Structure of technological chart.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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He is currently a senior research associate at Department of Automatics, Biocybernetics, and Robotics at Jožef Stefan Institute and an assistant professor at the Jožef Stefan International Postgraduate School. He was a postdoctoral researcher with SCIEX NMS-CH fellowship at the Biorobotics Laboratory, EPFL, Switzerland, and a visiting researcher at the ATR Computational Neuroscience Laboratories in Japan.\nHis research interests are at the intersection of humanoid and applicative industrial robotics, specifically in robot learning in general, learning by imitation, compliance and adaptation of robot motion and cooperative human-robot operation. He has authored or co-authored 20-some journal papers and over 100 conference papers.\nHe is an associate editor for International Journal of Advanced Robotic Systems, where he has been also a guest editor, associate editor for Frontiers in Neurorobotics, and has been a member of several conference editorial boards, including ICRA, IROS and Humanoids.\nDr. Gams received the ICRA CEB Best Reviewer Award at the 2015 IEEE International Conference on Robotics and Automation in Seattle, USA, Best Scientific Paper award at the 23rd International Workshop on Robotics in Alpe-Adria-Danube Region, Smolenice, Slovakia (2014), Best Paper Research Award at the 19th International Workshop in the Alpe-Adria-Danube region in Budapest, Hungary (2010) and the Jožef Stefan Golden Emblem Award for his Ph.D. dissertation in 2012.",institutionString:"Jožef Stefan Institute",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Jožef Stefan Institute",institutionURL:null,country:{name:"Slovenia"}}}],coeditorOne:{id:"315335",title:"Dr.",name:"Takamitsu",middleName:null,surname:"Matsubara",slug:"takamitsu-matsubara",fullName:"Takamitsu Matsubara",profilePictureURL:"https://mts.intechopen.com/storage/users/315335/images/system/315335.jpg",biography:"Takamitsu Matsubara received his B.E. in electrical and electronic systems engineering from Osaka Prefecture University, Osaka, Japan, in 2003, and M.E. in information science from the Nara Institute of Science and Technology, Nara, Japan, in 2005, and a Ph.D. in information science from the Nara Institute of Science and Technology, Nara, Japan, in 2007. From 2005 to 2007, he was a research fellow (DC1) of the Japan Society for the Promotion of Science. From 2013 to 2014, he was a visiting researcher of the Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands. \nHe is currently an associate professor at the Nara Institute of Science and Technology and a visiting researcher at the ATR Computational Neuroscience Laboratories, Kyoto, and at National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. \nHis research interests are machine learning and control theory for robotics. He has authored or co-authored over 20 journal papers and 60 conference papers. \nHe has been a guest editor for Advanced Robotics, and has been a member of several conference editorial boards, including ICRA, IROS, Humanoids and CoRL. \nDr. Matsubara received RSJ/KROS Distinguished Interdisciplinary Research Award at the 2015 IEEE RO-MAN, Best Oral Paper Award at the IEEE-RAS Humanoids2016, JNNS the Best Paper Award (2012, and 2018).",institutionString:"Nara Institute of Science and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Nara Institute of Science and Technology",institutionURL:null,country:{name:"Japan"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"22",title:"Robotics",slug:"physical-sciences-engineering-and-technology-robotics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"872",title:"Organic Pollutants Ten Years After the Stockholm Convention",subtitle:"Environmental and Analytical Update",isOpenForSubmission:!1,hash:"f01dc7077e1d23f3d8f5454985cafa0a",slug:"organic-pollutants-ten-years-after-the-stockholm-convention-environmental-and-analytical-update",bookSignature:"Tomasz Puzyn and Aleksandra Mostrag-Szlichtyng",coverURL:"https://cdn.intechopen.com/books/images_new/872.jpg",editedByType:"Edited by",editors:[{id:"84887",title:"Dr.",name:"Tomasz",surname:"Puzyn",slug:"tomasz-puzyn",fullName:"Tomasz Puzyn"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53972",title:"Planning and Management Tasks of Innovation Projects in Production and Economic Systems",doi:"10.5772/67260",slug:"planning-and-management-tasks-of-innovation-projects-in-production-and-economic-systems",body:'The development of modern production and economics is generally based on newfound knowledge and scientific achievements that are integrated into technologies and products.
At the same time, companies are very responsive to any fluctuations in the market and consumer preferences. The situation on the market changes very rapidly, and the number of competitive products is vast. The companies have to launch new developments to catch up with modern trends, satisfy consumer preferences, and create new market segments. They should also bear in mind that product life time reduces as a result of increasing modifications and improvements in production and economic systems (PES). So, management sector that deals with innovation projects, especially in small-scale companies, should always make decisions promptly and ensure high quality in their products. In real project and PES management, we encounter an increasing number of statistical data, lack of universal algorithms, and the sufficient software to operate them, and besides, there are problems for which the current solutions are insufficient. It is difficult to describe the relationship among projects, PES, and the environment; therefore, live data are essential for effective management in the decision-making process.
