Decrease of prevalence of
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Toxoplasmosis is a globally distributed parasitic zoonosis. Cats and other
This chapter reviews the epidemiology and epizootiology of
We analysed epidemiological data published in the last 20 years for all SEE countries except Bulgaria and Bosnia & Herzegovina for which none were available. Data in the published reports were obtained using a wide array of immunodiagnostic assays which may somewhat limit comparisons. Indeed, the tests in use have varied over time both among and within individual countries, and included the Sabin-Feldman test (SFT), complement fixation test (CFT), indirect fluorescence (IFAT) to direct agglutination (DA) and ELISA, whether in-house or commercial; the latter ones were obtained from various manufacturers. However, this limitation applies to any such review (Gilbert & Peckham, 2002), and moreover, the pattern of infection observed in the region despite the variety of tests with their different specificities, sensitivities, cut-offs etc., rather emphasizes the described trends.
The vast majority of epidemiological data on toxoplasmosis in SEE comes from studies in women of generative age, and a few from studies in immunocompromised patients.
Data on the prevalence of
Continuous monitoring of the prevalence of
Prevalence of
A decreasing trend of
It appears that, according to reports from the eastern part of the region, there is a north-to-south decrease in the infection prevalence (Table 1). For instance, in 1994, the prevalence ranged from 69% in southern Hungary (as a region neighbouring the SEE to the north) (Szénási et al., 1997), over 53% in Serbia (Bobić et al., 2003) to 26% in northern Greece (Diza et al., 2005). This trend was also evident within SEE in all years for which comparative data were available (2002: Serbia 36%, FYRoM 25%; 2004: Serbia 32%, Northern Greece 20%; 2007: Serbia 31%, Northern Greece 21%) (Cvetković et al., 2003; Diza et al., 2005; Bobić et al., 2007; Kansouzidou et al., 2008; Bobić et al., 2011). Moreover, a significant north-to-south decrease in the infection prevalence was also shown within Serbia itself (Bobić et al., 2003). The north-to-south decrease in the prevalence of infection within SEE suggests a possible influence of climatic conditions, which vary across the region from continental to Mediterranean, and over time as well.
1991 | 73% (1) | 69% (2) | ||
1992 | 70% (1) | 75% (2) | ||
1993 | 64% (1) | 58% (2) | ||
1994 | 69% (1) | 53% (2) | 26% (6) | |
2002 | 36% (3) | 25% (4) | ||
2004 | 32% | 20% | 20% (6) | |
2007 | 31% | 21% (7) |
Decrease of prevalence of
Seasonality of infection was examined in Slovenia (Logar et al., 2005) in the west and Serbia (Bobić et al., 2010) in the east. Both studies showed a strong seasonality, with significantly more cases of acute infection in the winter than in the summer months (Fig. 2). In Slovenia, seasonality of infection was attributed to "more frequent and closer contacts with potentially
Seasonality of
Accordingly, the decrease in the prevalence of
Toxoplasmosis is a major opportunistic infection causing life-threatening disease in immunocompromised individuals, which is considered to be a consequence of reactivation of previously latent infection. Although opportunistic infection due to
Although the population structure of
Strategies for the prevention of CT include general screening-in-pregnancy programs and health education, and countries with a low prevalence of infection generally opt for health education (Kravetz et Federman, 2005; Gilbert and Peckham, 2002), while those with a high prevalence adopt screening-in-pregnancy programs (Aspöck & Pollak, 1992; Thulliez, 1992). In the SEE, a systematic program for the prevention of CT based on serological screening of pregnant women and health education has been implemented in Slovenia, while no other country has a systematic prevention program (Logar et al, 2002). The changing pattern of infection across the region currently complicates the choice of prevention strategy. A decrease in the prevalence of infection in women of childbearing age implies a rising proportion of women susceptible to infection in pregnancy, which, in turn, may lead to an increase in the incidence of congenital infections. Indeed, an increase in the incidence of primary infections in pregnancy has been shown through systematic screening-in-pregnancy programs; in Austria, which has been screening all pregnant women ever since 1975, the decrease in
The decreasing prevalence of
Health education is an adequate preventive measure for uninfected immunocompromised patients too.
Epizootiological surveillance of
Throughout SEE, few data are available on
The data on strain isolation in animals are scarce; researchers in Bulgaria have reported isolation of both virulent and avirulent strains of
Earlier serological investigations of various animal species in SEE countries showed a prevalence of 37% for cattle, 30% for sheep, 26% for pigs, 17% for horses, 41% for dogs, 25% for cats, 52% for house mice and 20% for rats in Serbia, but except for pigs, they were carried out on samples of limited size (Šibalić, 1977). In Bulgaria, Nankov (1968) has shown a 15.7% prevalence in hares, and Arnaudov (1971, 1973) reported a prevalence of 32.6% for sheep and 27.2% for goats.
An overview of studies of
As in humans, and similar to the rest of Europe and elsewhere (Tenter et al., 2000; Hall et al., 2001), data on the prevalence in animals vary quite widely in SEE. The highest prevalence has been reported for cattle, sheep and goats (Table 2). While farming practices are similar throughout the SEE region, differences within the region mostly occur in the climatic conditions and terrain characteristics.
