*3.1. Weight- and Sex-Specific Differences in Experiences of Workplace Bullying*

Regarding experiences of workplace bullying, we found a significant main effect of weight status, *F* (2, 1276) = 4.24, *p* = 0.02 (*d* = 0.16), but no significant main effect of sex, *F* (1, 1276) = 1.80, *p* = 0.18 (*d* = 0.07), and no significant interaction effect of weight status x sex, *F* (2, 1276) = 0.73, *p* = 0.48 (*d* = 0.07). Post-hoc analyses using a Bonferroni-corrected significance level of 0.016 revealed that, compared with participants with normal weight, levels of experiences of workplace bullying were significantly greater in participants with obesity (*p* = 0.01, *d* = 0.34). The level of experiences of workplace bullying did not significantly differ between participants with normal weight and those with overweight (*p* = 0.04, *d* = 0.12) and between those with overweight and those with obesity (*p* = 0.15, *d* = 0.17).


Osteosarcoma – Diagnosis, Mechanisms, and Translational Developments

*Edited by Matthew Gregory Cable and Robert Lawrence Randall*

Published in London, United Kingdom

## *Supporting open minds since 2005*

Osteosarcoma – Diagnosis, Mechanisms, and Translational Developments http://dx.doi.org/10.5772/intechopen.73987 Edited by Matthew Gregory Cable and Robert Lawrence Randall

#### Contributors

Mulazim Hussain Bukhari, Farwa Batool, Samina Qamar, Mandeep Bedi, Kanya Honoki, Shingo Kishi, Yasuhito Tanaka, Hiroki Kuniyasu, Christos Valavanis, Gabriela Stanc, Maxim Rykov, Elmira Sengapova, Matthew Gregory Cable, Scott Barnett

#### © The Editor(s) and the Author(s) 2019

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**Author details**

Begoña Díaz de la Noval Consultant at Gynaecology and Obstetrics, Hospital Universitario Central de Asturias, Spain

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*Role of Sentinel Node Biopsy in Endometrial Cancer DOI: http://dx.doi.org/10.5772/intechopen.89949*


**3.1. Molecular breeding**

The role of molecular markers in facilitating selection has substantially increased in the past three decades. The rapid accumulation of genomic resources provides researchers with an unprecedented wealth of information to access and manipulate genetic variation that is useful for crop improvement [113]. Genomics-assisted breeding is expected to enhance the accuracy and efficiency of breeding programs to deliver superior cultivars for sustainable agriculture. The ultrahigh throughput and decreasing cost of genotyping have elicited concepts such as genomics-assisted breeding [52] and breeding-assisted genomics [114]. Currently, the new paradigm among the Consortium of International Agricultural Research Centers (www.cgiar.org) is to mobilize "Omics" and bioinformatics-enabled interventions to assess the level of available genetic variation, to broaden the genetic bases by creating new intra- and inter-species variations, to construct new cultivars with combinations of desirable and novel traits in more efficient and effective selection schemes. The ultimate goal is to accelerate genetic gain, which will contribute to improved food and nutritional security, in an environmentally sustainable way, in low-income countries.

The unprecedented scientific and technological progress in the fields of genomics and bioinformatics can successfully be harnessed to benefit smallholder farmers in developing countries. In the face of limited agricultural inputs in developing countries, genetic improve‐ ment can play a crucial role in raising crop productivity in an environmentally sustainable way. Spurred by steadily declining costs of genotyping and unparalleled progress in compu‐ tational abilities, modern genomic tools and processes are being used to devise an efficient and effective breeding strategy. The prominent constraints to breeding progress are slow genetic gain, complex traits, and genotype by environment interaction. Besides these generic con‐ straints, neglected crops of Africa were affected by a paucity of genomic information until the dawn of NGS.

