*Edited by Ali Demir Sezer*

This contribution book collects reviews and original articles from eminent experts working in the interdisciplinary arena of novel drug delivery systems and their uses. From their direct and recent experience, the readers can achieve a wide vision on the new and ongoing potentialities of different smart drug delivery systems. Since the advent of analytical techniques and capabilities to measure particle sizes in nanometer ranges, there has been tremendous interest in the use of nanoparticles for more efficient methods of drug delivery. On the other hand, this reference discusses advances in the design, optimization, and adaptation of gene delivery systems for the treatment of cancer, cardiovascular, diabetic, genetic, and infectious diseases, and considers assessment and review procedures involved in the development of genebased pharmaceuticals.

ISBN 978-953-51-2247-0

Smart Drug Delivery System

Photo by agsandrew / DollarPhoto


*3.2. Back Analysis of Constitutive Relationship Equation through Centrifugal Experiment*

The centrifugal test is applied to carry out a one day long self-weight consolidation of dredged/reclaimed clay in foreign and domestic scenarios because it can surmount the limitations of consolidation time and initial height through a back-analysis study using the time-settlement results of the test. The back analysis using the centrifugal experiment is explained as a flowchart in Figure 9. The equipment used for the centrifuge test in this study was from the Kangwon National University in Korea. The centrifugal experiment was carried out 2–4 times under different initial void ratio and height conditions for each specimen, and then a constitutive relationship equation was estimated through the back analysis from the experimental results. The experimental conditions such as test number, gravity acceleration, initial void ratio, and constitutive relationship equations analyzed by the back analysis for each region specimen are shown in Table 2. Numerical analysis for the self-weight consolidation test in the flowchart was performed by the microcomputer program 'Primary Consolidation, Secondary Compression, and Desiccation of Dredged Fill' (PSDDF) by Stark et al. [3,4].

**Figure 9.** Flowchart of back analysis of constitutive relationship using the centrifugal experiment.

**Table 2.** Centrifugal experimental conditions and results.


**1. Introduction**

#### **1.1 Liver trauma**

Trauma is the leading cause of death in people aged 1–44 years, with hemorrhage being the primary cause of preventable death, accounting for 30–40% of fatalities [1]. The liver is the main organ affected in penetrating abdominal trauma in 35–45% of cases, mainly due to its susceptible and relatively superficial location in the right hypochondrium [2], and is the most commonly injured organ in patients suffering blunt abdominal trauma as well [3].

### **2. Incidence**

Data from the National Trauma Data Bank (NTDB) showed that liver injury occurs in almost 40% of victims of blunt abdominal trauma with an overall mortality of 14.9% [4]. Liver trauma can range from minor lacerations or capsular hematomas with minimal morbidity and mortality to hepatic avulsions with high mortality. Most hepatic injuries are minor and can be graded using the American Association for the Surgery of Trauma Hepatic Injury Scale as described under the "classification" topic ahead [5].

The right lobe of the liver, being the largest portion of the liver parenchyma, constitutes the region most affected during abdominal injury. It is known that it occurs more frequently in males and in young individuals, in the first four decades of life, in the majority of cases. Associated factors include risky behavior, such as alcohol and drug consumption, and more exposure to accidents. The mortality of patients with liver trauma ranges from 14.9–20%. When associated with shotgun lesions, the severity of the injury tends to be higher; therefore, the mortality could be up to 20% [6].


*3.4.4 Vitamin E*

Vitamin E deficiency after BS is rare. The reported incidence is 4.8% and 0.9% after RYGB and SADI respectively [31, 33]. The most common symptoms associated with vitamin E deficiency include neuropathy, myopathy and anemia [21] (**Table 1**). Vitamin E neuropathy and myopathy can be treated with a dose of vitamin E 400 IU daily.


