Land area and population of people in different states of the Niger Delta, Nigeria.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"1615",leadTitle:null,fullTitle:"Advances in Quantum Theory",title:"Advances in Quantum Theory",subtitle:null,reviewType:"peer-reviewed",abstract:"The quantum theory is the first theoretical approach that helps one to successfully understand the atomic and sub-atomic worlds which are too far from the cognition based on the common intuition or the experience of the daily-life. 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Mangroves are one of the world’s most productive ecosystems. This is because they enrich coastal waters and serve as supermarket of the sea. They are globally distributed and occupy more than 150,000 km2, occur in over 123 countries and are made up of more than 73 species and/or hybrids [1, 2, 3]. Mangroves are divided into the Indo-West Pacific (IWP) and the Atlantic East Pacific (AEP) groups [4, 5]. They originated from a hot environment [6] and their distribution is influenced by meteorological events [7] such as temperature [8] and precipitation [9]. These climatic parameters influence their distribution to different habitat [10]. Although, tolerance to warm conditions dictates their distribution, they sometimes drift to temperate regions where intense cold weather threatens their survival [11]. Global warming causes mangroves to spread beyond their latitudinal limit [12]. Mangroves are largely restricted to latitudes between 30° north and 30° south. Northern extensions of this limit occur in Japan (31° 22′N°) and Bermuda (32° 20′N); southern extensions are in New Zealand (38°03′S), Australia (38° 45′S) and on the east coast of South Africa (32°59′S) [2]; while there are robust mangrove population on the western coast of Africa with mangroves in Nigeria as one of the most dominant.
\nTropical conditions are the best for mangroves, but excessive heat cause rapid evaporation leading to increase in salinity [13], which triggers the succession of salt tolerant mangrove species (e.g.
The mangrove trees conserve water resources and serve as wind breaks in many communities. Specifically, in the Niger Delta, there are several uses of mangroves by the indigenous people, these include; fire wood, building materials, medicinal products, food baskets and fishing tools etc.
\nFire wood is a major means of cooking and heating. The firewood is got mostly from the red mangrove tree stems (i.e. Rhizophora species). The trees are first cut into 0.6 m stumps and thereafter chopped into smaller pieces of wood and sold. Fire wood is the preferred cooking method in most rural areas in Nigeria. This is because the wood retains heat for long. Pieces of the wood numbering about 3–5 are gathered and placed under metal tripod stands, and lighted to cook food. The wood ash that comes out after the burning of the wood is used as soil enhancer and disease destroyer in farms when it is spread on the soil surface or on the leaves of crops. It prevents biting and chewing insect pest (grasshoppers and locust) from chewing the leaves. The fire wood is also useful in bakery, where larger wood stumps are placed underneath large ovens for baking bread.
\nThe wood is burnt completely in kiln to form charcoal that is used for outdoor cooking. Charcoal industry is a lucrative business embarked upon by many people in the Niger Delta. The charcoals is measured, put in bags and sold in the market. It is used by a large number of people for outdoor cooking especially during occasions and festivities. It is also used by road side food vendors to roast food items such as plantain, corn, bean balls, pan cakes etc.
\nThe mangrove stems are cut to make stakes. They are also used for construction and building of scaffold. The wood is sawed into different sizes and used as ply wood for building houses. The wood is tough and can be used as roofing boards for houses. However, the use of mangrove for building is restricted because of its high combustibility. Other examples of industrial building materials derived from mangrove include: thatches, bamboo, poles, boats and wooden bridges in local communities. The wood is also used as support pillars and reinforcements for locally built houses and bridges across small rivers or canals. Poles from mangrove are used to connect electric wires, which supplies electricity from one part of the town to another.
\nThe red mangrove propagule is succulent and rich in nutrients and is eaten by crabs (
Tree barks and roots are mixed with other components to produce medicinal herbs that are used to treat some ailments. The bark is chopped into small pieces and put in locally made alcohol to dissolve; lemon is added and left for some time after which it is consumed as medicinal herb for curing several ailments. The mangrove tree bark is boiled with other herbs and used to treat malaria.
\nThe mangrove swamps serve as natural fish ponds. The site is dug and surrounded by soil like an embankment with a passageway. During high tides water carrying fishes flows into the ponds, during ebb tide the water leaves and the fishes get trapped and remain in the embankment. The advantage of this fish pond is that there is a natural exchange of water from the sea, without the use of tap water. The need for external water supply is minimized because of the adjoining water body that supplies constant water to the pond.