The problem of management decision-making in PES projects was initially taken up by Albert-Kalmes, as a problem of inventory and statistics in factories and commodity production; later on, Frederick Taylor and Henry Gantt laid the foundations for the methods of planning [1]. Mathematical calculations in this area are connected with system analysis (introduced by J. Von Neiman and L.V. Kantorovich [2]). Economical aspects are considered in this approach in terms of pricing, production planning, sequence scheduling, fixed price and time-and-materials cost, and procurement management. These aspects are dependent on market segmentation and internal structure of PES, well known as Wagner-Whitin algorithm that relates these aspects with market. The idea to integrate market selection and production planning was introduced in Ref. [3] and up till now, this problem has not been solved. It is NP-complete problem that can be solved only if we fix separate factors (in particular, Jean Tirole has successfully solved management task for markets segmentation).
In 1995, Pepall introduced Game theory [4] to describe duplicates and innovations; this theory considers the relationship among projects. Such an approach triggered change management. Today, this idea is used in innovation management in terms of agent simulation and forecasting.
In the 1950s, mathematical and algorithmic calculations helped experts work out methods of planning and management known as Just-in-time; this method still enjoys high popularity. Main achievements in the late 1960s are connected with the works of Oliver White, who suggested that production, supply, and sale departments can be considered altogether in automated industrial enterprises. In his publications and in the periodicals of American Production and Inventory Control Society (APICS), we can find the algorithms of planning, which are known today as MRP. In the 1970s, Eli Goldratt in Israel worked out the method OPT. The modification of the algorithms of planning MRP~II was considered as the final achievement of all these methods till the beginning of the 1980s. The idea of computer-based integrated production CIM appeared in the first part of the 1980s, due to the integration of flexible manufacturing and efficient management. The US Department of Defence introduced, in the 1980s, CALS methods to ensure that all operations with orders, production development and organization, supplies, and operation of military technique were efficiently managed and planned. In beginning of the 20th century, the ideas of intellectual enterprise were introduced [5]; at that time, multiagent systems that were developed to consider such factors as autonomy, external factors’ dependency, flexibility, proactivity, social factor, and efficient intellectual management factor were not studied thoroughly enough to use them in complex information systems. In such an approach, it is difficult to ensure effective interaction among the parts of PES at an industrial enterprise. In particular, D.A. Novikov contributed greatly to the development of this area with his theory of active systems. This issue was also addressed by R.K. Sah and J. Stiglitz who proved the necessity of building complex structures in collective decision-making, for instance in organizational hierarchies.
The development of conceptual bases in management and simulation plays a significant role in PES and project management as it converts knowledge from object perspective into action [6].
Yu.A. Zelenkov introduced tuple description of goals and current possibilities of PES projects:
where
Within project approach in PES, it is recommended to use general purpose tuple [7]:
where
Sufficient management [8] requires formalized description of tuple parts (resulting in a lower degree of ambiguity).
In management, we should take into account that project and system develop over time and affect multiple PES. Therefore, it makes sense to consider the models for different points of time, levels, management types, and project stages that lead to necessity to study project identification and define decision points.
Such a task can be illustrated by determining managerial vector parameters
Scheme of current project state based on stages of project indicators, types, and levels of management.
By the set of parameters, decision points can be defined by PES data (equipment service intervals and internal technology cycles, etc.), statistical data, or forecasting data that describe a project or projects (the parameters of sales volume and price change, etc., are presented by innovation curves), see Figure 2.
Decision points in PES projects.
As a result, each decision point will be given a model that altogether will form a tuple
This way to form a tuple,
Hence, project time management can be reduced to tuples formalization
Project management process model at one of PES stages.
Forecasting tasks and the description of time series are studied by many authors, and there are many methods to solve this problem (mathematical regression model, functional description of parameters by innovation, and S-curves).
For mathematical formalization, we can refer to the scheme illustrated in the Figure 4.
Optimization task structure in production planning.
Formalization can be presented by selecting most efficient way to developing market segmentation when same project (a development way) can enter different markets (particularly, B2N, and B2C). This task has the following mathematical formula [10]:
where
The selection of PES where this project will be implemented is another example (project can be transferred for implementation to existing PES or can be implemented independently by creating new legal entities). Based on PES tasks, we can deal with the task of return maximization from project implementation or handle the task of reducing production time. Therefore, we obtain two models.
First model is built for mitigating the production cost:
where
Second model is designed for mitigating time required. We need to note that certain operations can be performed simultaneously (see Figure 5).
Example of operation sequences by project implementation in PES [
The model for mitigating the time required can be given as follows:
where
In PES, project management is aimed at return optimization [11] via portfolio selection for goods. At the same time, not all the economically justified goods can be produced at each technological enterprise.