**Abstract**

The last decade of the last century is marked by a revolution in the synthesis of materials for optical and photonic applications, against the background of the growing need for new high-performance materials to increase the efficiency, reliability and speed of response linked to environmental aspects. The diversity of requirements and the optimization of the responses has led to a major dispute over the structure and composition of these materials: Inorganic or Organic, Natural or Synthetic, Hybrid or Pure, which has stimulated interest in the development of various architectures. Special attention shall be paid to establishing a fundamental relationship to correlate the non-linear optical response and chemical structure, especially for the category of organic materials- particularly polymers- distinguished by structural/compositional versatility and suitable for processing by simple technique which allows serial production. In fact, optical nonlinearity (NLO) is not an exotic phenomenon. Indeed, all materials are optically nonlinear if light is sufficiently intense. The synthesis of functional photonic organic materials is a major challenge of contemporary community of material scientists to imagine new functional materials based of "collective" phenomena by virtue of the "engineered" molecule- molecule interactions and spatial relationships. In this context, this paper aims to highlight the most important features concerning the structural - compositional relationship of polymeric materials used in optoelectronic and photonic applications.

**Keywords:** polymer, azo-polymer, optical material, azo-chromophor, non linear optics, solvatochromic, photochromic, self anssembly


**4.4 Fitness function**

44 Woven Fabrics

0 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1

(B)

Fig. 4. (A) The schematic weave for the twill weave (B) The strings of the 8-harness twill

(A)

Weft

A main difference between genetic algorithms and more traditional optimization search algorithms is that genetic algorithms work with a coding of the parameter set and not the parameters themselves (Goldberg, 1989). Thus, before any type of genetic search can be performed, a coding scheme must be determined to represent the parameters in the problem in hand. In finding weave pattern solutions, consisting of proper combination of the three variables, including weave structure (i.e., the combination of warp float and weft float), warp yarn color, and weft yarn color. A multi-parameter coding, consisting of three sub-strings, is required to code each of the three variables into a single string. In this study, a binary coding is utilized and the bit-sizes of the encoding for the three variables are as follows. In a direct problem representation, the weave pattern variables themselves are used as a chromosome. A list of weave structure/warp color/weft color is used as chromosome representation, which represents the permutation of patterns associated with assigned weave structure, warp color and weft color. A gene is an ordered triple (weave structure, warp color, and weft color). This

Cweftk ……. Cweft1 Cwarpj ….. Cwarp1 fi ……… f1

Weft color Warp color Weave structure

representation belongs to the direct way, which is sketched in Figure 5.

Fig. 5. Representation Scheme of Chromosome

weave

Warp

**4.3 Chromosome**

In this study, the fitness function (i.e., evaluation function) is the user's preference. They are completely free for an operator to give a mark to the generated pattern without any restrictions depending on his/her being satisfied with it. GA is of an evolution capability based on the fitness value of each chromosome. The bigger the fitness of a chromosome is, the more probability it has to survive (be chosen). In other words, the gene (the feature) of the chromosome, which has bigger fitness than the others, will survive (be chosen) more easily to proceed with the operators, such as crossover, mutation, and reproduction, to create a brand new chromosome. Thus, the feature of the new generated chromosome will be inherited from the old chromosome composed of the required gene (i.e., feature). Moreover, each result of the generated chromosome for every generation can be reevaluated according to user's preference after examining the displayed pattern of weave structure decoded from generated parameters of WS, Cwarp, and Cweft1~Cweft8. By judging from the status of the displayed pattern, the user gives a mark (i.e., 0~1) to each of the pattern according to the satisfaction degree of each of them to the user. Thus, the fitness function is the user's preference and can be formed as Equation 6.

$$\text{Fitness(pattern)} = \text{User's preference(pattern)}\tag{6}$$

### **4.5 Example**

In this study, we use this system to search pattern parameters afforded to the predetermined specifications set as in Table 5, i.e., unit weave structure: 8 ends × 8 picks, the layout of warp yarn color is adopted as one color for simplification (if necessary, it can be set as various colors), and that of weft yarn is adopted as various colors. The desired pattern style is something like being of both the features of regular grid and interlacing twist.


**2. Linkage mapping in strawberry**

The principle of genetic mapping is that genes segregate on chromosome recombination during meiosis [20]. If two genes do not segregate independently, that means they are closely linked to each other on the same chromosome. The genes that are very close to each other or tightly linked will be transferred together from the parent to the progenies. While the two genes are not close to each other on the same chromosome the chance of them segregating separately from each other is increased. Thus, the genetic distance between genes can be calculated accordingly.