It is now feasible to access genome-wide nucleotide variation by re-sequencing the whole genome of thousands of accessions or by deploying one of the complexity reduction methods to generate high-density, genome-wide SNP markers associated with key agronomic traits attributed to quality, resilience to climate change, and biotic stresses. These technological advances led to the design of experimental populations involving multiple parents, in addition to the classical genetic mapping within specific biparental crosses. An overview of IITA's (and CGIAR's) activities in addressing crop productivity and other agricultural problems has been documented [4].

Evidence is emerging that the massive availability and accessibility of genomic resources and data management tools are paving the way for the deployment of innovative technologies to accelerate genetic gain. A number of recent reviews analyze the potential benefit of the Omics technologies to agricultural productivity and highlight various limitations that need to be addressed [19,27,52,115].

cassava [110], banana [111], and yams [112]. Survey of the incidence and distribution of viruses infecting these crops makes it one of the important tools for understanding the microbial genetics, physiology, and community ecology. The benefit of metagenomics extends to agriculturally important microbes, both disease causing and beneficial, in plant and animal

The role of molecular markers in facilitating selection has substantially increased in the past three decades. The rapid accumulation of genomic resources provides researchers with an unprecedented wealth of information to access and manipulate genetic variation that is useful for crop improvement [113]. Genomics-assisted breeding is expected to enhance the accuracy and efficiency of breeding programs to deliver superior cultivars for sustainable agriculture. The ultrahigh throughput and decreasing cost of genotyping have elicited concepts such as genomics-assisted breeding [52] and breeding-assisted genomics [114]. Currently, the new paradigm among the Consortium of International Agricultural Research Centers (www.cgiar.org) is to mobilize "Omics" and bioinformatics-enabled interventions to assess the level of available genetic variation, to broaden the genetic bases by creating new intra- and inter-species variations, to construct new cultivars with combinations of desirable and novel traits in more efficient and effective selection schemes. The ultimate goal is to accelerate genetic gain, which will contribute to improved food and nutritional security, in an environmentally

The unprecedented scientific and technological progress in the fields of genomics and bioinformatics can successfully be harnessed to benefit smallholder farmers in developing countries. In the face of limited agricultural inputs in developing countries, genetic improve‐ ment can play a crucial role in raising crop productivity in an environmentally sustainable way. Spurred by steadily declining costs of genotyping and unparalleled progress in compu‐ tational abilities, modern genomic tools and processes are being used to devise an efficient and effective breeding strategy. The prominent constraints to breeding progress are slow genetic gain, complex traits, and genotype by environment interaction. Besides these generic con‐ straints, neglected crops of Africa were affected by a paucity of genomic information until the

It is now feasible to access genome-wide nucleotide variation by re-sequencing the whole genome of thousands of accessions or by deploying one of the complexity reduction methods to generate high-density, genome-wide SNP markers associated with key agronomic traits attributed to quality, resilience to climate change, and biotic stresses. These technological advances led to the design of experimental populations involving multiple parents, in addition to the classical genetic mapping within specific biparental crosses. An overview of IITA's (and CGIAR's) activities in addressing crop productivity and other agricultural problems has been

production.

dawn of NGS.

documented [4].

**3.1. Molecular breeding**

**3. Application to crop improvement**

298 Next Generation Sequencing - Advances, Applications and Challenges

sustainable way, in low-income countries.

The two major approaches in the new paradigm of molecular breeding are (1) MAS for highly heritable traits and (2) GS for complex traits. These approaches involve the genotypic screening of large numbers of individuals at an early stage, selection at the seedling stage, and extensive phenotypic evaluation of fewer materials at a later stage. This reduced breeding cycles and the cost of multi-environment testing. Strategies such as GS also allow simultaneous selection for multiple traits through a selection index [52,116–119].

Broadly, there are two approaches to exploit QTLs. The first application is to detect large-effect QTLs with linkage or association analysis, whereas approaches such as GS utilize the compu‐ tation of an individual breeding value based on genome-wide marker genotype, without taking into consideration the single small-effect QTLs in the prediction model.