\nThis includes timber and non-timber products. The timber products are used by the furniture and building industry. Several furniture products are derived from trees cut from the rain forest. Non-timber products include medicinal herbs and pharmaceutical products used locally to treat certain ailments.
\nMangrove forests are relaxation points for many citizens who visit the area on site seeing trips. The mangrove forest has a sweet smelling aroma that is therapeutic when one spends time in it. The sea breeze that blows and serenades the trees is a soothing balm that calms a restless nerve. Scientific research is also carried out in the area to identify numerous species found within the forest. The mangrove forest of the Niger Delta contains numerous unidentified species. The forest is a living laboratory that requires further scientific work to identify and classify the species.
\nThe mangrove forest serves as sites for libation and ancestral activities by natives who visit the area to derive some spiritual powers. Big trees are usually not cut, but allowed to grow and serves as points for libation by people that practices African traditional religion. The mangrove forest also serves as hiding place for natives during local wars.
\nThe tree bark when boiled produces dye used by the clothing industry. The red mangrove tree bark is boiled in hot water to bring out dyes made of red to brown coloration. This is then used to dye fishing net, which help to disguise and attract fishes for higher catch by fishermen.
\nThe mangrove forest is also a region rich in crude oil and gas, which has made Nigeria the largest producer of crude oil in Africa and the sixth largest in the world [28].
\nThe major threats to mangroves in the Niger Delta are oil and gas exploration, deforestation, dredging, urbanization and Invasive Nypa palm species. Oil exploration began when the first oil well was struck in Oloibiri in the Niger Delta in 1956. Since the striking of this oil well thousands of other oil wells had been drilled resulting to millions of crude oil spillages [28]. The oil spillages had lead to the constant pollution of the mangrove forest leading to the death of numerous mangrove stands [29, 30]. Additionally, the exploratory process involves different stages such as deforestation activities aimed at creating a right of way passage (ROW) for oil pipelines, building of boot camps for seismic workers within the forest, etc. leading to the truncation of wildlife activities [31]. Similarly, the use of explosives such as dynamites during exploration for crude oil also led to the death of organisms and the destruction of the forest. Indiscriminate sand dredging is high in the area and had led to the disappearance of many coastal communities because of their conversion from aquatic to a terrestrial environment for the purpose of land expansion to establish residential and industrial quarters. The mangrove forest once destroyed takes up to 15 years or more to re-vegetate as compared to the rain forest that takes 5 years to re-grow. This shows that all aspects of oil exploration are inimical to the mangroves right from the pre-exploratory, exploratory and post exploratory stages. This is because each stage of oil and gas exploration involves hydrocarbon pollution and physical destruction of the mangrove forest. Pollution impacts flora and fauna, for instance oils from spillages clog the roots of mangroves causing outright death through the suffocation of the lenticels, leaf yellowing and defoliation [31, 32]. Pollution has effect on mollusk, crustaceans, echinoderms, polychaetes, cnidarians, oysters, scallops, periwinkles and different species of fishes that inhabit the mangrove forest. Similarly, the immobility of benthic organisms predisposes them to death from pollution. Different species of crabs such as
Urbanization is also a major threat to the mangroves, this is because population explosion in Nigeria, which is the most populous country in Africa, had led to the migration of a large number of people numbering over 20 million [33] into coastal regions of the Niger Delta to establish houses. Industrialization of wetland areas leads to the urbanization of rural areas that were formerly a habitat for mangroves. Increase in anthropogenic activities around mangrove forest had resulted to the invasion by opportunistic nypa palms (
There are several species of mangroves in the Niger Delta, but the most dominant ones are the red (
Different mangrove and non-mangrove species found in mangrove swamps affected by anthropogenic activities (dredging and sand filling). (a) Nypa palm (
Dredged and sand filled mangrove forest in Buguma, Niger Delta, Nigeria. There are still some mangroves that can be seen at the foreground. On the left of the picture is the fence of a secondary school. No mangrove tree has ever grown in this area since the sand filling in 1984. The area is now occupied by weeds and other alien plant species.