The first step of genetic mapping is the selection of two genetically very distant parents that demonstrate obvious genetic differences in many traits of interest. The parents must be far apart genetically for the exhibition of sufficient polymorphism. However, if two parents are too distant genetically, some undesirable events can accrue, such as sterility in progenies and high segregation distortion during the linkage analysis.

The structure of populations consists of two ways; firstly, inbred lines derived from homozygous parents; the second one is the inbreeding lines or cultivars and individuals derived from the crossing of the allogamy species. Thus, two-way

pseudo-testcross, half-sib, and full-sib populations derived from crossings can be used for genetic mapping in outcrossing species. The progenies from the selfing of the F1 generation (F2), backcross (BC), recombinant inbred lines (RILs), double haploids (DHs), and near isogenic lines (NILs) can be preferred for mapping in alloploid species according to the breeding objectives [14, 21, 22]. The selection of population type plays an important role in successfully constructing the linkage maps. Although F2 progenies are obtained from selfing F1 hybrids, BC lines are generated by crossing F1 progenies back with one of the parents. RILs are produced by single-seed selections derived from individuals of the F2 population. The selections should be continued from six to eight generations. Double haploid lines (DHLs) are developed by doubling the gametes of individuals. DHLs regenerate in tissue culture studies after chromosome doubling from pollen grains or haploid embryos derived from crosses.

The first aim of molecular breeding is the construction of linkage groups. The linkage mapping detects genes or markers association with molecular markers and their chromosomal positions and they are valuable tools in order to perform positional cloning of desired/known genes or related regions and to consist of contigs and scaffolds of physical maps at the chromosomal level.

The first linkage mapping report in *Fragaria* was performed using an isozyme marker and a morphological (yellow fruit color) trait, using individuals of a cross between *F. vesca* cultivars Yellow Wonder and Baron Solemacher [23]. Another finding was reported that an isozyme locus (PGI-2) was used in the same population in non-runnering r locus-related morphological traits [24]. The first linkage map in *Fragaria* was constructed similarly in diploid *F. vesca* [25]. The difference between these maps was that they were constructed in the F2 population produced by crossing Baron Solemacher and W6, a non-runnering European cultivar belonging to *Fragaria vesca ssp. vesca*, and *Fragaria vesca ssp. americana* W6, a wild runnering genotype. A total of 75 random amplified polymorphic DNA (RAPD) markers, two isozymes, an STS marker associated with the alcohol dehydrogenase gene, and the runnering locus were used in linkage mapping, and the length of the map in seven linkage groups was calculated as 445 cM.

The genetic linkage map of octoploid strawberries was constructed by using amplified fragment length polymorphism (AFLP) markers [26]. A total of 515 markers and 119 F1 plants of a two-way pseudo-test cross of Capitola (CA75.121– 101 *×* Parker) and CF1116 (Pajaro *×* (Earliglow *×* Chandler)) were used in linkage map construction. A total of 28 and 30 linkage groups were constructed in the maternal and paternal linkage maps. The female map covered 1604 cM and the male map length was 1496 cM. The second map of the diploid *Fragaria* population was generated from the F2 population developed from an interspecific hybridization of *F. vesca* 815 and *F. bucharica* 601 [27]. A total of 78 markers were mapped, 68 of them were simple sequence repeats (SSRs), and the map length was computed as 448 cM. This mapping was important because it was the first SSR-based linkage map in strawberry. This map was enriched by different mapping studies using restriction fragment length polymorphism (RFLPs), AFLPs, SSRs, SNPs, expressed sequence tags (ESTs) and gene-specific markers located in seven linkage groups and the total map length was 528.1 cM. This map is considered the reference framework map in the genus *Fragaria* [27–32].