In order to cover these particular features, we need to give sound suggestions based on the set of criteria. For instance, criteria function and limitations will be given for volume scheduling of production planning as follows:
where
Otherwise, criteria function can be written the following way:
by
The amount of costs
If it is necessary to consider these costs and the time required Eq. (6), we can complement the model with respective criteria of type Eq. (5).
The costs are specified by technological charts of product
Operation number | Operation name | Operation cost | Previous operations | Subsequent operations | Performance time | Need in parts | Need in materials | Need in equipment |
---|---|---|---|---|---|---|---|---|
|
Structure of technological chart.
Forecast data are used for market conditions. Therefore, value scheduling parameters are
As a result, we receive a portfolio and product release schedule that stipulate the amount of expected return from one product and accumulated effect from a released group of products.
This way of formalization helps distinguish projects in terms of specific features of PES and expected change of market conditions or other critical project parameters that were used in criteria function.
However, we must emphasize that on each stage critical for the task to be tackled (time sequence is restricted by decision points), we might need multiple solution of the problem as a number of parameters is determined by forecast data, and the situation can be changed over the time.
Therefore, the problem can be solved by making a table with time function. Due to lack of analytical methods that can be used nowadays to solve obtained tasks, we suggest use multiple cyclic numerical solution with time period
Received solutions and time sequence selection can require additional research though, as we can encounter periodical change of production volume that leads to additional expenses for preparation and/or modification of production system.
Despite all the advantages of mathematical programming, in general, it is not easily solved (especially in case of multiple criteria). Such tasks are considered as NP-complete problems (for instance, for market segmentation task [3]). Due to forecast errors, complex tasks obtained by mathematical programming can be solved by approximate methods. That is why it is very important to study sensitivity of gained solutions to the level of market and PES parameters’ deviation and take into account production and planning risk evaluation; the stipulated parameters can have Markov property (Markov process) and can be designed by Monte-Carlo method.
Due to considerable restrictions nowadays, we can take advantage of other ways to formalize such groups of tasks. A vast amount of Nobel laureates focus on this problem (L.V. Kantorovich, 1975; R. Solow, 1987; H. Markowitz, 1994; J. Stiglitz, 2001; J. Tirole, 2014). Besides, management and sufficient formalization principles in management and applications greatly contribute to existing approaches and theories.
The analysis of gained results plays a significant role in managerial decision-making. Many authors make big efforts to tackle tasks with risk analysis of segregate solutions. For example, for project portfolio risk evaluation, we can use capital asset pricing model (CAMP) introduced by Sharpe [13], Lintner [14], and Mossin [15] based on the theory of Markowitz described in Refs. [16, 17]. For risk evaluation, we can also use the approach covered in Ref. [18], when we use function-based parameters obtained by forecast margins.
Over-time consideration of parameters makes it possible to mitigate risks associated with the selection of innovation projects (managerial and organizational), for which membership function may be identified for every moment of time.
In this case, the expected return of product portfolio can be determined as is evident from:
where
Correlation factor can be calculated by the formula:
Then the risk evaluation for
Forecast data can be calculated by the formula [19]:
and
where
For retrospective data:
where
Correlation ration can be defined upon statistical data (Table 2) and Slope One algorithm [20].
Period (day, month, quarter, year, …) | Product 1 | Product 2 | | Product | | Product |
---|---|---|---|---|---|---|
1 | ||||||
| | | | | | |
Sales volume matrix.
In this case, the angle cosine between vectors formed by columns of values for each product (item-to-item algorithm) can be used as the correlation factor:
where
These calculations can be subsequently used to fill in the product correspondence table.
In this case, portfolio will be chosen on the assumption that the expected return can be also determined by the following formula [21]:
where
When we use forecasts in decision making, we face risks on whether the forecasts are reliable and how the result will guarantee the quality of decision-making. Forecasting can be performed only by a certain degree of assurance; however, long-term forecasts produce low degree of accuracy (the intervals of potential deviations will increase). The magnitude of deviations can be calculated for normal distribution parameters based on the maximum margin of error when forecast can be regarded as accurate [18]:
where
Let us find
The fuzzy set
Let us assume that a risk is calculated for a certain set of project parameters taking into account all risk factors, i.e., multivariable risk:
where let us say,
Furthermore, let us suppose that risk is measured over a certain risk set:
where
Defined index that is used in this formula should be determined by either expert evaluations or forecast margins generated by diverse methods; all these margins are based on various original data. Hence, we use these data to define margin range of an interested parameter (i.e., risk measured by this method will uniquely be placed in the range, that generates fuzzy set), and membership function is built on Gaussian function (used by the description of normal distribution law).
To define function parameters, let use Gaussian function
where
For the parameters, that do not obey normal distribution law, we can use triangular functions, generic Bell function, and sigmoid function for asymmetric distributions, etc.
Based on membership function and taking into account potential risky events that influence each of the margins, we measure risk evaluation margin [18] for the described parameter value:
where
We should take into consideration that obtained risk evaluations do not consider the risks of previous stages.
