Most of the back-end software used by the server is open source and freely available to the public. This feature is also highly valued by viewers. The reason is not only reduced cost but also the openness of science that could be performed with open-source software. We consider such open access as critical in the field of molecular modeling: it allows researchers to use the program codes and develop them further, thus advancing the development of better modeling methods.

The main engine room of the server is the GLYCAM force field, the outcome of more than 20 years of development. Its latest incarnation, GLYCAM06 (7), has been significantly modified to satisfy the following requirements: transferability of the parameter set to all carbohydrate ring conformations and sizes, carbohydrate derivatives, and other biomolecules; self-containment and transferability to many quadratic force fields; ability to treat both α- and β-anomers without specific atom types. The GLYCAM-Web server (5) is a very widely and extensively used resource. Its statistics records show that an average of 1700–1800 unique users access the site monthly (data examined: December 2014–March 2015).

The GLYCAM-Web server (5) and the GLYCAM force field (7) were used for the investigation of recognition specificity of ABO blood group antigens by antibodies (8). Such circulating antibodies could cause a hyperacute immune response and sometimes death resulting from mismatched blood transfusion or organ transplantation (9). Another recent example of application of GLYCAM was in the study of structure and immune recognition of HIV-1 envelope (10), where it was used for modeling the *N*-linked glycan shield.

UniCarb KnowledgeBase (11, 12) was conceived to support online data storage and search capabilities for glycomics and glycobiology by integrating structural, experimental, and functional information. The aim of UniCarbKB is to advance the understanding of structures, pathways, and networks involved in glycosylation and processes mediated by carbohydrates. This information-rich resource is freely accessible, supports data annotation, and promotes adoption of common standards, necessary for seamless and meaningful integration of structural and functional data. UniCarbKB is the outcome of the collaborative effort of researchers from Australia (Macquarie University and University of NSW), Germany (Max Planck Institute of Colloids and Interfaces), Ireland (National Institute for Bioprocessing Research and Training), Japan (Soka University), Sweden (University of Gothenburg), Switzerland (Swiss Institute for Bioinformatics and University of Geneva), and USA (CCRC).

UniCarb KnowledgeBase provides querying interfaces for three-structural databases (GlycoSuiteDB, EUROCarbDB, and GlycoBase) and a link to the UniCarb-DB (13). UniCarbKB continues efforts started with GlycoSuiteDB (14) in curating structural glycobiology information from research literature. GlycoBase (15) and UniCarb-DB (16) are databases of experimental glycan structures. Support for EUROCarbDB (17) is planned for the future release.

At the time of writing, over 906 references, 3238 glycan structure entries, and 898 glycoproteins have been curated in Uni-CarbKB. A total of 598 protein glycosylation sites have been annotated using experimentally confirmed glycan structures. Beyond storing, curating, and providing searchable access to structural and functional data, UniCarbKB offers a range of tools for data pre-process and analysis: GlycanBuilder (18, 19) – for fast and intuitive drawing of glycan structures; GlycoMod tool (20, 21) – for predicting the possible glycan structures on proteins, from their experimentally determined masses. Uni-CarbKB is connected to the GLYCAM site (described above) and RINGS – a web resource offering algorithms and data mining tools for glycobiology research (22). Further, through its SugarBindDB module, UniCarbKB is linked to the Functional Glycomics Gateway of the Consortium for Functional Glycomics (CFG) (23) and the Glyco3D Portal for Structural Glycosciences (24, 25).

The GlycoMod tool of the UniCarbKB was used in a study of the formation of subcellular-specific *N*-glycosylation glycoprotein determinants (26). Lee et al. used liquid chromatography and mass spectrometry-based quantitative glycomics to investigate eight human breast epithelial cells with diverse genotypes and phenotypes – mostly human breast cancer cell lines – and have shown that the secreted glycoproteins consistently displayed more processed, primarily complex type, *N*-glycans than the high-mannose-rich microsomal glycoproteins. They have also demonstrated that secreted glycoproteins displayed significantly more α-sialylation and α-1,6-fucosylation, but less α-mannosylation, than both cell-surface and microsomal glycoproteomes.