An AFLP-based genetic map was generated by Lerceteau-Köhler et al. [26], and this map was enriched by Rousseau-Gueutin et al. [33]. The researchers explained that a total of 213 genotypes and many AFLP markers, sequence-characterized

### *Quantitative Trait Loci Associated with Agronomical Traits in Strawberry DOI: http://dx.doi.org/10.5772/intechopen.108311*

amplified regions (SCARs) and SSRs were located in linkage groups. In the female map, 367 loci were mapped into 28 linkage groups, and the map length was 2582 cM, while a total of 440 markers were placed into 26 linkage groups in the male map and the length was 2165 cM. The final map is considered as the first comprehensive reference map for octoploid strawberries. This map helps the construction of the homoeologous of the four *Fragaria* x *ananassa* linkage groups using the *F. vesca* reference map. Another linkage map was also constructed in *F. virginiana*. A total of 319 and 331 SSR-based markers were located in 33 and 32 linkage groups which belong to maternal and paternal maps, respectively [34, 35]. One of the most comprehensive linkage maps in octoploid strawberries was constructed with 490 transferrable SSR or gene-specific markers in individuals obtained from Redgauntlet *×* Hapil. The authors stated that the constructed linkage map can be used as a framework for future genetic studies and to facilitate the marker-assisted selection in octoploid strawberry breeding. This high-density map was constructed which represents 91% of the *Fragaria* x *ananassa* genome having a map length of 2140.3 cM and consisting of 549 markers [36].

A total of 186 SSR markers were mapped into 28 linkage groups in octoploid strawberry, derived from Holiday *×* Korona crossing, the estimated map length was 2050 cM [37]. Another linkage map in *Fragaria* x *ananassa* was generated using a total of 4474 SSRs (3746 EST-SSRs in *F. vesca*, 603 EST-derived in *Fragaria* x *ananassa,* and 125 transcriptomic-derived from *Fragaria* x *ananassa*) were used for mapping. All markers were first mapped onto a parent-specific linkage map using three different populations such as 02–19 *×* Sachinoka, Kaorino *×* Akihime, and 0212921 *×* 0212921 inbred lines which were assembled onto one linkage map. The constructed map length was calculated as 2364.1 cM [38]. The backcross progenies of a cross between *F. vesca* and *F. viridis* were utilized in linkage mapping and the map length was computed as 241.6 cM [39, 40]. The construction of linkage groups in octoploid strawberries can be used for comparative studies between the diploid and octoploid maps.

The development of NGS technology and a decrease in unit cost in sequencing increased the improvement of the different sequencing platforms such as ddRAD-seq, DarT-seq, and WGRS (whole genome re-sequencing). Construction of the well-saturated linkage maps is very important for the discrimination of the homoeologous belonging to each chromosome in strawberries. Tennessen et al. [41] used a novel approach called Phylogenetics of Linkage-MapAnchored Polyploid Sub-genomes (POLiMAPS) and understanding of the octoploid strawberry sub-genomes in *F. vesca* subsp. *bracteata*, *Fragaria chiloensis*, and *F. virginiana* populations. The researchers stated that one of these sub-genomes is close to diploid *F. vesca* and the diploid *F. iinumae*, others are close to an unknown ancestor of *F. iinumae*. Meanwhile, the IStraw 90 K Axiom® array was developed by the RosBREED project with international cooperation. The IStraw 90 K Axiom® array was mined from the first draft of the *F. vesca* genome (v1.0). This array contributed to the construction of the map for detection of the homoeologous sub-genomes [42]. It was constructed by using 26 *Fragaria* species, 16 *F.* x *ananassa* and 10 individuals belonging to wild strawberry species. Totally, mined 6594 SNP loci were located on the first SNP linkage map generated by Bassil et al. [42]. The digest restriction-associated DNA sequencing and diversity array technology were used for the construction of high-density linkage maps (ddRAD-Seq, [43]; DarT, [44]). In another study, the IStraw90 SNP array and genotyping by sequencing technology (GBS) were utilized by Mahoney et al. [45] in order to construct a highly saturated

linkage group in diploid strawberry, *F. iinumae*. Sargent et al. [36] consisted of the highly saturated linkage map using many polymorphic loci (8407 SNPs) and the IStraw 90 K Axiom® and this map contributed to the detection of the *F. ananassa* genome structure. The SNP alleles on the linkage map were compared with the corresponding *F. vesca* and *F. iinumae* alleles, and the haploSNPs were categorized in the current study. HaploSNPs, which are sub-genome specific markers that have saturated common markers, can be used to compare QTL regions in strawberries, revealing the ancestral-associated loci [36]. These linkage maps provided a better understanding of the genome structure of the octoploid strawberry and increased the interest in SNP-based linkage mapping studies for practical breeding applications.