The single nation-wide survey on
Croatia | Goats | 79 | 13.9 | MAT (1:20) | Farms | Rajković-Janje et al., 1993 |
Goats | 100 | 4 | MAT (1:20) | Farms | ||
Σ Goats | 179 | 8.4 | MAT (1:20) | Σ | ||
Sheep | unknown | 11.6 (sheep) 9.4 (lambs) | DAT | Farms | Rajković-Janje et al., 1994 | |
Sheep | unknown | 0.5 | DAT | Nomadic | ||
Σ Sheep | 334 | 4.8 | DAT | Σ | ||
Sheep | unknown | 48.4 | ELISA | 10 farms | Marinculić et al., 1997 | |
Chickens | 716 | 0.4 | Bioassay | Abattoir | Kutičić & Wikerhauser, 2000 | |
Rats | 142 | 1.4 | Bioassay | Pig farms | Kutičić et al., 2005 | |
Mice | 86 | 0 | Bioassay | Pig farms + households | ||
Serbia | Cattle | 611 | 76.3 | MAT (1:25) | Farms + abattoir | Klun et al., 2006 |
Sheep | 511 | 84.5 | MAT (1:25) | Farms | ||
Pigs | 605 | 28.9 | MAT (1:25) | Farms + abattoir | ||
Horses | 250 | 30.8 | MAT (1:25) | Farms | Klun, 2005 | |
Sheep | 367 | 7.1 | unknown | Aborting sheep | Vidić et al., 2007 | |
Sheep | 30 | 36.7 | IFAT (1:20) | Farm | Lalošević et al., 2008 | |
Pigs | 488 | 9.2 | MAT (1:25) | Abattoir | Klun et al., 2011 | |
Goats | 356 | 74.7 | MAT (1:25) | Farms | Djokić et al., 2011 | |
Rats | 80 | 27.5 | MAT (1:25) | Urban | Vujanić et al., 2011 | |
Rats | 144 | 7.6 | Microscopy | |||
Mice | 12 | 3 | Microscopy | |||
Rats | 144 | 10.4 | Real-time PCR | |||
Mice | 12 | 83.3 | Real-time PCR | |||
Pigeons | 30 | 13.3 | MAT (1:25) | Urban (wild) | Personal observations | |
Bulgaria | Wild animals (7 species) | 643 | 0 – 16.7 | Agar gel micro precipitation | Wild | Arnaudov et al., 2003 |
Sheep | 380 | 48.2 | IHAT (1:10) | Farms | Prelezov et al., 2008 | |
Goats | 364 | 59.8 | IHAT (1:10) | |||
Greece (mainland) | Sheep | 840 | 53.4 | IFAT | Farms | Kontos et al., 2001 |
Sheep | 450 | 58.5 | ELISA | |||
Cattle | 105 | 20 | ELISA | Dairy farms | Diakou et al., 2005a | |
Sheep | 350 | 52.6 | ELISA | Mixed stock farms | Diakou et al., 2005b | |
Goats | 280 | 62.9 | ||||
Sheep | 184/182 | 52.2/50.5 | ELISA (a) | Organic farms | Ntafis et al., 2007 | |
Goats | 229/167 | 22.3/18 | ELISA (a) | |||
Horses/equids | 753/773 | 1.7/1.8 | ELISA (a) | Farms | Kouam et al., 2010 | |
Dogs | 2512 | 31.8 | ELISA | unknown | Haralabidis & Diakou, 1999 | |
Pigeons | 379 | 5.8 | ELISA | Domestic flocks | Diakou et al., 2011 | |
Pigeons | 50 | 0 | ELISA | Urban (wild) | ||
Σ Pigeons | 429 | 5.1 | ELISA | Σ | ||
which is consistent with reports on the rapid decrease of
The same study showed a prevalence of 85% in sheep in Serbia, of which 10% had high antibody levels of ≥1:1600, suggestive of acute infection. Although arbitrary, the cut-off of 1:1600 is even conservative since Dubey and Welcome (1988) had considered a titre of 1:1024 high. However, correlation with ovine abortions could not be established, since etiological laboratory diagnosis of ovine abortions in Serbia does not include diagnosis of
An outbreak of toxoplasmosis in sheep has recently been reported; massive abortions (60%) occurred in a flock of 500 dairy sheep in Northern Greece at 110-130 days of pregnancy, diagnosed upon observation of tissue cysts in brain smears of aborted fetuses, and by serological (ELISA) examinations of mother and fetal serum samples. The abortion rate declined immediately upon instituting sulfadimidine therapy (Giadinis et al., 2011).