Numerous reviews, opinion articles, and research papers have addressed the benefit, chal‐ lenges, and prospect of GS crystallized in a recent review [113]. The salient features of GS include benefits such as increased gain from selection, reduced breeding cycles, and thus reducing cultivar development costs. Other advantages include utilization of genome-wide markers, afforded by ultrahigh-throughput NGS assays (compared to predecessor approaches to estimate breeding values), as well as the ability to target multiple traits for multiple environments. In clonally propagated crops, an additional advantage is the use of historical phenotype data to refine the prediction model.

Given the long cycle of breeding, African staple crops such as cassava are set to benefit from GS approaches [117,118,120], where preliminary results have indicated reduced time of breeding cycle and reasonable prediction accuracy in some traits. Various ways of refining the prediction models via repeated phenotypic evaluations are being considered. Fig. 1 depicts a 1-year GS-based breeding cycle that is underway at IITA, Nigeria. The challenge in this breeding scheme is, however, the situation of erratic flowering in some lines, which hinders recombination of selected clones due to failure to flower. Addressing the biology of flowering using genomics tools is imperative. In cereals, current studies are investigating at least two key applications of GS in maize and wheat breeding programs – predicting the genotypic values of individuals for potential release as cultivars and predicting the breeding value of candidates in rapid cycle populations. Prediction accuracy is affected by genetic relatedness of the populations and the heritability of the trait, where the prediction accuracy is lower in complex traits [121].

Utilization of molecular technologies that have revolutionized commercial crop breeding can be used as a proof of concept for adoption of such genomics-based prediction methodologies [122,123] to improve trait performance in other less-studied crops [115,116]. These approaches are being adopted in crops of importance in developing countries such as in maize and wheat [121], rice [124], pulses (legumes) [11], cassava [118,120], cowpea [125], lentil [126], soybean [127,128], and pigeon pea [129]. With respect to the best practice for GS, various models are being put forward [113]. Below is the rapid cycling breeding scheme for cassava, a long cycle clonally propagated crop (Figure 1).

**Figure 1.** An overview of genomic selection-based annual breeding cycle implemented for cassava at the International In‐ stitute of Tropical Agriculture (IITA) in Nigeria. In June, crossing blocks are planted with parents selected using genom‐ ic selection and crosses made between September and November. Mature seeds are germinated and transplanted in January under irrigation. DNA is extracted from seedlings in March for genotyping by sequencing at the Genomic Diver‐ sity Facility (GDF). Raw SNP data are released to "Cassavabase" for further processing. Genomic-estimated breeding val‐ ues (GEBVs) are then calculated and used to select candidate parents for the next recombination cycle. The remaining clones are also evaluated in clonal evaluation yield trials for variety development as well as for re-training the GS predic‐ tion model. **Cassavabase** (www.cassavabase.org): A bioinformatics infrastructure that integrates phenotypic data from field trials, genotypic data, as well as statistical tools in a single, user-friendly, web-based, and reliable database [130]. Breeders can use the intuitive web-based interphase to calculate genomic-estimated breeding values (GEBVs) of individ‐ uals by selecting a training population for modeling and estimating genomic-estimated breeding values of selection can‐ didates (http://cassavabase.org/solgs). **GDF:** Genomic Diversity Facility (http://www.biotech.cornell.edu/brc/genomicdiversity-facility) provides expertise and state-of-the-art support for genotyping by sequencing (GBS) projects, including project optimization, library production, DNA sequencing, and data analysis.