In strawberry breeding, the development of linkage maps is very important in order to reveal the genetic structure of complex traits because this complexity can be only solved using the QTL mapping and/or association mapping approaches [46]. Although a few association mapping studies have been reported in strawberries, many genetic mapping studies have been performed using biparental populations that have been generated from different crosses [26, 32, 33, 47–50].


**1. Introduction**

The origins of STEM Education in the United States can be traced back to the launch of Sputnik by the Soviet Union in 1957. There was concern in the Western World that national security required investing in science and mathematics education to maintain safety and remain competitive economically. Over time, concerns about STEM competitiveness have increasingly focused more heavily on economic competitiveness and technological innovation in a global economy. This trend can be seen in reports such as *A Nation at Risk* [1], which served as a reaction to the U.S. perception that it was losing its competitive edge as new economic powers were rising in Asia and Europe. Additionally, the release of the results of the *Trends in International Mathematics and Science Study* (TIMMS) beginning in 1995 showed that U.S. students were not achieving at high levels when compared to students in other developed nations. These same achievement trends had also been presented in the Program for International Student Assessment (PISA) results since 1997, inspiring the 2007 National Academy of Sciences report *Rising above the gathering storm: Energizing and*

*employing America for a brighter economic future* (RAGS), and its sequel, *Rising Above the Gathering Storm Revisited: Approaching Category 5* (RAGSR), in 2010 which once again sounded the alarm concerning U.S. student achievement in relation to other countries internationally [2, 3]. However, compared to earlier alarmist reports, the RAGS report provided suggestions for educational interventions that were practical at scale. These recommendations, referred to as *actions,* included the following:


Corrected p value (pc) not significant, @ Corrected p value (pc) significant,

Controls No. (%)

*CC (L)* 11 (4.7) 1 (0.78) 0.03# 4.3 (1. 21-

*Low secretor* 8 (3.4) 1 (0.8) 0.11 3.17 (0.87-

**genotypes and haplotypes**

*, IL-10, IL-6* and *TGF-*

genes interact in integrated networks. Since only *TNF-*

is encoded on chromosome 6p21.3, *IFN-*

T1D No. (%)

*IL-6* is encoded on 7p21 and *TGF-*

*TNF-, IFN-*

cytokine genes.

Genotype / haplotype

*AA (L)* 41

*TA+TT (H)* 56

*GG+GC (H)* 86

*Low secretor* 45

*High Secretor* 52

*High Secretor* 89

ACC,ACC; ACC,ATA = Low secretors.

Table 2. Simultaneous presence of *TNF-*

genotypes and haplotypes (Kumar et al., 2007).

CG,CC, CC,CC = Low secretors.

*IL-10*

*TGF-β1*

genotypes.

mRNA levels of insulin than *Class-I* in thymii of fetuses, suggesting poor expression of thymic INS expression resulting in poor thymic education for insulin in people with homozygous *Class-I,I* and *class-I,III VNTRs* resulting in break of tolerance in predisposed individuals. However, higher expression of insulin in the thymii of individuals with homozygous *class-III, III* may be able to facilitate immune tolerance induction, as a mechanism for dominant protective effect of *Class-III* alleles (Pugliese et al., 1997, Vafiadis et al., 1997).

Our results are contrary to that of Veijola et al. (Veijola et al., 1995) on Finnish children who showed that both 5' and 3' INS loci showed an association with T1D in *non-DR3/non-DR4* patients (Veijola et al., 1995). However, in our studies only 9.47% of the non-*DR3/DR4*  patients were homozygous for *Class-I INS-VNTR* as compared to 4.2% controls and this difference was not significant statistically. Julier et al. (Julier et al., 1991), on the other hand, had reported that the risk contributed by the INS region was increased in *DR4-*positive patients. Again, in our study only 6.32% of the patients as compared to 1.39% of controls had *INS-VNTR class-I* homozygosity with *DRB1\*04:01* and *DRB1\*04:05* alleles and this difference was not significant statistically.