In horses, who generally have lower seroprevalence values than small ruminants (van Knapen et al., 1982), a prevalence of 30.8% has been determined in a study in Serbia (Klun, 2005),
In pigs, an overall seroprevalence of 29% was established in Serbia (Klun et al., 2006). Of those seropositive, 4% were likely to be in the acute stage of infection, indicating continuous presence of infection reservoirs in the environment. Risk factors included age and farm type (Table 3). Since pigs are continuously exposed to infection, the increase in the risk of infection with age, ranging from 15% in market weight age pigs to 41% in adults, was expected, and repeated previous findings (Dubey et al., 1991; Dubey et al., 1995; Weigel et al., 1995; Damriyasa et al., 2004). Pigs on finishing type farms were four-fold more likely to be infected than those from farrow-to-finish farms. According to these results, it was proposed that a national strategy to reduce the level of
Cattle | Herd size | |||
Large (n"/>100) | 1.00 | |||
Small (n<10) | 2.19 | 1.28–3.75 | 0.004 | |
Type of housing | ||||
Stable only | 1.00 | |||
Access to outside pens | 0.37 | 0.21–0.67 | 0.001 | |
Access to pasture | 0.72 | 0.25–2.07 | 0.548 | |
Region | ||||
Northern Serbia | 1.00 | |||
Western Serbia | 2.04 | 1.10–3.79 | 0.024 | |
Central/Eastern Serbia | 0.97 | 0.32–2.90 | 0.951 | |
Belgrade District | 0.60 | 0.30–1.20 | 0.148 | |
Sheep | Farm type | |||
Private large (n"/>100) | 1.00 | |||
State-owned (n"/>100) | 4.18 | 2.18–8.00 | <0.001 | |
Private small (n<10) | 1.79 | 0.55–5.81 | 0.332 | |
Region | ||||
Northern Serbia | 1.00 | |||
Western Serbia | 4.66 | 1.18–18.32 | 0.028 | |
Central/Eastern Serbia | 0.82 | 0.25–2.73 | 0.748 | |
Pigs | Age group | |||
Market weight (<8 months) | 1.00 | |||
Adult (≥8 months) | 3.87 | 2.60–5.76 | <0.001 | |
Farm type | ||||
Farrow-to-finish | 1.00 | |||
Finishing | 3.96 | 1.97–7.94 | <0.001 | |
Risk factors for
A recent study on
On the other hand, the modern approach in farm management to provide for the welfare of the animals as well as organic food for human consumption is to develop animal-friendly (organic) farms. According to experiences from the Netherlands (Kijlstra et al., 2004), development of such farms may result in an increase in
A major reason for the control of
On the other hand, according to official statistical reports (RZS, 2006–2010), pork represents approximately 50% of all meat consumed in Serbia. Thus, although pigs were the least infected of the examined species, given the findings that the prevalence increases with age and reaches 41% in sows (Klun et al., 2006), pork consumption may significantly contribute to human infection. When used for cooking, pork is generally properly thermally processed, but in most of the SEE countries’ tradition mature pork is also highly valued for making delicatessen meat products. Raw or improperly cured sausages and ham are the source of small (family) epidemics of trichinellosis which, in spite of mandatory meat examination for
For most meat animals, although a trend is generally (worldwide) difficult to establish due to the scarcity of studies in most countries (no two time points), there is no visible reduction in the prevalence of
Ubiquitous contamination of the environment is also evident from the presence of
Existing strategies for the prevention of toxoplasmosis in countries which have been implementing them for years have led to a decrease in its incidence, but have not solved the problem of congenital infection. This clearly shows that new comprehensive strategies for the prevention of toxoplasmosis are needed. These should be based on accurate and validated data on (1) the routes and risk factors for human infection on local level, which will allow for a more efficient health education; (2) routes and risk factors for meat animal infection to diminish infection reservoirs; (3) environmental contamination. Epidemiological and epizootiological data presented in this chapter show how far along this road we have come, and more importantly, how far we still have to go to achieve successful prevention of
This work has been supported by project grant No. III 41019 from the Ministry of Education and Science of Serbia.
According to Russell Ackoff [1], a systems theorist and professor of organizational change, the content of the human mind can be classified into three categories:
IF temperature < = 0° C THEN cold = true;
Cold IF == right THEN notify personnel to remove ice from aircraft.
Indeed, knowledge is the appropriate collection of information such that it intends to be useful. Knowledge is a deterministic process. Memorization of information leads to knowledge. Knowledge represents a pattern and provides a high level of predictability regarding what is being described or will happen next.
Ex: If the humidity is very high and the temperature drops drastically, the atmosphere is unlikely to hold the humidity so that it rains.
This knowledge has a useful meaning, but its integration in a context will infer new knowledge. For example, a student memorizes or accumulates knowledge of the multiplication Table. A student can answer 2 × 2 because this knowledge is in the multiplication table. Nevertheless, when asked for 1267 × 300, he cannot answer correctly because he cannot dip into the multiplication table. To answer such a question correctly requires a real cognitive and analytical capacity that exists in the next level … comprehension. In computer jargon, most of the applications we use (modeling, simulation, etc.) use stored knowledge.
The system is an aggregated “whole” where each component interacts with at least one other component of the system. The components or parts of a system can be real or abstract.
All system components work toward a standard system goal. A system can contain several subsystems. It can be connected to other systems.
A system is a collection of elements or components that interact to achieve goals. The elements themselves and the relationships between them determine how the system works. Systems have inputs, processing mechanisms, outputs, and feedback mechanisms. A system processes the input to create the output [3].
Input is the activity of collecting and capturing data.
Processing involves the transformation of inputs into outputs such as computation, for example.
Output is about producing useful information, usually in the form of documents and reports. The output of one system can become the input of another system. For example, the output of a system, which processes sales orders, can be used as input to a customer’s billing system. Computers typically produce output to printers and display to screens. The output can also be reports and documents written by hand or produced manually.
Finally, feedback or feedback is information from the system used to modify inputs or treatments as needed.
An information system (IS) is a set of interrelated components that collect, manipulate, store and disseminate information and provide a feedback mechanism to achieve a goal. The feedback mechanism helps organizations achieve their goals by increasing profits, improving customer service [3], and supporting decision-making and control in organizations [4].
Companies use information systems to increase revenues and reduce costs.
In organizations, information systems are structured around four essential elements, proposed in the 1960s by Harold Leavitt (Figure 1). The pattern is known as the “Leavitt Diamond.”
Leavitt’s diamond: A socio-technical view of IS.
A company has systems to support the different managerial levels. These systems include transaction processing systems, management information systems, decision support systems, and dedicated business intelligence systems.