Of particular interest to those working on the glycobiology of infectious disease is the SugarBindDB module of UniCarbKB – a database of pathogen lectins and corresponding glycan targets (27, 28). The SugarBindDB advances a pathogen-capture technology specifically for the binding of pathogen lectins to carbohydrate epitopes. The contents of the database come from publicly accessible information. After experts (glycobiologists, microbiologists, and medical histologists) locate candidate papers, the binding data is extracted for human pathogens and specific glycans.

The SugarBindDB database can be searched using names of different entities involved in the carbohydrate-protein interactions. For pathogenic agents (e.g., *Pseudomonas aeruginosa*), the outcomes include agent type and taxonomy ID. For lectins themselves (e.g., PA-IIL), the database stores genes, Protein Data Bank (PDB) codes, and associated Glyco3D and CFG references (see below for more information about the Glyco3D resources). A query could also be constructed for specific ligands (e.g., Lewis X). The entries in SugarBindDB are limited to sugar sequences that demonstrate consistent binding to pathogen lectins. Sugar sequences include "glycotope" within a larger carbohydrate structure (the precise lectin-binding site) or entire structures, depending on specific findings within the cited papers. Similar to the GLYCAM-Web server mentioned above, the SugarBindDB is user-friendly in that it allows displaying glycans in a variety of formats; specifically, it supports the modified 2D condensed IUPAC nomenclature, Oxford symbol notation, and the colored CFG cartoons. A sugar query can be input as a structure drawn with GlycanBuilder (18, 19). The SugarBindDB database can also be browsed by disease (e.g., Influenza) or by published references (by author or title descriptions) or searched by the affected area in the pathology (e.g., intestine). Search outputs are very informatively interconnected, i.e., the biological context of each ligand is shown in relation to a disease, to other known ligands, to similar lectins, etc. At the time of writing, over 178 references, 549 pathogenic agents, and 200 ligands have been curated in SugarBindDB.

Glyco3D (25) was developed at the Centre de Recherches sur les Macromolécules Végétales (CERMAV-CNRS), France. It comprises a group of databases containing the 3D data collected from an extensive screening of scientific literature: BiOligo (more than 250 bioactive oligosaccharides along with about 120 of their constituting disaccharide and about 80 monosaccharide segments); PolySac3DB (157 polysaccharides); Lectin3D (more than 1,000 structures); GAG3D (GAG-binding proteins co-crystallized with their ligands); MAbs (a limited set of high resolution structures of carbohydrate–antibody complexes); GT3D (glycosyltransferases crystallized with or without their carbohydrate ligands). Lectins are oligomeric proteins, which can specifically recognize carbohydrates and are involved in many processes crucial to infection and immunity (29); ~70% of the Lectin3D structures are present in complex with a carbohydrate ligand, from monosaccharides to oligosaccharides or glycoproteins, allowing great insight into the molecular basis of carbohydrate recognition by lectins. GAGs are complex anionic polysaccharides (e.g., heparin), specifically recognized by protein receptors, which participate in the regulation of many processes, including cell adhesion. Anticarbohydrate antibodies, demonstrating specificity to various carbohydrate epitopes, are of a high importance in immunology and vaccine development (30).

The individual databases within Glyco3D are publically accessible via the portal (24), furnished with a user-friendly graphical user interface with several search options. All 3D structures, established using a variety of structure determination methods, can be visualized and some basic measurements are possible on the spot. For a more detailed analysis, the structures can be downloaded in a variety of commonly used molecular file formats.