Such forecasting risk evaluation can be applied only in the case if we know all the parameter values that we need to assess; that is a disadvantage of this method.
A simulated model can help forecast features and behavior of object of inquiry both inside the area, where the model is simulated, and (by proved application) outside this area (forecasting role of a model); manage the object by selecting most efficient model-based impacts (managerial role); recognize the phenomenon or the object that was used for simulating the model (cognitive role of a model); obtain skills to manage the object by using the model as a training simulator or a game (training role); and enhance the object by modifying and testing the model (project role).
In practice, the stipulated task management in PES helps design simulation models for certain tasks avoiding NP-complete problem (for instance, Wagner-Whitin algorithm); furthermore, the use of sequential stage-to-stage transitions of forecast parameters or production cycles as described in Ref. [7] as crucial points in decision-making helps to avoid infinite-horizon problems [22] and exclude innovative regression in PES introduced by the corresponding member of RAS D.A. Novikov [23].
The described approach for management decision-making helps study PES processes at any accuracy degree. At the same time, the model complies with each management algorithm or system behavior and assesses risk margin for decision-making models.
The author thanks the Government of Perm Krai for the support of the project for “Development of software and economic and mathematical models for supporting innovation project management processes in production systems”, implemented in accordance with decree №166-п of 06.04.2011. The reported study was in part supported by the Government of Perm Krai, research project No. C-26/058.
Certain microorganisms have the unique ability to populate the human gastrointestinal tract and thus generally referred as gut microbiota. Gut microbiota is always non-pathological, and hence, the immune system is not triggered because of their presence. Humans co-evolved with a huge number of intestinal microbial species that offer to the host certain benefits by playing an important role in preventing them from pathogenic activities [1]. In addition to metabolic benefits, symbiotic bacteria benefit the host with various functions like boosting the immune homeostasis and inhibiting the colonization by other pathogenic microorganisms. The ability of symbiotic bacteria to inhibit pathogen colonization particularly in the gut is mediated via several mechanisms including direct killing of pathogen, competition for limited nutrients, and enhancement of immune responses [2]. The intestinal microorganisms also co-evolved and have strong affiliations and association towards each other. In this evolutionary process, the persistent and enduring members of this microflora become more competent during unsettling influences and thereby become essential for human health [3]. Definite composition of human microbiome varies between individuals [4] particularly among lean and obese people. The microbiome is also affected by the dietary modifications adapted for the weight loss [5]. Examination of metabolic profiles of human infant microbiota revealed that ingestion, storage and digestion of dietary lipids were explicitly regulated by the microbiome [6, 7].
\nThe human gut microbial communities are a mixture of microorganisms. The classes of microbes that constitute the gut microbiome communities differ between hosts. The difference is attributed to factors such as, inability of a microorganism to migrate between different hosts, intense environmental conditions inside and outside host’s gut and host inconsistency in terms of genotype, diet, and colonization history [8]. The co-evolution of humans and their symbiotic microorganism has created bilateral interactions which are important for the health of humans, and any genetic or ecological change in this bilateral interaction can result in pathological conditions like infection [8]. Gut microbial communities are important for diverse host functions, including metabolism, fertility, development, immunity, and even antioxidant activities which promote health and fitness of the host [9, 10, 11, 12]. The gut microbiome has a much larger genetic variety compared to the genome of the host, e.g., human genome is comprised of 20-25,000 genes whereas microbiome inhabiting the body is estimated to be in trillions. Almost 1010 microorganisms enter the human body daily and with the progress of co-evolution of gut microbes in humans, the capability of microbes to exchange their genes and associated functions with the environment are some of the main factors leading to host adaptation. Therefore, the “hologenome” model appraises the host and its microbes genomes as one unit under assortment [13, 14]. It is acknowledged that host-symbiont co-evolution is accountable for basic biological aspects. In this chapter we aim to discuss the importance of gut microbiomes as a new organ system because of its association with the genetics and its role in the disease and health condition of the host. Moreover, the involvement of these microbiomes in shaping the overall health and constructing a symbiotic relationship with their host species is discussed as well as the co-evolution of gut microbes with the human body.
\nA microbiome is the community of microbes dwelling collectively in a selected habitat. Humans, animals, vegetation, soils, oceans or even buildings have their own specific microbiome [15].
\nThe human gut environment is extremely complex with a unique ecology which comprises of trillion of microbiota with approximately 1.5 kg in mass. By using genetic techniques like 16S sequencing, 1000 microorganisms have been identified within the gut, with approx. 200 (0.5%) defining the core of the intestine microbiome [16]. These bacteria protect the gut epithelial cells against external pathogens. They also help the breakdown of indigestible dietary polysaccharides in the gut and thus supply a quick chain of fatty acids, including acetate, butyrate, and propionate, which serve as vital metabolites for direct energy source of intestinal epithelial cells, prevention of insulin resistance and modulators of insulin secretion [17] (Figure 1).