Companies use information systems so that accurate and up-to-date information is available when needed [5].
Within the same organization, executives at different hierarchy levels have very different information requirements, and different types of information systems have evolved to meet their needs. A common approach for examining the types of information systems used within organizations is to classify them according to their roles at different organizational structure levels, and this approach is called a vertical approach. Indeed, the organization is considered a management pyramid at four levels (Figure 2):
Information Systems types according to managerial level.
At the operational level, managers need systems that keep track of the organization for necessary activities and operations, such as sales and material flow in a factory. A transaction processing system is a computer system that performs and records the routine (daily) operations necessary for managing affairs, such as keeping employee records, payroll, shipping merchandise, keeping records, accounting and treasury.
At this level, the primary purpose of systems is to answer routine questions and monitor transactions flow through the organization.
At the operational level, tasks, resources, and objectives are predefined and highly structured. The decision to grant credit to a customer, for example, is made by a primary supervisor according to predefined criteria. All that needs to be determined is whether the client meets the criteria.
Middle managers need systems to help with oversight, control, decision making, and administrative activities. The main question that this type of system must answer is: is everything working correctly?
Its role is to summarize and report on essential business operations using data provided by transaction processing systems. Primary transaction data is synthesized and aggregated, and it is usually presented in reports produced regularly.
DSS supports decision-making for unusual and rapidly evolving issues, for which there are no fully predefined procedures. This type of system attempts to answer questions such as: What would impact production schedules if we were to double sales for December? What would the level of Return on investment be if the plant schedule were delayed by more than six months?
While DSSs use internal information from TPS and MIS systems, they also leverage external sources, such as stock quotes or competitor product prices. These systems use a variety of models to analyze the data. The system can answer questions such as: Considering customer’s delivery schedule and the freight rate offered, which vessel should be assigned, and what fill rate to maximize profits? What is the optimum speed at which a vessel can maximize profit while meeting its delivery schedule?
ESS helps top management make decisions. They address exceptional decisions requiring judgment, assessment, and a holistic view of the business situation because there is no procedure to be followed to resolve a given issue at this level.
ESS uses graphics and data from many sources through an interface that senior managers easily understand. ESS is designed to integrate data from the external environment, such as new taxes or competitor data, and integrate aggregate data from MIS and DSS. ESSs filter, synthesize and track critical data. Particular attention is given to displaying this data because it contributes to the rapid assimilation of these top management figures. Increasingly, these systems include business intelligence analysis tools to identify key trends and forecasts.
Decision-making in companies is often associated with top management. Today, employees at the operational level are also responsible for individual decisions since information systems make information available at all company levels.
So decisions are made at all levels of the company.
Although some of these decisions are common, routine, and frequent, the value of improving any single decision may be small, but improving hundreds or even thousands of “small” decisions can add value to the business.
Not all situations that require decisions are the same. While some decisions result in actions that significantly impact the organization and its future, others are much less important and play a relatively minor role. A decision’s impact is a criterion that can differentiate between decision situations and the degree of the decision’s structuring. Many situations are very structured, with well-defined entrances and exits. For example, it is relatively easy to determine the amount of an employee’s pay if we have the appropriate input data (for example, the number of hours worked and their hourly wage rate), and all the rules of relevant decision (for example, if the hours worked during a week are more than 40, then the overtime must be calculated), and so on. In this type of situation, it is relatively easy to develop information systems that can be used to help (or even automate) the decision.
In contrast, some decision situations are very complex and unstructured, where no specific decision rules can be easily identified. As an example, consider the following task: “Design a new vehicle that is a convertible (with a retractable hardtop), has a high safety rating, and is esthetically pleasing to a reasonably broad audience. No predefined solution to this task finalizing a design will involve many compromises and require considerable knowledge and expertise.
Examples of Types of decisions, according to managerial level, are presented in Table 1.
Decision level | Characteristics of decisions | Examples of decisions |
---|---|---|
Top Management | Unstructured | Decide whether or not to come into the market |
Approve the budget allocated to capital | ||
Decide on long-term goals | ||
Intermediate management | Semi-structured | Design a marketing plan |
Develop a departmental budget | ||
Design a website for the company | ||
Operational management | Structured | Determine the overtime hours |
Determine the rules for stock replenishment | ||
Grant credit to customers | ||
Offer special offers to customers |
Types of decisions according to managerial level.
Generally speaking, structured decisions are more common at lower levels of the organization, while unstructured problems are more common at higher business levels.
The more structured the decision, the easier it is to automate. If it is possible to derive an algorithm that can be used to make an efficient decision and the input data to the algorithm can be obtained at a reasonable cost, it generally makes sense to automate the decision.
Davenport and Harris [6] proposed a framework for the categorization of applications used for decision automation. Most of the systems they describe include some expert systems, often combined with DSS and/or EIS aspects. The categories they provided include Solution Configuration, Optimization of Performance, Routing or Segmentation of Decisions, Business Regulatory Compliance, Fraud Detection, Dynamic Forecasting, and Operational Control.
Many business decision situations are not very structured, and therefore cannot (or should not) be fully automated.
Data visualization tools allow users to see patterns and relationships in large amounts of data that would be difficult to discern if the data had been presented in tabular form, for example.
Geographic Information Systems (GIS) helps decision-makers visualize issues requiring knowledge about people’s geographic distribution or other resources. GIS software links the location data of points, lines, and areas on a map. Some GIS have modeling capabilities to modify data and simulate the impact of these modifications. For example, GIS could help the government calculate response times to natural disasters and other emergencies or help banks identify the best replacement for installing new branches or ATMs of tickets.