Why would a user access 3D structural information relevant to glycobiology via databases such as Glyco3D while it can be obtained from the original sources, mainly PDB (31)? The answer is: in databases, such as Glyco3D, the data is manually curated and value-added. Such curation and value-adding is indispensable, given poor quality of structural information for carbohydrates in PDB. Agirre et al. have recently demonstrated that 64% of all *N*-glycan -pyranosides in PDB reflect poor fit to the electron density (32). They have revealed significant structural errors, such as incorrect conformations, stereochemical configurations, and linkages, resulting from incorrect model building from the start, as well as more subtle errors, resulting from incorrect refinement. Even subtle errors could be detrimental if incorrect structures are interpreted in a biological context. The reasons for errors are various, but the main one seems to be poor chemical understanding on the part of crystallographers and the lack of appropriate torsional restraints, often in disagreement with the low-resolution data. The detriment of such errors goes beyond incorrect interpretations in specific cases. It also leads to skewed statistics based on all published/deposited structures, creating new abnormal "norms." Thus, structural glycobiology is now ripe for remediation of the type that the protein crystallography field underwent in not too distant past (33).

Tools for structural validation of carbohydrate 3D structural data are available, have been developed specifically for carbohydrates, and are publically accessible (34). For example, pdb-care (35) can check residue notation (which can be a source of confusion for carbohydrate residues) and atom connectivities and evaluate biological correctness of glycan structures. CARP (carbohydrate Ramachandran plot) (36) analyses carbohydrate data by generating a separate Phi/Psi plot for each linkage type, such as in the study of conformational preferences of *Shigella flexneri* O-antigens and their implications for vaccine design (37). Information necessary for such plots can be obtained from the GlyTorsion database (36), containing glycosidic torsions observed in PDB entries, or from conformational maps based on molecular dynamics simulations provided by GlycoMapsDB (38). Both pdbcare and CARP are available at the Glycosciences.de portal (39).

To summarize, the online resources available for structural glycobiology reviewed here demonstrate good scientific citizenship features: multinational collaborative approach to design and development, open access to data and code, and the drive for common and accepted standards. The collaborative approach is illustrated not only by initiatives such as UniCarbKB, but also by efforts to cross-link and integrate existing resources (e.g., SugarBindDB and Glyco3D) and map functional and structural information (e.g., mapping of SugarBindDB to GlycoSuiteDB to link saccharide sequences with their physical location in the body). Further, somewhat altruistic, aspect of these developments is the extensive effort that goes into manual curation of data. Efforts like these should be recognized, credited, and supported more strongly by relevant scientific societies and funding bodies.

### **Acknowledgments**

The authors gratefully acknowledge the contribution toward this study from the Victorian Operational Infrastructure Support Program received by the Burnet Institute.

### **References**

a global glycan reference MS/MS repository. *Biochim Biophys Acta* (2014) **1844**:108–16. doi:10.1016/j.bbapap.2013.04.018

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Yuriev and Ramsland. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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**3. Social media tools**

Social media platforms make it possible to create and share content across virtual communities and networks with the goal of transforming communication into interactive discourse. There are 13 subtypes, including blogs, microblogs, social networking tools for business and networking, collaborative projects, forums, photo-sharing tools for business, product and service evaluations, research networks, social games, and virtual worlds [6]. They can be accessed from many devices and locations around the clock, 7 days a week. They are therefore solely dependent on the availability of the internet and the users' will. They have an impact on politics, the economy, science, and the educational system. There are many divisions of social media, and new subtypes are possible every day. The following typology, used according to Aichner and Jacob in 2015 [6], defines the scope and applicability or use of the proposed models. **Table 1** lists the types of social media with associated descriptions.

As a result of the emerging globalizing environment, various social, economic, cultural, and demographic changes require a long-term reflection on the position of education and science in society, especially because the capital of human knowledge in modern societies means an advantage over financial capital. Changes in that globalized world, thanks to the development of new technologies, are fast and difficult to predict, and education can play its role in the triangle of knowledge; lifelong learning, science, and innovation, to be fulfilled more permanently if the results of research and innovation have an adequate impact on educational processes. Consequently, educational environments must be improved by encouraging creative thinking and innovative action [7], and social media cannot be overlooked or neglected in this process. The following will explain the possible purpose of using social media for higher education.