It has now become evident that with advances in genotyping, fueled by NGS, phenotyping has become the rate-limiting step in genomics-enabled breeding. Concomitant development in phenotyping speed and precision is pivotal to associate genome with phenome [131] and to enable routine cost-effective high-throughput precision phenotyping. Approaches to increase throughput and quality of phenotyping range from automated and mechanized field experi‐ ment management, digital data capture, improved sample tracking methods, to deployment of ground-based and aerial advanced technologies in imaging and remote sensing [132–135]. Precision phenotyping has led to accelerated genetic gain by increasing heritability, mainly through reducing environmental variation [116,131], and reduced cost of trait measurement. Furthermore, robust and standardized screening protocols and the establishment of pheno‐ typing hubs for abiotic (drought, nutrient use efficiency) and biotic (pest and disease hotspots) stresses are key elements for precision phenotyping to dissect the genetics of quantitative traits.

Leveraging existing data management and decision support tools to accommodate new data types and analytical tools, including digitized data collection (e.g., personal digital assistant (PDA), electronic field books) and sample tracking using bar codes, will be keys to the ultimate success of genomics breeding in developing countries.

#### **3.2. Genetic resource management and utilization**

[127,128], and pigeon pea [129]. With respect to the best practice for GS, various models are being put forward [113]. Below is the rapid cycling breeding scheme for cassava, a long cycle

**Figure 1.** An overview of genomic selection-based annual breeding cycle implemented for cassava at the International In‐ stitute of Tropical Agriculture (IITA) in Nigeria. In June, crossing blocks are planted with parents selected using genom‐ ic selection and crosses made between September and November. Mature seeds are germinated and transplanted in January under irrigation. DNA is extracted from seedlings in March for genotyping by sequencing at the Genomic Diver‐ sity Facility (GDF). Raw SNP data are released to "Cassavabase" for further processing. Genomic-estimated breeding val‐ ues (GEBVs) are then calculated and used to select candidate parents for the next recombination cycle. The remaining clones are also evaluated in clonal evaluation yield trials for variety development as well as for re-training the GS predic‐ tion model. **Cassavabase** (www.cassavabase.org): A bioinformatics infrastructure that integrates phenotypic data from field trials, genotypic data, as well as statistical tools in a single, user-friendly, web-based, and reliable database [130]. Breeders can use the intuitive web-based interphase to calculate genomic-estimated breeding values (GEBVs) of individ‐ uals by selecting a training population for modeling and estimating genomic-estimated breeding values of selection can‐ didates (http://cassavabase.org/solgs). **GDF:** Genomic Diversity Facility (http://www.biotech.cornell.edu/brc/genomicdiversity-facility) provides expertise and state-of-the-art support for genotyping by sequencing (GBS) projects, including

It has now become evident that with advances in genotyping, fueled by NGS, phenotyping has become the rate-limiting step in genomics-enabled breeding. Concomitant development in phenotyping speed and precision is pivotal to associate genome with phenome [131] and to enable routine cost-effective high-throughput precision phenotyping. Approaches to increase throughput and quality of phenotyping range from automated and mechanized field experi‐ ment management, digital data capture, improved sample tracking methods, to deployment of ground-based and aerial advanced technologies in imaging and remote sensing [132–135].

project optimization, library production, DNA sequencing, and data analysis.

clonally propagated crop (Figure 1).

300 Next Generation Sequencing - Advances, Applications and Challenges

Genebanks play an important role in safeguarding crop genetic diversity against the ongoing loss. They provide genetic variation for breeding for continued adaptation to changing environmental conditions and consumer demands [136,137]. The recent prog‐ ress in DNA sequencing technologies that require less investment for generating large data is an opportunity to further investigate genetic variation maintained in the large germ‐ plasm collections held in trust by the CGIAR and increase the efficiency of genebanks. The 11 genebanks of the CGIAR conserve over 666,000 accessions of mainly food crops [138]. The International Institute of Tropical Agriculture (IITA) maintains over 28,000 accessions of major food crops of Africa, namely cowpea (*Vigna unguiculata*), cassava (*Manihot esculenta*), yam (*Dioscorea* spp.), soybean (*Glycine max*), bambara groundnut (*Vigna subterra‐ nea*), maize (*Zea mays*), and plantain and banana (*Musa* spp.). The aforementioned, including other important crops in developing countries [e.g., finger millet (*Eleusine coracana*), tef (*Eragrostis tef*), enset (*Ensete ventricosum*), grass pea (*Lathyrus sativus*) and their wild relatives], were considered understudied [2]. Large-scale characterization of all accessions and other genetic stocks is imperative to stimulate their utilization in breeding programs [139,140].