Geographic (or geospatial) information refers not only to things that exist (or are being planned) on specific locations on the Earth’s surface but also to events such as traffic congestion, flooding, and other events such as an open-air festival [7].
Its scope and granularity characterize this information:
Location, extent, and coverage are essential aspects of geographic information.
Granularity, for example, geometric information, can be concise or fuzzy depending on the application.
GIS is used to capture, store, analyze, and visualize data that describes part of the Earth’s surface, technical and administrative entities, and the results of geosciences, economics, and ecological applications.
It is a computer system with a database observing the spatial distribution of objects, activities, or events described by points, lines, or surfaces.
It is a comprehensive collection of tools for capturing, storing, extracting, transforming, and visualizing real-world spatial data for applications.
It is an information system containing all the data of the territory, the atmosphere, the surface of the Earth, and the lithosphere, allowing the systematic capture, the update, the manipulation, and the analysis of these data standardized reference framework.
It is a decision support system that integrates spatial data into a problem-solving environment.
Other definitions of GIS exist depending on the point of view of application [7], a GIS can be considered as
A collection of spatial data with storage and retrieval functions
A collection of algorithmic and functional tools
A set of hardware and software components necessary for processing geospatial data
A particular type of information technology
A gold mine for answers to geospatial questions
A model of spatial relations and spatial recognition.
Typically, a GIS provides functions for the storage and retrieval, interrogation and visualization, transformation, geometric and thematic analysis of information.
Indeed, geographic/geospatial information is ubiquitous, as seen on mobile devices such as cell phones, maps, satellite images, positioning and routing services, and even 3D simulations, gaining popularity from increasingly essential segments of the consumers.
Technological advances in recent years have transformed classical GIS into new forms of geospatial analysis tools, namely:
Web-based and service-oriented approaches have led to a client–server architecture.
Mobile technology has made GIS ubiquitous in smartphones, tablets, and laptops (opening up new markets).
IS applications cover functional areas and focus on the execution of business processes across the enterprise, including all management levels.
There are several categories of business applications: Enterprise Resource Planning (ERP), Supply Chain Management systems (SCM), Customer Relationship Management systems (CRM), electronic commerce or e-commerce, Knowledge Management systems or KM, and Business Intelligence or BI. The categories of business applications dealt with in this section cover all managerial levels since KMS are mainly intended for top management (ESS), SCMs, CRMs, and BI for mid-level management (MIS and DSS), ERP and e-commerce dedicated to the transactional level (TPS or basic or operational).
However, it is useful to specify that some ERP systems, such as the global giant SAP, offer versions of its software package covering these different categories, including SCM and CRM.
ERPs allow business processes related to production, finance and accounting, sales and marketing, and human resources to be integrated into a single software system. Information that was previously fragmented across many different systems is integrated into a single system with a single, comprehensive database that multiple business stakeholders can use.
An ERP system centralizes an organization’s data, and the processes it applies are the processes that the organization must adopt [8]. When an ERP provider designs a module, it must implement the rules of the associated business processes. ERP systems apply best management practices. In other words, when an organization implements ERP, it also improves its management as part of ERP integration. For many organizations, implementing an ERP system is an excellent opportunity to improve their business practices and upgrade their software simultaneously. Nevertheless, integrating an ERP represents a real challenge: Are the processes integrated into the ERP better than those currently used? Furthermore, if the integration is booming, and the organization operates the same as its competitors, how do you differentiate yourself?
ERPs are configurable according to the specificities of each organization. For organizations that want to continue using their processes or even design new ones, ERP systems provide means for customizing these processes. However, the burden of maintenance falls on the organizations themselves in the case of ERP customization.
Organizations will need to consider the following decision carefully: should they accept the best practice processes embedded in the ERP system or develop their processes? If the choice is ERP, process customization should only concern processes essential to its competitive advantage.
Electronic commerce is playing an increasingly important role in organizations with their customers.
E-commerce enables market expansion with minimal capital investment, improves the supply and marketing of products and services. Nevertheless, there is still a need for universally accepted standards to ensure the quality and security of information and sufficient telecommunications bandwidth.
The three main categories of e-commerce are Business-to-Consumer (B2C), Business-to-Business (B2B), and Consumer-to-Consumer (C2C).
Business-to-Consumer (B2C) e-commerce involves the retailing of products and services to individual customers. Amazon, which sells books, software, and music to individual consumers, is an example of B2C e-commerce.
Business-to-Business (B2B), e-commerce involves the sale of goods and services between businesses. The ChemConnect website for buying and selling chemicals and plastics is an example of B2B e-commerce.
Consumer-to-Consumer (C2C), this type of e-commerce involves consumers selling directly to consumers. For example, eBay, the giant web-based auction site, allows individuals to sell their products to other consumers by auctioning their goods, either to the highest bidder or through a fixed price.
Information systems for the management of the supply chain or SCM make it possible to manage its suppliers’ relations. These systems help suppliers and distributors share information about orders, production, inventory levels, and delivery of products and services so that they can source, produce and deliver goods and services efficiently.
The ultimate goal is to get the right amount of products from their suppliers at a lower cost and time. Additionally, these systems improve profitability by enabling managers to optimize scheduling decisions for procurement, production, and distribution.