**Table 1.**

*Description of social media [3, 6].*


*Edited by Florin-Constantin Mihai*

The book points out that rural regions need proper attention at the global level concerning solid waste management sector where bad practices and public health threats could be avoided through traditional and integrated waste management routes. Solid waste management in rural areas is a key issue in developing and transitioning countries due to the lack of proper waste management facilities and services. The book further examines, on the one hand, the main challenges in the development of reliable waste management practices across rural regions and, on the other hand, the concrete solutions and the new opportunities across the world in dealing with municipal and agricultural wastes. The book provides useful information for academics, various professionals, the members of civil society, and national and local authorities.

ISBN 978-953-51-3485-5

Solid Waste Management in Rural Areas

Photo by artursfoto / iStock

## Solid Waste Management in Rural Areas

*Edited by Florin-Constantin Mihai*

## **SOLID WASTE MANAGEMENT IN RURAL AREAS**

Edited by **Florin-Constantin Mihai**

#### **Solid Waste Management in Rural Areas**

http://dx.doi.org/10.5772/66551 Edited by Florin-Constantin Mihai

#### **Contributors**

Qiuyan Yuan, Marco Nuti, María Cristina Echeverria, Elisa Pellegrino, Petra Schneider, Anh Hung Le, Jan Šembera, Rodolfo Daniel Silva-Martínez, Christia Meidiana, Harnenti Afni Yakin, Wawargita Permata Wijayanti, Cácio Boechat, Adriana Arauco, Rose Duda, Antonny Sena, Manoel Souza, Ana Brito, Iria Villar, David Alves, Salustiano Mato, Xosé Manuel Romero, Bernardo Varela, Sandro Xavier De Campos, Rosimara Zittel, Luciléia Granhen Tavares Colares, Karine Marcondes Da Cunha, Tatiana Windékpè KOURA, Valentin Missiakô Kindomihou, Gustave Dieudonné DABENONBAKIN, Brice Augustin SINSIN, Florin-Constantin Mihai, Mohammad Taherzadeh

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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, 2017 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

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**1. Introduction**

A pure environment gives a quality of life on earth. In ancient times, it was believed that people on earth had an unlimited abundance of land and resources; today, however, the resources in the world show a greater or lesser degree of our carelessness and negligence in using them. In many parts of globe, the problems associated with contaminated sites are now growing up. The actual cause of this scenario is result from past industrial activities when awareness of the health and environmental effects connected with the production, use, and disposal of hazard‐ ous substances were less well recognized than today. It became a global complication when the estimated number of contaminated sites became significant. There are several traditional methods which have been applied to overcome this inconvenience. From the list of ideas which have been applied the best ones are to completely demolish the pollutants if possible, or at least to transform them to innoxious substances. Bioremediation is an option that utilizes microbes to remove many contaminants from the environment by a diversity of enzymatic processes. However, it will not always be suitable as the range of contaminants on which it is effective is limited, the time scales involved are relatively long, and the residual contaminant levels achievable may not always be appropriate. we attempted to assist by providing information how the bioremediation is linked with cutting edge sciences like genomics, transcriptomics, proteomics, interactomics and bioinformatics.

Some new techniques in molecular biology particularly genetic engineering, transcriptom‐ ics, proteomics and interactomics offer remarkable promise as tools to study the mecha‐ nisms involved in regulation of mineralization pathways. The strategies need to be refined in which transcriptomics and proteomics data are combined together in order to understand the mineralization process in a meaningful way.These techniques show great promise in their ability to predict organisms' metabolism in contaminated environments and to predict the microbial assisted attenuation of contaminants to accelerate bioremediation. Bioinfor‐ matics technology has been developed to identify and analyse various components of cells