#### **4. Cytokine genes**

Cytokines are the coordinators of the immune system that interact in integrated networks and functions of one cytokine may be modulated or substituted by another (Bidwell et al., 1999). A cascade of cytokines are involved in pro-inflammatory auto-immune responses in T1D. Single nucleotide polymorphisms (SNPs) in different pro-inflammatory and antiinflammatory cytokine genes at certain defined regions have been shown to be associated with differential amount of their production (Asderakis et al., 2001, Awad et al., 1998, Bittar et al., 2006, Burzotta et al., 2001, Fishman et al., 1998, Louis et al., 1998, Pociot et al., 1993). Pro-inflammatory cytokines and their integrated influences are known to regulate complex immune responses during autoimmune destruction of tissues (Rabinovitch, 1994). Hence it is necessary that they are studied and analysed in context of each other and not in isolation from each other. We had reported for the first time the integration and interaction of *TNF-* gene with other cytokine genes and *HLA-DRB1* and *B* loci alleles (Kumar et al., 2007).

We studied the cytokine gene polymorphism using XIIIth International Histocompatibility Workshop's (IHWC, Heidelberg kit) and One lambda's cytokine typing kits (Canoga Park, CA, USA) based on Polymerase Chain reaction (PCR) with sequence specific primers (PCR-SSP). PCR-SSPs were done for *IFN- (A+874T)* (14), *TNF- (G-308A)* (15), *IL-6 (G-174C)* (9), *IL-10 (A-1082G, T-819C, C-592A*) (16), and *TGF1 (Tcdn10C, Gcdn25C*) (11). *T→C* substitution in nucleotide 29, codon 10 of the first exon of TGF1, changes the amino acid Leu → Pro. Similarly *G→ C* substitution in nucleotide 74, codon 25 of first exon of *TGF1*, changes the amino acid Arg → Pro. However, since we are studying the SNPs in the two codons, we will refer to the SNPs in codons 10 and 25 hereafter. and not the resultant amino acids to avoid any confusion and to maintain consistency with the other SNPs.

Our results showed that the high producing genotype of *TNF--308GA* and *AA* were significantly increased and low producing genotype *GG* was significantly reduced in T1D patients as compared to controls (p < 7 x10-6). None of the other cytokine genes showed any significant difference between the patients and controls.

mRNA levels of insulin than *Class-I* in thymii of fetuses, suggesting poor expression of thymic INS expression resulting in poor thymic education for insulin in people with homozygous *Class-I,I* and *class-I,III VNTRs* resulting in break of tolerance in predisposed individuals. However, higher expression of insulin in the thymii of individuals with homozygous *class-III, III* may be able to facilitate immune tolerance induction, as a mechanism for dominant protective effect of *Class-III* alleles (Pugliese et al., 1997, Vafiadis et

Our results are contrary to that of Veijola et al. (Veijola et al., 1995) on Finnish children who showed that both 5' and 3' INS loci showed an association with T1D in *non-DR3/non-DR4* patients (Veijola et al., 1995). However, in our studies only 9.47% of the non-*DR3/DR4*  patients were homozygous for *Class-I INS-VNTR* as compared to 4.2% controls and this difference was not significant statistically. Julier et al. (Julier et al., 1991), on the other hand, had reported that the risk contributed by the INS region was increased in *DR4-*positive patients. Again, in our study only 6.32% of the patients as compared to 1.39% of controls had *INS-VNTR class-I* homozygosity with *DRB1\*04:01* and *DRB1\*04:05* alleles and this

Cytokines are the coordinators of the immune system that interact in integrated networks and functions of one cytokine may be modulated or substituted by another (Bidwell et al., 1999). A cascade of cytokines are involved in pro-inflammatory auto-immune responses in T1D. Single nucleotide polymorphisms (SNPs) in different pro-inflammatory and antiinflammatory cytokine genes at certain defined regions have been shown to be associated with differential amount of their production (Asderakis et al., 2001, Awad et al., 1998, Bittar et al., 2006, Burzotta et al., 2001, Fishman et al., 1998, Louis et al., 1998, Pociot et al., 1993). Pro-inflammatory cytokines and their integrated influences are known to regulate complex immune responses during autoimmune destruction of tissues (Rabinovitch, 1994). Hence it is necessary that they are studied and analysed in context of each other and not in isolation from each other. We had reported for the first time the integration and