\nCore human microbiome.
The genetic makeup of humans is virtually identical, yet the small differences in DNA give rise to remarkable phenotypic assortment across the human population. The trillions of microbes inhabit our bodies and create complex, body-habitat-specific, adaptive ecosystems that are finely tuned to frequently changing host physiology [18]. A healthy “functional core” is actually a complement of metabolic and other molecular functions that are performed by the microbiome within a particular habitat but are not necessarily provided by the same organisms in different people [19].
\nThe gastrointestinal tract (GIT) of humans is colonized by a vast variety of microbial population that can be understood as a complex and polygenetic trait which has been interacting and co-evolved with their host genetic environment [20, 21, 22]. It was previously considered that fetus lives in a germ free environment in the mother womb and the gut microbiota are transferred to the baby from mother’s birth canal and body via horizontal transmission only [23]. But advanced researches have revealed that microbiota are also vertically transmitted to the infants from their mothers [24]. Presence of microbes in the meconium of the babies born by cesarean section clearly demonstrates that the gut microbes are not only derived after the birth [25, 26]. Moreover, presence of many microbes in the umbilical cord blood of the preterm babies and in the amniotic fluid substantiate the findings that the fetus in the mother womb is not totally sterile [27, 28]. Many gut bacterial genera are shared among the mammal species. The microbiomes of mice show strong fidelity throughout the generations and reiterate the intrinsic significance of these microorganisms in health.
\nAs mentioned above human intestinal microbiome composition is shaped by multiple factors like genetics, diet, environment and lifestyle. Several studies point towards stronger contribution by the environmental factors in shaping the gut microbial composition compared to the genetic factor [29]. It has also been speculated that gut microbial diversity affects the prediction accuracy for certain human traits including glucose and obesity problems, as compared to different animal models that use only host genetic and environmental factors [30].
\nHorizontal gene transfer (HGT), genomic and metagenomics are possible approaches to identify drug targets that may also be considered as an evidence of co-evolution of hosts and their symbionts. Symbionts have the capacity to perform many metabolic activities including fermentation of dietary carbohydrates, drug metabolism, antimicrobial protection and immunomodulation, which is primarily due to the presence of genes in their genome which are missing in mammalian genomes. Therefore, horizontal gene transfer mechanisms are potential targets for drug discovery that become more evident with the use of gnotobiotics (germ free animal) in experimental trial to unveil the microbial function in the complex GIT microenvironment, and to investigate how orally administered drugs impact the gut microbial ecology in long term. HGT has gained immense interest in medical field as it contributes to the spreading of antibiotic resistance genes as well as it may cause closely related microbial strains to differ drastically in terms of clinical parameters [31]. Genetic variation in intestinal microbes may trigger the production of metabolites, but it may also generate changes in host’s genome that may increase metabolite uptake or prevent their further synthesis. Co-evolution may lead to co-differentiation since permanent association of host and symbiont lineage can result in diversification [32]. The co-differentiation correlate resemblances in the microbial symbiont and the host [33, 34] which can be extended to an entire microbial community that passes vertically from host to offspring. Over the course of speciation, the microbial communities differentiate as a mirror to host phylogeny (such situation would be expected in hosts where parents immunize their offspring with microbial clique, e.g., Koala bear mother inoculate “pap” with dropping to shift young one from milk to eucalyptus leaves diet) [35]. Fecal microbiome from healthy humans is a mirror of distal gut microbiome which is highly rich in genes involved in the vitamin synthesis, breakdown of nutrients, and metabolism of xenobiotics as compared to already sequenced human genome and microbes genome [4]. The presence of conjugate transposons in gut microbiome is another important source of horizontal gene transfer in bacteria [36]. The HGT is involved not only in spreading antibiotic resistance genes, but also as a source of clinical response of closely related microbial strains of
Novel strategies in drug discovery are being pursued by targeting horizontal gene transfer involved in the resistance to antibiotic [39] as well as virulence [40]. Targeting virulence factors with Salmonellosis inhibitors causes less damage to indigenous microbes compared to traditional antibiotic therapy, less selective pressure for evolution and transfer of resistance and may be more effective against divergent organisms that have acquired a particular virulence factor by HGT. Genomic islands which are a good source of genes and gene transfer systems are also being targeted with small molecule inhibitors that are co-administered with antibiotics to prevent resistance factors by targeted pathogenesis during the therapy [41].
\nIt has been established that the majority of molecules possessing physiological or pharmacological features are either transported into and or out of the cells by transporting proteins rather than by a passive transport mechanism where drug molecules cross cell membranes through solute transporters that are already involved in the movement of different metabolic intermediary molecules through channels. More than 1000 different types of transporting proteins (transporters) are present in humans [42] comprising solute carriers (SLC) and ATP binding cassettes (ABC) transporters involved in the transport of a broad range of substrates [43].
