlled using a Thermo Fisher Qubit 3.0 High Sensitivity DNA kit (Life Technologies, Carlsbad, CA, USA) and standardized to 0.2 ng/μL for library construction. Sequencing libraries were prepared, along with a ZymoBIOMICS Microbial Community DNA standard, using a Nextera XT DNA Library Prep kit (Illumina, San Diego, CA, USA) following Illumina's recommended guidelines. Libraries were quality controlled using a Thermo Fisher Qubit 3.0 High Sensitivity DNA kit and an Agilent 2100 Bioanalyzer High Sensitivity DNA kit (Agilent, Santa Clara, CA, USA) and were subsequently sequenced as paired-end, 2 × 150 bp, using a NextSeq 500 Mid-Output kit (Illumina, San Diego, CA, USA), with a 1% phi X sequencing control spike-in.


**1. Introduction**

66 Selected Topics on Optical Amplifiers in Present Scenario

Vyrsokinos K., Stampoulidis L. , Gomez-Agis F. , Voigt F. K., Zimmermann L., Wahlbrink

Yu S., Gu W.(2005), 'A tunable wavelength conversion and wavelength add/drop scheme

Zaghloul Y.A., Zaghloul A.R.M.(2006), 'Unforced polarization based optical implementation

Zaghloul Y. A., Zaghloul A. R. M. and Adibi A. (2011), 'Passive all-optical polarization

Zhang S., Li Z., Liu Y., Khoe G. D. and Dorren H. J. S.(2005) 'Optical shift register based on

of Binary logic', Optic Express, Vol.14, No.16, 2006, pp.7252-7269.

doi:10.1109/PHOTWTM.2010.5421936

Vol.13,No.24,2005,pp.9708-9713

20346

J. Quantum Electron, Vol.41. No.7, 2005, pp. 1007-1012

T., Sheng Z. , Thourhout D.V. , Dorren H.J.S.,(2010) 'Ultra-high Speed, all-optical wavelength converters using single SOA and SOI photonic integrated circuits', Photonics Society Winter Topicals Meeting Series (WTM), IEEE,2010,pp.113–114,

based on cascaded second order nonlinearity with double pass configuration', IEEE

switch, binary logic gates, and digital processor', Vol.19, No.21, 2011, pp.20332-

an optical flip-flop memory with a single active element', Optics Express ,

The service evolution and the rapid increase in traffic levels fuel the interest toward switching paradigms enabling the fast allocation of Wavelength Division Multiplexing WDM channels in an on demand fashion with fine granularities (microsecond scales). For this reason, in the last years, different optical switching paradigms have been proposed (Sabella et al., 2000): optical-packet switching (OPS), optical-burst switching (OBS), wavelength-routed OBS, etc. Among the various all-optical switching paradigms, OPS attracts increasing attention. Owing to the high switching rate, Semiconductor Optical Amplifier (SOA) is a key technology to realize Optical Packet Switches. We propose some Optical Packet Switch (OPS) architectures and illustrate their realization in SOA technology. The effectiveness of the technology in reducing the power consumption is also analyzed. The chapter is organized in three sections. The main blocks (Switching Fabric, Wavelength Conversion stage, Synchronization stage) of an OPS are illustrated in Section 2 where we also show some examples of realizing wavelength converters and synchronizers in SOA technology. Section 3 introduces SOA-based single-stage and multi-stage switching fabrics. Finally the SOA-based OPS power consumption is investigated in Section 4.

### **2. Optical packet switching architectures**

The considered optical switch architecture (Eramo, 2000; 2006; Sabella et al., 2000) is shown in Fig. 1. It has *N* input and output fibers, each fiber supports a WDM signal with *M* wavelengths, so an input (or output) channel is characterized by the couple (*i*, *λj*) wherein *i* (*i* ∈ 1, ··· , *N*) identifies the input/output fiber and *λj*, (*j* ∈ 1, ......, *M*) identifies the wavelength. In general, optical packet switches can be divided into two categories: slotted (synchronous) and unslotted (asynchronous) networks. In a synchronous switch (Eramo, 2000), as illustrated in Fig. 1 packets with fixed length are aligned (synchronized) by synchronizers before they enter the switch fabric. This type of switch generally achieves a fairly good throughput since the behavior of the packets is regulated. However, complex and expensive synchronization hardware is needed at each node. On the other hand, in an asynchronous switch (Eramo et al., 2003), the packets are not aligned and they are switched one by one on the fly. Asynchronous networks generally have lower cost, better flexibility, and robustness, but usually they have lower overall throughput than synchronous networks. The switch architecture is equipped with a number *r* of WCs which are shared according

are composed of a series of optical switches designed to select the proper optical path