© 2013 Kumavath and Devarapalli; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

such as gene and protein functions, interactions, metabolic and regulatory pathways. Bioin‐ formatics analysis will facilitate and quicken the analysis of cellular process to understand the cellular mechanism to treat and control microbial cells as factories. The next decade will belong to understanding molecular mechanism and cellular manipulation using the integra‐ tion of bioinformatics.

the factors that control the rate and extent of bioremediation. Sub-optimal nutrient levels, pH, salinity and other environmental factors can limit the growth and metabolism of organisms that are involved in bioremediation in contaminated environments. Ecological studies of phyto-plankton use molecular techniques to evaluate the stress response of photosynthetic microorganisms in the environment (Palenik and Wood. 1998). In a similar manner, evaluation of the metabolic state of bioremediating microorganisms through analysis of the mRNA concentrations for key genes that are involved in responding to stress could help to identify

Scientific Swift in Bioremediation: An Overview

http://dx.doi.org/10.5772/56409

377

The subset of genes transcribed in any given organism is called the transcriptome, which is a dynamic link between the genome, the proteome and the cellular phenotype. The regulation of gene expression is one of the key processes for adapting to changes in environmental conditions and thus for survival. Transcriptomics describes this process in a genome wide range. DNA microarrays are an extremely powerful platform in transcriptomics that enable determination of the mRNA expression level of practically every gene of an organism (Schena et al. 1998., Golyshin et al. 2003., Diaz. 2004) The most challenging issue in microarray experiments is elucidation of data (Dharmadi and Gonzalez. 2004). Often, hundreds of genes may be up- and/or down-regulated in a particular stress condition. In this context, several statistical issues become tremendously complex, including accounting for random and

Even with the complete genome sequences of microorganisms with the potential for bioremediation (Golyshin et al. 2003., Tiedje. 2002., Heidelberg et al. 2002., Seshadri et al. 2005., Rabus et al. 2005), studies are not accelerating in a rapid manner. With the complet‐ ed genome sequences, it is possible to analyse the expression of all genes in each ge‐ nome under various environmental conditions using whole-genome DNA microarrays (Gao et al. 2004., Muffler et al. 2002., Schut et al. 2003). Such genome-wide expression analysis provides important data for identifying regulatory circuits in these organisms (Lovley. 2003., Rabus et al. 2005., Muffler et al. 2002). In the past, DNA microarrays have been used to evaluate the physiology of pure environmental cultures (Schut et al. 2003) and to monitor the catabolic gene expression profile in mixed microbial communities (Dennis et al. 2003). More than 100 genes were found to be affected by oxygen-limiting conditions when a DNA microarray was used to study changes in mRNA expression levels in Bacillus subtilis grown under anaerobic conditions (Ye et al. 2000). Sensitivity may often be a part of the problem in PCR-based cDNA microarrays, since only genes from popula‐ tions contributing to more than 5% of the community DNA can be detected. Several parameters were evaluated to validate the sensitivity of spotted oligonucleotide DNA

modifications to contaminated environments that might promote bioremediation.

**3. Role of transcriptomics**

systematic errors and performing poor analysis.

**4. Applications of DNA microarray**

Bioremediation is an option that utilizes microbes to remove many contaminants from the environment by a diversity of enzymatic processes. The major positive shades of bioremedia‐ tion are comparatively low-cost and techniques based on low-technology (Robb et al, 1995) which generally have a high public acceptance and can often be carried out on site. However, it will not always be suitable as the range of contaminants on which it is effective is limited, the time scales involved are relatively long, and the residual contaminant levels achievable may not always be appropriate. Varying degrees of success bioremediation has been used at a number of sites worldwide (Ajay et al, 2009). Here, we attempted to assist by providing information how the bioremediation is linked with cutting edge sciences like genomics, transcriptomics, proteomics, interactomics and bioinformatics (Fleming et al. 1993., Schena et al. 1998., Sikkema et al. 1995., Kuhner et al. 2005., Ellis et al. 2000).