Traditionally, genebanks have used morphological descriptors for germplasm characteriza‐ tion; however, these are highly influenced by environmental conditions and different stages of plant development [141]. Moreover, the number of descriptors can be quite limited, thus greatly reducing the power to distinguish consanguineous varieties [142]. Molecular marker technologies have been widely applied for characterization and utilization of germplasm in genebanks [143]. However, the marker systems used prior to the advent of NGS, which sample a subset of the genome, have restricted applications mainly because of their limited abundance in the genome. NGS has enabled marker analysis at a much higher density. NGS-based genotyping, such as GBS, has been used for genetic diversity assessment of cultivated yam and its wild relatives [144] and cocoa [145], as well as other crop species. Breeding programs in the public and private sector deploy whole-genome fingerprinting of inbreds, to get an insight into the haplotype-level genetic diversity [116,140,146].

The advance in sequencing technologies is an advantage for efficient sequencing of large collections that include poorly studied species in genebanks with larger analytical power than the conventional molecular marker systems. Diversity assessments per se have huge utility in terms of germplasm utilization, such as definition of heterotic groups that enable breeders to make decisions in planning crosses for the population development. In addition to diversity assessment, NGS-based technologies are likely to impact further analysis of genetic variation, in terms of characterization of functional genetic diversity [148] and can be applied to pre-breeding activities to boost utilization of genetic resources in breeding programs [29,52,147].

NGS can also be applied to enhance management aspects of the genebanks, including identifying duplicates and identification of mislabeled accessions, both of which are common challenges in genebanks [148]. Diversity assessments using NGS could help guide the need for further targeted germplasm collection and improve the development of subsets of the collection, also referred to as core or minicore or diversity research sets, that would further improve the efficient utilization of germplasm for cultivar development.

A strong genomics and bioinformatics platform will greatly facilitate essential elements of genebank management, particularly the verification of accession identity, characterization of duplicates in the collection, and diversity analysis. Furthermore, rapid genotyping methods (e.g., GBS and WGS) will be essential for allele mining and large-scale associa‐ tion of genotype–phenotype, which are taken together with methods of developing traitspecific subsets, also referred to as core or mini core or diversity research sets, to greatly enhance the value of the collections for breeding and research. In particular gene pool, enhancement (pre-breeding) will be strengthened in terms of both base broadening within a species and use of crop wild relatives for the integration of key traits. Such approaches can be applied not only to staple crops but also to obtain rapid advances in the improve‐ ment of underutilized and under-researched but important crops such as cocoyam, winged bean, and African yam bean.

#### **3.3. Breeding data management**

The adoption of new Omics technologies by breeding programs in developing countries can contribute to the enhancement of breeding efficiency. There is a growing effort to harness advances in bio-computational methods and information and communication technology (ICT) to successfully utilize diverse phenotypic, environmental, genomic, and other metadata to provide decision support tools at various stages of the breeding pipeline. Modern breeding schemes such as GS and MAS involve a deluge of genotype data such as GBS-derived SNP markers, advanced statistical analysis to compute GEBV, and large amounts of high-throughput phenotype information, all of which require efficient informat‐ ics tools, automated data analysis pipelines, and decision-making tools for analysis and integration. Efficient utilization of such unprecedented volumes of genotypic, phenotypic, and other data entails development of informatics, database, and decision support tools.