\nHuman intestinal peptide transporter 1 (hPepT1) belonging to the proton-coupled oligopeptide transporter (POT) family which is also known as solute carrier 15A (SLC15A) is present in the enterocytes, the PepT2 (oligopeptide transporter 2, SLC15A2) in kidney, the PHT1 (peptide histidine transporter 1, SLC15A4) in brain and the PHT2 (peptide histidine transporter 2, SLC15A3) located in spleen, lungs and thymus. Both hPepT1 and PepT2 mediate the transport of di−/tri-peptides and a broad range of peptidomimetics in the organisms, whereas PHT1 and PHT2 mediate the translocation of histidine and with a few selected di- and tri-peptides [44]. The hPepT1, an oligopeptide transporter 1 located in the enterocystes of the small intestine, has low affinity and high capacity transporter protein to transport 400–800 different dipeptides and tripeptides and drugs like ACE’1 (Enalapril) and antiviral (acyclovir) [45]. The hPepT1 is also found in microbes like
Passive diffusion and secondary transport mechanisms in bacteria may involve uptake of drug into bacterial cytoplasm [52, 53]. In the inner membrane of
As described above microorganisms present in the gut of the living organisms contribute to health or cause disease of these organisms by interplay with their immune system. Microbiome is developed at birth according to host interaction but later it is evolved and modified by surrounding factors like environmental and diet. The variation in genetic expression of different individuals is thought to be linked with different microbial composition [57]. Genotype of the host affects the composition of gut microbes. Even mutation of a single gene can cause modification in the structure of gut microbiota. The exact mechanism of association between the gut microbes and the genotype of host is still unknown. Bifidobacteria are highly prevalent beneficial bacteria in gut microbiome and are associated with lactase non-persistent genotype. This genotype is responsible for the synthesis of lactase enzyme which helps to digest the lactose, present in the milk. Absence of this enzyme leads to lactose intolerance in different organisms. So it is important to investigate susceptibility of different underlying pathological conditions by studying microbiomes association with genotype and environmental factors that vary among different human populations [58].
\nDifferent studies showed that metabolic disorders are largely congenital and are associated with different microbiomes. For example, gut microbiomes have been linked to metabolic disorders and obesity [59].
\nIn gut microbiome, dysbiosis (imbalance of microbial flora) can be induced by host factors and/or external factors such as the intake of antibiotics, mental and physical stress, and nutrients in the diet. Dysbiosis is likely to impair the regular gut microbiota and the appearance of pathobionts and the production of metabolites which may be dangerous to the host or may deregulate beneficial microbial-derived metabolites. The microbial symbiosis has a significant role in the development of many diseases [60] such as the gastrointestinal diseases [61, 62], infections [63], metabolic disorders, liver diseases [64], autoimmune diseases [65], mental or psychological diseases [66] and respiratory diseases [67].
\nThe inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC), has for quite some time been suspected to be a host reaction to its gut microbiota. CD represents the chronic inflammation of the GIT (involving any part from mouth to anus) with idiopathic etiology while UC is the chronic inflammation of the large bowel of the GIT with no known cause. Numerous aspects of the microbiota’s association in IBD have been inspected in recent years. About 10–20% of adults and adolescents worldwide are affected by IBD [68]. The precise cause of IBD is unidentified, but it is believed to be a multifactorial disease. Inflammation, infection, visceral hypersensitivity, immunity, genetic factors, motor dysfunction of the GIT as well as psychopathological factors are suspected to play a role in its development [69]. Moreover, abnormal gut microbiota has been noticed in the IBD patients and in animals with intestinal inflammatory disease [70, 71, 72, 73]. Some of the metabolically active anaerobic bacteria in the colon and terminal part of ileum interact with the immune system of epithelium and mucosal layer of the host intestine. Continuous stimulation of these microbial antigens promote pathogenic immune responses and may cause defects in the barrier functions of mucous layer by killing some beneficial bacteria or by immune dysregulation, consequently resulting in UC and CD. Moreover, disrupted microbiota structure and function in inflammatory bowel disease intensify the immune response of the host causing dysfunction of epithelium and increased permeability of the mucous layer of the intestine [74].
\nIt is difficult to identify a single factor responsible of IBD; however, several observations have demonstrated a change in the gut microbial composition in IBD patients, both CD and UC [70]. Even though the gut microbiota has been recognized as responsible for the IBD establishment in non-predisposed hosts, numerous researches have revealed a high rate of pathogenic
For gastric cancer,
Worldwide, the colorectal cancer (CRC) is the fourth most common cause of death associated with cancer [81]. Like other cancers, the CRC is a complex disease related to environmental and genetic factors. Ongoing research has proposed that gut microbiota assumes a role in the convergence of these factors, likely through forming a tumor-advancing environment.