SOA-Based Optical Packet Switching Architectures 69

In these architectures, however, increasing the number of switches to improve the time resolution causes additional increases in optical loss and crosstalk. To overcome loss problems SOA-based synchronizers have been proposed. Next we illustrate and explain two of them. In the first one (Sakamoto et al., 2002) synchronization is achieved by selecting one of some optical paths, each with a different length, using wavelength and space switching based on a wavelength-tunable distributed Bragg reflector laser diode (LD) and *n* semiconductor optical amplifier (SOA) gates per channel. The synchronizer has its own internal reference clock. The clock period equals the time slot duration (*Ts*) and the synchronizer aligns input packets with the time slot packet by packet. Synchronization is achieved by counting each delay of each input packet with respect to the reference time and choosing the optical paths with the appropriate length. Fig. 2 shows the schematic structure of the synchronizer. Each channel is equipped with a wavelength-divisionmultiplexing (WDM) coupler, a wavelength converter, an optical splitter, semiconductor optical amplifier (SOA) gates, two stages of fiber delay lines, an optical coupler, arrayed waveguide gratings (AWGs) for MUX/DEMUX, a delay counter, and a wavelength-tunable laser. The out-of-band optical label switching technique is used, in which optical packet and optical labels are carried on different wavelengths (Okada et al., 2001). The delay counter estimates the delay of each optical label and selects one of *m* wavelengths of the tunable laser and one of *n* SOA gates. The wavelength of each optical packet signal is converted to the laser wavelength by the wavelength converter. The wavelength-converted optical packet signal passes through one of *n* SOA gates and the

2*k* (*Ts*:time slot; *k*:integer).

*n* . The packet signal

MUX AWG

O*1*

DEMUX AWG

> O*2*

O*m*

2nd Stage Delay lines (OFCs)

and pairs of fiber delay lines with different optical lengths of *Ts*

first-stage delay lines, each of which has a different delay time of *Ts*

**control signals**

Tunable LD

> **S P L I T T E R**

time difference of *Ts*

WDM

OFC: Optical fiber circuit.

coupler EDFA WC

synchronization is attained.

Delay O/E Counter

then passes through one of *m* AWG ports and the second-stage delay lines, each with delay

**SOA #n**

1st Stage Delay lines (OFCs)

Fig. 2. Schematic structure of the synchronizer. WC: Wavelength converter. SOAG: SOA gate.

The second synchronizer has been proposed in (Mack et al., 2008) and it is illustrated in Fig. 3. Feed-forward structure with SOA-based gates is used here because of its high operation speed, large tuning range, and the potential for integration within the large SOA-based switch

**SOA #1**

**SOA #2**

*n*×*m* . Consequently, there are optical paths with different lengths and

**C O U P L E R**

EDFA

to a particular strategy (Eramo et al., 2009b). At each input line, a small portion of the optical power is tapped to the electronic controller. The switch control unit detects and reads packet headers and drives the space switch matrix and the WCs. Incoming packets on each input line are wavelength demultiplexed (DEMUXs blocks in Fig. 1). An electronic control logic, on the basis of the routing information contained in each packet header, handles packet contentions and decides which packets have to be wavelength shifted. Packets not requiring wavelength conversion are directly routed towards the output lines; on the contrary, packets requiring wavelength conversions will be directed to the pool of *r* WCs and, after a proper wavelength conversion, they will reach the output line. An example of realization of synchronizers and wavelength converters in SOA technology is shown in Sections 2.1 and 2.2 respectively. Section 3 is devoted to illustrate both SOA-based single-stage and multi-stage switching fabrics.

Fig. 1. Optical Packet Switching Architecture with *N* Input/Output Fibers, *M* Wavelength and *r* shared Wavelength Converters.


*3.2. Extrusion and Fermentation*

Extrusion is a type of high-temperature treatment, and as discussed previously, it can decrease the number of mycotoxins in cereals [68]. Contradictory results were indicated by Zokaityte et al. They found that extrusion may affect the mycotoxin levels differently by increasing, decreasing, or not changing their concentrations in the samples. The combination of extrusion at different temperatures (115 and 130 ◦C), over different screw speeds (16, 20, and 25 rpm), with fermentation for 24 h at 30 ◦C by using 2 strains of LAB (*Lactobacillus casei* and *Lactobacillus paracasei*) and their effects have also been studied. This combination increased the amount of lactic acid and decreased bacterial contamination as a result of pH reduction. The effect of extrusion on different mycotoxins contradicted the results obtained in other studies. The 15-DON concentration increased in all extruded samples, and the fermentation of the samples decreased them to acceptable levels. The capacity of fermentation to decrease mycotoxin levels in the food or feed samples may be caused by the binding capacity of LAB. Mycotoxin types and their initial concentrations in the food matrix, the physicochemical characteristics of this matrix, and the fermentation variables, such as temperature and duration, play a major role in determining the binding percentages [124].


**Abstract**

Spinal cord injury (SCI) is a harmful event that involves several repercussions on sensory and motor function that affects the quality of life (QoL) of patients. After SCI, many damage mechanisms are activated that impact on both autonomous extrinsic and intrinsic innervation toward the gut, and these changes modify the gut motility causing bowel dysfunction (BD), an entity that affects 40% of patients with SCI, being the second comorbidity after loss of mobility with no recognized cure. The severity of complications is ruled by the level and severity of injury, having a worse prognosis with an injury that is the most proximal to the brain. In the last 5 years, some experiments have tried to elucidate the consequences of dysbiosis in the gut and aggregated proinflammatory processes. The goal of this chapter is to establish the importance of bacterial composition and immune system repercussions in bowel dysfunction after SCI and how could it give rise to new therapies.

**Keywords:** neurogenic bowel dysfunction, autonomic dysreflexia, gut microbiota, spinal cord injury