Access to affordable genotyping platform by scientists in developing countries has been realized through various bilateral research-for-development projects. However, it is inconceivable to make progress without modern breeding tools and management process‐ es that will facilitate the integration, analysis, and decision-making tools. One initiative that aims at providing some of these tools is the breeding management system (BMS) devel‐ oped and promoted by the integrated breeding platform (IBP) (https://www.integrated‐ breeding.net/breeding-management-system). The service of BMS is delivered by IBP regional hubs that are strategically located throughout developing countries and hosted by partner research institutions such as IITA in Nigeria. The hubs provide support for adoption, customization, and use of BMS and related services, mainly through capacity building, technical support, and crop-specific expertise. Presently, IBP comprises ready-to-use information and tools for over 10 crops, including diagnostic markers and trait dictionaries.

The advance in sequencing technologies is an advantage for efficient sequencing of large collections that include poorly studied species in genebanks with larger analytical power than the conventional molecular marker systems. Diversity assessments per se have huge utility in terms of germplasm utilization, such as definition of heterotic groups that enable breeders to make decisions in planning crosses for the population development. In addition to diversity assessment, NGS-based technologies are likely to impact further analysis of genetic variation, in terms of characterization of functional genetic diversity [148] and can be applied to pre-breeding activities to boost utilization of genetic resources in breeding

NGS can also be applied to enhance management aspects of the genebanks, including identifying duplicates and identification of mislabeled accessions, both of which are common challenges in genebanks [148]. Diversity assessments using NGS could help guide the need for further targeted germplasm collection and improve the development of subsets of the collection, also referred to as core or minicore or diversity research sets, that would

A strong genomics and bioinformatics platform will greatly facilitate essential elements of genebank management, particularly the verification of accession identity, characterization of duplicates in the collection, and diversity analysis. Furthermore, rapid genotyping methods (e.g., GBS and WGS) will be essential for allele mining and large-scale associa‐ tion of genotype–phenotype, which are taken together with methods of developing traitspecific subsets, also referred to as core or mini core or diversity research sets, to greatly enhance the value of the collections for breeding and research. In particular gene pool, enhancement (pre-breeding) will be strengthened in terms of both base broadening within a species and use of crop wild relatives for the integration of key traits. Such approaches can be applied not only to staple crops but also to obtain rapid advances in the improve‐ ment of underutilized and under-researched but important crops such as cocoyam, winged

The adoption of new Omics technologies by breeding programs in developing countries can contribute to the enhancement of breeding efficiency. There is a growing effort to harness advances in bio-computational methods and information and communication technology (ICT) to successfully utilize diverse phenotypic, environmental, genomic, and other metadata to provide decision support tools at various stages of the breeding pipeline. Modern breeding schemes such as GS and MAS involve a deluge of genotype data such as GBS-derived SNP markers, advanced statistical analysis to compute GEBV, and large amounts of high-throughput phenotype information, all of which require efficient informat‐ ics tools, automated data analysis pipelines, and decision-making tools for analysis and integration. Efficient utilization of such unprecedented volumes of genotypic, phenotypic, and other data entails development of informatics, database, and decision support tools.

further improve the efficient utilization of germplasm for cultivar development.

programs [29,52,147].

302 Next Generation Sequencing - Advances, Applications and Challenges

bean, and African yam bean.

**3.3. Breeding data management**

In today's Omics era, web-based, peer-reviewed molecular databases and web servers abound [149]. An annual issue of the journal "Nucleic Acid Research" is dedicated to databases and web servers and documents a wide spectrum of databases, including a substantial number on plant databases. A comprehensive list of genomic resources (platforms and databases) relevant to genomics-enabled crop improvement, including genome sequences of crop plants, has been published recently [12]. Table 2 provides a partial list of deployed or planned breedingrelevant technology and tools currently in use. The Kazusa marker database [150] features genomics and genetics information for 10 plant species, whereas SolGenomics is a portal for several solanaceous plant species [130]. These and other breeders' toolboxes such as Soybase and MaizeGDB can serve as a starting point for comparative analysis of orphan crops with limited genomic resources.