\nIn certain studies, by using a germ-free mice model of adenomatous polyposis coli (APC), a markedly reduced incidence of colonic tumor and a lower tumor load was revealed when compared to normally raised mice. Further other distinct CRC phenotypes such as bleeding from rectum and iron deficiency has also been shown with an invasion of inflammatory cells emerging from an intestinal epithelial barrier dysfunction. Therefore, it seems that the microbiome and host factors (for example, age and genetic predisposition) are important to the CRC growth and progression [82].
\nCardiovascular and metabolic disorders are collectively known as cardiometabolic diseases and are associated with high morbidity and mortality along with significant health care expenditures [83]. The gut-derived and endogenously produced endotoxins including indoxyl sulfate,
The gastrointestinal (GI) system and skin are highly vascularized and densely innervated organs with crucial neuroendocrine and immune roles which are uniquely related to the normal function of skin [90]. Evidence of bidirectional and intimate connection between the gut and skin health as well as a close link between GI health to skin allostasis and homeostasis has been established [91]. GI disturbances resulted often in cutaneous manifestations and the GI system, especially the gut microbiota, appears to participate in the pathophysiology of many inflammatory diseases, i.e., acne, atopic dermatitis and psoriasis [92, 93].
\nThe mechanism by which GI flora exert their effect on skin homeostasis is still unknown; however it is postulated that probably such effect may be related to the modulatory influence of gut commensals on the systemic immunity [94]. Certain gut microbiota and their metabolites, i.e., polysaccharide A, retinoic acid from
In cases of disturbance in intestinal barriers, it was found that intestinal bacteria and their metabolites may have the propensity to accumulate in the skin and have also access to the bloodstream which ultimately disrupts skin homeostasis. In fact, DNA of intestinal microbes has been separated from the plasma of psoriatic patients, thus showing a direct connection between the gut microbiota and skin homeostasis [90]. The short chain fatty acids (SCFAs), i.e., acetate, butyrate and propionate resulting from the fermentation of the fibers in GIT are believed to play an important role in the maintenance of certain skin microbiota which consequently affect cutaneous immune defense system. For example, propionic acid has an antimicrobial effect against the most common community-acquired methicillin-resistant
Intestinal dysbiosis may have the negative potential to affect the skin function since gut microbial flora has a huge potential to produce molecules, both harmful and beneficial, that could then reach the circulation and influence skin. Metabolic products of aromatic amino acids, i.e.,
Infectious diseases of the respiratory tract including pneumonia and influenza result in deaths of approximately 3.25 million people annually [99]. The majority of the therapies being used currently are suboptimal because the problems of efficiency, toxicity and antibiotic resistance are difficult to overcome [100]. Most of the respiratory tract infections represent failure of host’s immune defense. Recently, it was suggested that gut microbiota plays a crucial role in the initiation and adaptation of the immune response in other distal mucosal sites including lungs. Therefore, it is of interest to understand the underlying mechanisms that regulate the interplay between lung defense and gastrointestinal tract and how this interaction aids in achieving optimal lung health.
\nAn abnormal T-helper type 2 (Th2) cell responses is often associated with asthma and allergies. The Th2 cells are recognized by their ability to synthesize inflammatory cytokines including IL-13, IL-9, IL-5 and IL-4 [101] Evidence suggests that the development of allergic diseases in lung is directly affected by alteration in gut immune response [65]. In fact, a single oral dose of
Gut microbiota also plays a critical role in the immune response to respiratory tract viral infections like influenza. In infected mice, the CD8 and CD4 T cell subpopulations are directly influenced by the intestinal microbiota [103]. It has also been suggested that an intact intestinal microbiota is necessary for the expression of pro-inflammatory cytokines including pro-IL-18 and pro-IL-1β, which are essential for clearance of influenza [104]. This indicates that microbial signals are provided by gut microbiota which are crucial for the shaping and priming the immune response to viral pneumonia.
\nSimilar findings regarding the role of gut microbiome in immune response to respiratory bacterial infections have also been observed in germ-free mice. These mice were found to be more susceptible to pulmonary infection caused by bacterial pathogen
All systems of the body including maternal microbiome are affected by pregnancy. Changes in gut and vaginal microbiome during gestation are of particular significance because during vaginal delivery there is vertical transmission of microbes to the newborn [106, 107, 108]. During pregnancy the vaginal microbiota composition changes throughout the gestation period. In addition to vaginal microbiome, the maternal intestinal microbiome also undergoes change during pregnancy. It has been reported that bacterial diversity decreases in women as the pregnancy progresses [107]. Particularly, the ratio of pro-inflammatory
The consequences of changes in maternal vaginal and gut microbiota on mother health are not clear; however, the gestational changes in fecal and vaginal microbiota are considered to be important for the adaptive response necessary for protection as well as to promote the fetus health. These changes also help in providing a particular microbial inoculum to the newborn at birth before its exposure to other environmental microbes. Also the microbial communities’ composition in maternal vagina and gut are not independent of each other. In fact, in pregnant women of 35–37 weeks of gestation most of bacteria, including species of
Some research studies reported that shift in gut microbiota of mother during pregnancy may be an adaptive response for the mother and newborn health. In mice, an increase in the gut bacteria associated with gestational age, promotes body weight gain indicating a co-evolution of these microbes with their hosts during pregnancy [107]. Moreover, during vaginal delivery, the vertical transmission of these maternal gut microbiomes to the neonate may help the newborn to get an immediate access to microbiota at birth [107, 111].