Anomalies in the supply chain, such as parts shortages, underutilized storage areas, prolonged storage of finished products, or high transportation cost, are caused by inaccurate or premature information. For example, manufacturers may stock an excessive amount of parts because they do not know precisely the dates of upcoming deliveries from suppliers. Alternatively, conversely, the manufacturer may order a small number of raw materials because they do not have precise information about their needs. These supply chain inefficiencies squander up to 25 percent of the company’s operating costs.
If a manufacturer has precise information on the exact number of units of the product demanded by customers, on what date, and its exact production rate, it would be possible to implement a successful strategy called “just in time” (just-in-time strategy). Raw materials would be received precisely when production needed them, and finished products would be shipped off the assembly line with no need for storage.
However, there are always uncertainties in a supply chain because many events cannot be predicted, such as late deliveries from suppliers, defective parts or non-conforming raw materials, or even breakdowns in the production process. To cope with these kinds of contingencies and keep their customers happy, manufacturers often deal with these uncertainties by stocking more materials or products than they need. The safety stock acts as a buffer against probable supply chain anomalies. While managing excess inventory is expensive, a low stock fill rate is also costly because orders can be canceled.
CRM aims to manage customer relationships by coordinating all business processes that deal with customers’ sales and marketing. The goal is to optimize revenue, customer satisfaction, and customer loyalty. This collected information helps companies identify, attract and retain the most profitable customers, and provide better service to existing customers and increase sales.
The CRM captures and integrates the data of the company’s customers. It consolidates data, analyzes it, and distributes the results to different systems and customer touchpoints throughout the company. A point of contact (touchpoint, contact point) is a means of interaction with the customer, such as telephone, e-mail, customer service, conventional mail, website, or even a sales store, by retail.
Well-designed CRM systems provide a single view of the company’s customers, which is useful for improving sales and customer service quality. Such systems also provide customers with a single view of the business regardless of their contact point or usage.
CRM systems provide data and analytical tools to answer these types of questions: “What is the value of a customer to the business” “Who are the most loyal customers?” “Who are the most profitable customers” and “What products are profitable customers buying?”
Businesses use the answers to these questions to acquire new customers, improve service quality, support existing customers, tailor offerings to customer preferences, and deliver escalating services to retain profitable customers.
Some companies perform better than others because they know how to create, produce, and deliver products and services. This business knowledge is difficult to emulate, is unique, and can be leveraged and deliver long-term strategic benefits. Knowledge Management Systems or KMS enable organizations to manage processes better to collect and apply knowledge and expertise. These systems collect all the relevant knowledge and experiences in the company and make them available to everyone to improve business processes and decision management.
Knowledge management systems can take many different forms, but the primary goals are: 1) facilitating communication between knowledge workers within an organization, and 2) to make explicit the expertise of a few and make it available to many.
Consider an international consulting firm, for example. The company employs thousands of consultants across many countries. The consultancy team in Spain may be trying to resolve a client’s problem, very similar to a consultancy team in Singapore that has already been solved. Rather than reinventing the solution, it would be much more useful for the Spain team to use the Singapore team’s knowledge.
One way to remedy this situation is to store case histories from which employees worldwide can access (via the Internet) and search for cases (using a search engine) according to their respective needs. If the case documentation is of good quality (accurate, timely, complete), the consultants will share and benefit from each other’s experiences, and the knowledge gained.
Unfortunately, it is often difficult to get employees to contribute meaningfully to the knowledge base (as they are probably more concerned with moving forward on their next engagements with customers rather than documenting their past experiences). For such systems to have any chance of success, the work organization must change, such as establishing a reward system for cases captured and well documented.
The term Business Intelligence (BI) is generally used to describe a type of information system designed to help decision-makers learn about trends and identify relationships in large volumes of data. Typically, BI software is used in conjunction with large databases or data warehouses. While the specific capabilities of BI systems vary, most can be used for specialized reporting (e.g., aggregated data relating to multiple dimensions), ad-hoc queries, and trend analysis.
As with knowledge management systems, the value of business intelligence systems can be hampered in several ways. The quality of the data that is captured and stored is not guaranteed. Besides, the database (or data warehouse) may lack essential data (for example, ice cream sales are likely to correlate with temperature; without the temperature information, it may be difficult to identify why it is. There has been an increase or decrease in sales of ice cream). A third challenge is the lack of mastery of data analysts over the context of the organization’s operations, even if they are proficient in BI software. In contrast, a manager has mastery of the organization but does not know how to use BI software. As a result, it is common to have a team (a manager associated with a data analyst) to get the most information (and/or knowledge) from a business intelligence system.
Unlike physical assets, the information does not necessarily disappear when it has been stolen. If an organization holds confidential information such as a new manufacturing process, it may be uploaded by an unauthorized person and remain available to the organization.
Exposing information to unauthorized personnel constitutes a breach of confidentiality.
Another type of system failure happens when the integrity of information is no longer guaranteed. In other words, rather than unauthorized exposure of information, there are unauthorized changes of information. A corporate website containing documentation on how to configure or repair its products could suffer severe financial harm if an intruder could change instructions, leading to customers misconfigure or even ruin the purchased product.
Finally, the denial of access to information or the unavailability of information represents another type of information failure. For example, if a doctor is prevented from accessing a patient’s test results, the patient may suffer needlessly or even die. A commercial website could lose significant sales if its website were down for an extended period.
Understanding the potential causes of system failure enables appropriate action to be taken to avoid them. There are a wide variety of potential threats to an organization’s information systems.