We studied the cytokine gene polymorphism using XIIIth International Histocompatibility Workshop's (IHWC, Heidelberg kit) and One lambda's cytokine typing kits (Canoga Park, CA, USA) based on Polymerase Chain reaction (PCR) with sequence specific primers (PCR-

 *(A+874T)* (14), *TNF-*

29, codon 10 of the first exon of TGF1, changes the amino acid Leu → Pro. Similarly *G→ C*

→ Pro. However, since we are studying the SNPs in the two codons, we will refer to the SNPs in codons 10 and 25 hereafter. and not the resultant amino acids to avoid any

significantly increased and low producing genotype *GG* was significantly reduced in T1D patients as compared to controls (p < 7 x10-6). None of the other cytokine genes showed any

substitution in nucleotide 74, codon 25 of first exon of *TGF*

confusion and to maintain consistency with the other SNPs.

significant difference between the patients and controls.

Our results showed that the high producing genotype of *TNF-*

gene with other cytokine genes and *HLA-DRB1* and *B* loci alleles

*1 (Tcdn10C, Gcdn25C*) (11). *T→C* substitution in nucleotide

 *(G-308A)* (15), *IL-6 (G-174C)* (9), *IL-10*

*1*, changes the amino acid Arg

*-308GA* and *AA* were

al., 1997).

difference was not significant statistically.

SSP). PCR-SSPs were done for *IFN-*

*(A-1082G, T-819C, C-592A*) (16), and *TGF*

**4. Cytokine genes**

interaction of *TNF-*

(Kumar et al., 2007).

#### **4.1 Simultaneous presence of TNF- genotypes with IFN-, IL-6, IL-10 and TGF-1 genotypes and haplotypes**

*TNF-, IFN-, IL-10, IL-6* and *TGF-1* genes are localized on different chromosomes. *TNF-* is encoded on chromosome 6p21.3, *IFN-* is encoded on 12q14, *IL-10* is encoded on 1q31-q32, *IL-6* is encoded on 7p21 and *TGF-1* is encoded on 19q13.2. However, the products of these genes interact in integrated networks. Since only *TNF-* showed a significant association with T1D, we studied whether simultaneous presence of *TNF-* genotypes with different genotypes of the other cytokines in an individual could suggest an interaction between these cytokine genes.

N=Total number of samples studied, \$Number of control samples studied for IL-6 were 127, one sample could not be typed due to PCR failure. TNF-α GA/AA have been combined as high secretor genotypes.


*Smartphones for Vision Rehabilitation: Accessible Features and Apps, Opportunity… DOI: http://dx.doi.org/10.5772/intechopen.97703*

technology-interaction i.e., interface, is depend on vision, then visually impaired people face a tremendous limitation to access all kind of the information present in the smartphone. With ongoing advances and sophistication in mobile technology, the smartphone can be operated with eyes-free interface in an efficient way. These accessible applications are based on audio and tactile/haptic based interaction substituting the need of visual based interaction. There are many "apps" available that are accessible to people with visual disability. Few examples are given with a brief description of each in **Table 2**. **Figure 6** represents illustrations of few accessible apps. Majority of these apps are freely available online, so a visually impaired person can download, and install any specific app for their purpose.

These applications enable the smartphones serve as assistive technology for visually impaired people and facilitate to do tasks related to independent living, performing daily activities, to engage in education, societal activities, help in access information in today's digital society, sighted help, and to improve the overall quality of life. For example, the "Kibo" application helps an individual with a visual disability to access all electronic prints or pdf files or word document files (**Figure 7**); Be My Eyes for sighted help; Visor for color inverts; Mani app for currency identification and RightHear for navigation.

**Figure 6.** *Third Party accessible applications.*

**Figure 7.** *Kibo accessible application.*