\nBoth extrinsic and intrinsic factors play an important role to regulate the development and maturation of the central nervous system (CNS) in humans. In germ-free and antibiotic-treated animals the physiology of the CNS can be affected by neurochemistry as well as by specific microbiota [112]. Evidences for interaction between neuropsychiatric and gastrointestinal pathology in humans have been reported in different psychiatric conditions including autism, depression and anxiety [113].
\nThe role of gut-brain interaction in the nervous system development is also recognized. Gut-brain axis actually establishes a relationship between gut-microbiota and their interaction with brain leading to changes in the status of the CNS. The dysbiosis in microbial species of the gut may lead to induce imbalance in host homeostasis, atypical immune signaling and ultimately progression of CNS diseases [114].
\nThe permeable blood brain barrier (BBB) and functional lymphatic vessels residing in dura meningeal membrane may serve as a gateway for transmission of signals [115]. The exposure to several environmental factors can affect the generation of neurons during the development of the CNS [113]. It has been suggested that maternal-fetal interface permeability permits regulatory factors from the gut microbiota to stimulate Toll-like receptor 2 (TLR2) that helps to promote neural development of fetus and also impart its effects on cognitive function during adulthood [116].
\nThe combination of microbial strains (especially the probiotic) can actively counteract the deficient neurogenesis which further strengthen the developmental link of microbiome to the hippocampal neuronal generation [117]. The brain-blood barrier (BBB) is a highly selective and semipermeable barricade that permits the passage of neutral, low molecular weight and lipidic soluble molecules [118]. In the development of the structural components and growth of vasculature, BBB requires arachidonic acid (AA) and decohexaenoic acid (DHA) which are provided as polyunsaturated fatty acids (PUFA) by gut microbiome [119]. It has been demonstrated that the restoration of BBB is possible in germ-free mice by colonization of
The most important environmental factors that may lead to dysbiosis include (i) Physical or psychological stress, (ii) use of antibiotics, and (iii) diet (Figure 2).
\nEnvironmental factors influencing gut microbiota.
Stress is usually defined as homeostasis disruption due to physical, psychological or environmental stimuli known as stressors leading to adaptive behavioral and physiological response in order to restore homeostasis [121]. The effect of both psychological and physical stress on gut microbiome is widely recognized and has been observed in both humans as well as animals [122]. Some research conducted in mice has shown that the microbial composition in the cecum was altered in response to the exposure of a social stressor by placing an aggressive male mouse into the cages of non-aggressive mice. Furthermore, the plasma concentration of stress hormones such as adrenocorticotropic hormone (ACTH) and corticosterone was found to be significantly higher in germ-free mice as compared to specific pathogen-free mice. In addition, several stressors including acoustic stress, self-control conditions and food deprivation have a negative impact on the gut microbiome resulting in the impairment of the immune system [123, 124].
\nIt has been observed in both humans and animals that the treatment with antibiotics can result in a decreased population of beneficial bacteria including
Food is metabolized by the gut microbial species to extract nutrients, but some microbial species are more efficient in extracting nutrients from food as compared to other species. As different individuals have slightly different microbial populations, it is probable that more nutrients are harvested by some people’s gut microbes making them perhaps more prone to become overweight. A high percentage of
The human body is a super-organism consisting of 10 times more microbial cells than our own body cells. The body’s assortment of microorganisms is mainly in gastrointestinal tract, collectively called the gut microbiota. It can be comparable to an organ in because it performs functions necessary for our survival by contributing directly and/or indirectly in various physiological processes. For the past decade, human gut microbiota has been extensively studied as many scientists believe that human health mainly depends on microbes that are living on or in our body apart from our own genome. Recently, research findings have suggested that gut microbiome is evolving as a new organ system mainly due to its specific biochemical interaction with its host which affirm its systemic integration into the host physiology as gut bacteria are not only critical for regulating gut metabolism, but also important for other systems of host including immune system. The focus of this chapter was to highlight the importance of gut microorganisms as a new organ system and their possible involvement with host systems as well as the metabolism of different drugs and nutrients in the gut by these microbes. So, in this chapter, we have reviewed opinions of different researchers about the role of gut microbiota in maintaining health as well as its contributory role in different ailments. However, literature revealed that the involvement of gut microbiota in altering host genetics effecting disease progression needs further investigations.
\nIntechOpen publishes different types of publications
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\n\nRESEARCH CHAPTER – A research chapter reports the results of original research thus contributing to the body of knowledge in a particular area of study.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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