Human threats are the most complicated to manage because they include a wide variety of behaviors. To illustrate how the level of detail can vary, some relevant subcategories include:
Accidental behavior by members of the organization, technical support staff, and customers of the organization
Malicious behavior by someone inside or outside the organization
Other categories of threats include:
A natural event: flood, fire, tornado, ice storm, earthquake, pandemic flu
Environmental elements: chemical spill, gas line explosion.
Technical Threat: Hardware or software failure
Operational Threat: a faulty process that unintentionally compromises the confidentiality, integrity, or availability of information. For example, an operational procedure that allows application programmers to upgrade software without test or notification system operators can result in prolonged outages.
It is possible to categorize the various checks intended to avoid a failure, such as:
Management controls management processes that identify system requirements such as confidentiality, integrity, and availability of information and provide for various management controls to ensure that these requirements are met.
Operational controls: include the day-to-day processes associated with the provision of information services.
Technical controls: concern the technical capacities integrated into the IT infrastructure to support the increased confidentiality, integrity, and availability of information services.
A widely cited Gartner research report concludes that “people directly cause 80% of downtime in critical application services. The remaining 20% are caused by technological failures, environmental failure or a natural disaster”.
Often, these failures are the result of software modifications such as adding new features or misconfiguring servers or network devices.
IT professionals should ensure that system changes are prioritized and tested and that all interested parties are notified of proposed changes.
Perceptible benefits can be quantified and assigned a monetary value. Imperceptible benefits, such as more efficient customer service or improved decision making, cannot be immediately quantified but can lead to quantifiable long-term gain [4].
System performance can be measured in different ways.
Efficiency is often referred to as “doing the things right” or doing things right. Efficiency can be defined as the ratio of output to input. In other words, a company is more efficient if it produces more with the same amount of resources or if it produces the same amount of output with a lower investment of resources, or - even better - produces more with less input. In other words, the company achieves improvements in terms of efficiency by reducing the waste of resources while maximizing Productivity.
Each time an item is sold or ordered, the manager updates the quantity of the item sold in the inventory system. The manager needs to check the sales to determine which items have been sold the most and restocked. This considerably reduces the manager’s time to manage his stock (limit input to achieve the same output). So efficiency is a measure of what is produced divided by what is consumed [3].
Effectiveness is measured based on the degree achieved in achieving system objectives. It can be calculated by dividing the objectives achieved by the total of the objectives set.
Effectiveness is denoted as “doing the right thing” or doing the things necessary or right. It is possible to define effectiveness as an organization’s ability to achieve its stated goals and objectives. Typically, a business more significant is the one that makes the best decisions and can carry them out.
For example, to better meet its various customers’ needs, an organization may create or improve its products and services founded on data collected from them and information accumulated from sales activities. In other words, information systems help organizations better understand their customers and deliver the products and services that customers desire. Collecting customer data on an individual basis will help the organization provide them with personalized service.
The manager can also ask customers what kind of products and services customers would like to buy in the future, trying to anticipate their needs. With the information gathered, the manager will order the customers’ products and stop ordering unpopular products.
In what follows, we present several formulas established to measure efficiency and effectiveness resulting from the information systems use. Indeed, the impact of an information system on an organization can be assessed using financial measures.
When the information system is implemented, management will certainly want to assess whether the system has succeeded in achieving its objectives. Often this assessment is challenging to achieve. The business can use financial metrics such as Productivity, Return On Investment (ROI), net present value, and other performance metrics explained in the following:
Return on investment, denoted as a Return rate, is a financial ratio that measures the amount gained or lost compared to the amount initially invested.
An information system with a positive return on investment indicates that this system can improve its efficiency.
The advantage of using Return on investment is that it is possible to quantify the costs and benefits of introducing an information system. Therefore, it is possible to use this metric to compare different systems and see which systems can help the organization be more efficient and/or more effective.
Developing information systems that measure Productivity and control is a crucial element for most organizations. Productivity is a measure of produced output divided by required input. A higher production level for a given entry-level means greater Productivity; a lower output level for a given entry-level means lower Productivity. Values assigned to productivity levels are not always based on hours worked. Productivity may be based on the number of raw materials used, the quality obtained, or the time to produce the goods or services. According to other parameters and with other organizations in the same industry, Productivity’s value has to mean only compared to other Productivity periods.
Another measure of the SI value is the increase in profit or the growth in realized profits. For example, a mail-order company installs an order processing system that generates 7 percent growth in profits over the previous year.
Market share is the percentage of sales of a product or service relative to the overall market. If installing a new online catalog increases sales, it could help increase the company’s market share by, for example, 20 percent.
Although customer satisfaction is difficult to quantify, many companies measure their information systems performance based on internal and external feedback. Some companies use surveys and questionnaires to determine whether investments have resulted in increased customer satisfaction.
Another way to measure the value of information systems has been developed by the Gartner Group and is called the Total Cost of Ownership (TCO). This approach allocates the total costs between acquiring the technology, technical support, and administrative costs. Other costs are added to the TCO, namely: retooling and training costs. TCO can help develop a more accurate estimate of total costs for systems ranging from small computers to large mainframe systems.
The evolution of information technologies leads to the reflection on new approaches that set up more flexible, more scalable architectures to meet its agility needs. The urbanization of information systems is one such approach.
The company’s information system’s urbanization is an IT discipline consisting of developing its information system to guarantee its consistency with its objectives and business. By taking into account its external and internal constraints while taking advantage of the opportunities of the IT state of the art.
This discipline is based on a series of concepts modeled on those of the urbanization of human habitat (organization of cities, territory), concepts that have been reused in IT to formalize or model the information system.