Developments of several other similar and complementary custom-made breeding toolboxes are underway in various projects implemented in developing countries. A concerted effort by multidisciplinary teams, galvanized by various consortium research programs (CRPs), including national programs, are diligently working on development of pipelines for connect‐ ing diverse types of data to appropriate analytical tools and for processing imaging and remote sensing phenotype data.

The multidisciplinary nature of modern plant breeding/genetic research is underpinned by acquisition, analysis, and utilization of "big data" not only from field trials but also from laboratory analyses. Laboratory analysis includes analytical chemistry for profiling nutritional content and other metabolites, which entails efficient data management system. Moreover, high-density genome-wide marker data generated from next-generation sequencing for marker–trait associations as well as whole-genome expression profiling are increasingly being utilized for crop improvement pipelines. A comprehensive open-access database comprising phenotype and marker data, trial design, and analysis pipeline is a must-have to aid in streamlined integration of various data from plant breeding, including phenotypes recorded from field trials; genotypic data, gene expression, and analytical chemistry requires reliable and user-friendly database. Such a database must also have inbuilt quantitative genetics analysis tools/pipelines that would allow breeders to not only store and retrieve raw data but also calculate breeding values and selection index, design crosses, as well as field trials. Moreover, discovery research such as QTL mapping can be done on the database through implementation of genetic mapping methods.

\*BMS, hosted by IITA as a regional hub for integrated breeding platform (IBP), is a suite of interconnected software specifically designed to help breeders manage their day-to-day activities through all phases of their breeding programs.

Note: Other CGIAR-driven initiatives include Genomic and Open-source Breeding Informatics Initiative (GOBII), Integrated Genotyping Service and Support at Biosciences eastern and central Africa (BECA)/International Livestock Research Institute (ILRI), and Shared Indus‐ trial-Scale High-Throughput Genotyping Facility for delivering high-density genomics breeder's tools and low-cost genotyping services.

**Table 2.** Partial list of crop- or project-specific databases and breeder's toolboxes relevant to breeders in developing nations that are in use or in progress.


**THE TRANSMISSION ELECTRON MICROSCOPE**

Edited by **Maaz Khan**

http://dx.doi.org/10.5772/1977 Edited by Khan Maaz

#### **Contributors**

Yekaterina Bedoshvili, Yelena Likhoshway, Lukasz Major, Juergen M. Lackner, Jerzy Morgiel, Nora Elizondo-Villarreal, Ricardo Obregon-Guerra, Sergio Belmares-Perales, Manuel Garcia-Mendez, Arquimedes Cruz-Lopez, Odilon Vazquez-Cuchillo, Antonio A. Zaldivar-Cadena, Donald Homero Galvan-Martinez, Lorena Alvarez-Contreras, Ran Tel-Vered, Shokrollah Hajivand, Lanzhuang Chen, Liming Guan, Jiandong Lin, Mingrui Lin, Naoki Yamamoto, Wei (Kelvii) Guo, Magdalena Bieda-Niemiec, Krzysztof Maciej Sztwiertnia, Anna Korneva, Jakub Kawałko, Lawrence Selvaraj, Takashi Egawa, Zhe Wang, Chengji Zhao, Hongzhe Ni, Rajiv Kumar Mandal, Fernanda Elena Monasterio, Takashi Sumigawa, Takayuki Kitamura, James Evans, Saeed Shokri, Robert John Aitken, Masoud Hemadi, Katsuhiro Sasaki, Hidekazu Murata, Kotaro Kuroda, Hiroyasu Saka, Abhijit Kar, Ajoy Kumar Ray, Jian Li, Hiroo Omi

#### **© The Editor(s) and the Author(s) 2012**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2012 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
The Transmission Electron Microscope Edited by Khan Maaz p. cm. ISBN 978-953-51-0450-6 eBook (PDF) ISBN 978-953-51-6192-9