Town planning defines rules and a coherent, stable, and modular framework, to which the various stakeholders refer for any investment decision relating to the management of the information system.
In other words, to urbanize is to lead the information systems’ continuous transformation to simplify it and ensure its consistency.
The challenges of urbanization consist of managing complexity, communicating and federating work, considering organizational constraints, and guiding technological choices.
Define and frame the objectives of the project, define the scope, develop the schedule.
Carry out the inventory, organize the work, and present the deliverables. More precisely, list the assets and map the different layers (business, functional, application, and technical):
Business Architecture
Identify “business processes”: Who does what and why? The description of the processes is done with BPMN, EPC formalisms, etc. This step is tricky and may require the use of exploration methods. However, it does improve the overall understanding and increase the possibilities for optimization
Functional architecture
Identify the “functional block”: What do we need to carry out the business processes? Here, we are based on a classic division into zones (exchanges, core business, reference data, production data, support activities, management). This step’s difficulty lies in choosing the right level of detail and remaining consistent with business processes. However, it provides a hierarchical presentation and makes it easier to break down the work.
Application Architecture
Identify the applications: How to achieve the functionalities? This step is based on a classic N-Tiers division. However, it is not easy to provide value and solutions compared to functional architecture. This stage lays the foundations for the realization (major technological choices, etc.).
System Architecture
Identify the technical components: With what and where the applications work, it is based on a classic division into technical areas (security, storage, etc.). It is not easy to make the connection between applications and servers. This step brings concrete and structuring and is essential to assess the cost of the system.
Impact on the different layers, consideration of constraints (human, material, etc.), design of costed scenarios, and arbitration of the choice of a target.
How to organize the work, frame and then refine the budgets, design and plan projects, define the support strategy, set up an organization, contributions, roles, and responsibilities of actors.
At the end of this process, a Land Use Plan (LUP) is defined. It is a report consisting of:
Summaries of the orientations chosen as well as the justifications for the options selected.
A definition of areas, neighborhoods, and blocks.
Existing and target maps (process, functional, application, and technical mapping).
Additional documents (interview reports, list of people and organizational entities, etc.)
The goal is to identify the gaps between the existing and the principles of urbanization and establish changes by describing the actions and their corresponding cost.
In practice, the urbanization process is very cumbersome to implement. On the one hand, it requires the participation of many actors in the organization, and on the other hand, the analysis is very long. As a result, needs to change, and LUP is no longer necessarily suitable.
The reasons for a successful or unsuccessful IS implementation are complex and contested by different stakeholders and from the various perspectives involved. Developers tend to focus on the system’s technical validity in terms of execution, operation, and evolution. Other qualities are often considered, such as security, maintainability, scalability, stability, and availability. All of these criteria are considered to be signs of successful IS Development.
The failure of an IS can be defined as: either the system put in place does not meet the user’s expectations or does not function properly. The reasons for failure are as divergent as the projects.
The perspective of project management, on the other hand, tends to focus on the consumption of resources. The project delivered with the initial budget and within the allotted time is considered a successful project. Nelson [9] analyzed 99 SI projects and identified 36 classic errors. He categorized these errors into four categories: process, people, product, and technology. The last category concerns the factors leading to IS failures based on the misuse of modern technologies.
The seminal article by DeLone and McLean [10] suggested that IS success should be the preeminent dependent variable for the IS domain. These researchers proposed a taxonomy of six interdependent variables to define the IS’ success as the system’s quality, the quality of information, the IS, user satisfaction, individual impact, and organizational impact.
One of the significant extensions to this proposition is the dimension of the IT department’s quality of service [11].
Either way, the use of the system is seen as a sign of its success. The IS use level is incorporated into most IS success models [11, 12]. These models show the complexity of measuring user satisfaction because, even in the same organization, some user groups may be more or less enthusiastic than others to use the new information system.
In the current global context of the covid pandemic, it appears clear that information systems that integrate web and mobile technologies can positively contribute to the monitoring of contaminated cases and therefore minimize the risks of contamination provided that users adhere to this movement for the benefit of all [13]. A truly global, rapid, and efficient decision-making process is enabled by the integration of information systems from distributed sources [14].
To conclude this introductive chapter, we present its key ideas:
Levels of information are data, information, and knowledge.
The system is an aggregated “whole” where each component interacts with at least one other system component to achieve a goal.
An information system can be defined as a set of interconnected components that gather, process, store and dispense information to support decision making and control in an organization. An IS can be seen as a socio-technical system. The technical part includes the technology and the processes, while the social part includes the people and the structure.
The role of information systems is to solve an organization’s problems concerning its information needs
A company has systems to support the different managerial levels: transaction processing systems, management information systems, decision support systems, and systems dedicated to business intelligence.
Decisions can be operational or strategic.
There are several categories of business applications: enterprise resource planning, supply chain management systems, customer relationship management systems, knowledge management systems, and business intelligence.
Among the failures that can affect IS a violation of confidentiality, integrity, and availability of information.
The controls intended to avoid the IS’s security failures include management controls, operational controls, and technical controls.
The information system’s performance can be measured according to efficiency, effectiveness, Return on investment, Productivity, customer satisfaction, etc.
Urbanizing an information system means directing its continuous transformation to guarantee its consistency
The reasons for a successful or unsuccessful implementation of an IS are complex and contested by the various stakeholders and from the various perspectives involved.
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\n\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
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\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 26th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:289,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/87572",hash:"",query:{},params:{id:"87572"},fullPath:"/profiles/87572",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()