The white mangroves (
Combinations of biotic and abiotic factors had made the mangroves one of the most unique, but less studied systems in the world. The problem of data gap in Africa is often cited in many literatures with little done to correct this trend. This work therefore, brings to fore the distribution and composition of mangroves and non-mangroves species in two locations in the Niger Delta to enable scientist in other regions of the world to have a better understating of the largest mangrove forest in Africa. The emphasis of mangrove study in the past has been the effect of pollution on mangrove forest, but no mention was made of species composition and distribution. This is the reason why this study is embarked upon to help bridge the data gap. This study thus intends to achieve the following objectives;
\n\n
To determine the distribution, composition and structural characteristics of mangroves
To evaluate the adaptive strategies of mangroves vis-a-vis their significance to the environment.
The Niger Delta region is situated in the southern part of Nigeria and bordered to the south by the Atlantic Ocean and to the East by Cameroon. It occupies a surface area of about 112,110 km2. It represents about 12% of Nigeria’s total surface area and it is predicted that by the year 2020 its population would have exceeded 45 million inhabitants, which is almost two third of the entire population of Nigeria (i.e. 200 million). The region is made up of nine of Nigeria’s constituent states (i.e. 37) (Table 1):
\nStates | \nLand area (km2) | \nPopulation | \nCity capital | \n
---|---|---|---|
Abia | \n4877 | \n5,106,000 | \nUmuahia | \n
Akwa Ibom | \n6806 | \n5,285,000 | \nUyo | \n
Bayelsa | \n1107 | \n2,703,000 | \nYenagoa | \n
Cross River | \n21,930 | \n4,325,000 | \nCalabar | \n
Delta | \n17,163 | \n5,681,000 | \nAsaba | \n
Edo | \n19,698 | \n4,871,000 | \nBenin | \n
Imo | \n5165 | \n5,283,000 | \nOwerri | \n
Ondo | \n15,086 | \n4,782,000 | \nAkure | \n
Rivers | \n10,378 | \n7,679,000 | \nPort Harcourt | \n
Total | \n112,110 | \n45,715,000 | \n\n |
Land area and population of people in different states of the Niger Delta, Nigeria.
Source: Adjusted from [41].
The Niger Delta region makes up 4% of Nigerian population. There is an annual growth rate of 3.5% The population of youths below 30 years (62%) far exceed that of adults of 30–69 years (36%) and older adults above 70 years (2%). The life expectancy is about 50 years. There is resurgence in population of people migrating into the mangrove forest areas to seek for habitation in the last 20 years. The consequence of this situation is the clearing of more mangrove forests.
\nMangroves in the Niger River Delta, Nigeria are the largest in Africa, and the third largest in the world. It is estimated to cover between 5000 and 8500 km3 [42]. It has a tropical monsoon climate and rainfall occurs almost all throughout the year, except November, December and January. Mean annual rainfall ranges from over 4000 mm in the coastal towns, and decreases inland to 3000 mm in the mid-delta area; and slightly less than 2400 mm in the northern parts of the region. In the north western portions including Edo and Ondo States, annual rainfall ranges from 1500 to 2000 mm, respectively. The two seasons that prevail in the Niger Delta are the wet (February–October) and the dry (November–January) seasons with a break in August, known as the “August break”. During the dry season harmattan winds also called the North East Trade winds blow particles of dust from the Sahara Desert to the coastal maritime regions in the Niger Delta. The monthly temperature ranges between 26 and 30°C. Temperatures are generally high in the region and fairly constant throughout the year. Average monthly maximum and minimum temperatures vary from 28 to 33°C and 21 to 23°C, respectively. The warmest months are February, March and early April in most parts of the Niger Delta Region. The coolest months are June through to September during the peak of rainfall during the wet season. The soil is swampy and grades from red to brown as a result of iron deposition [38]. The soil compaction ranges from 0.25–0.75 tonnes/cm, while the pH ranges from 5.0–7.0.
\nA study on species distribution was conducted between seaward and landward sites in Buguma. Along a 20 m transect running across the middle of the plot, eight equally spaced points were identified and soil samples collected and species composition and diversity indices estimated from seaward to landward locations. The soil samples were collected with a hand held augur (Germany) and placed in a black cellophane bag. Leaf samples were collected at each point and placed in an ice cooler, and sent to the laboratory for physico-chemical analysis. The different plant communities were identified by a plant taxonomist.
\nFloristic diversity, which is the percentage occurrence of mangrove species present around the forests, was determined within a 5 × 5 m2 sub-plots within a 20 × 20 m plot in Buguma and Okrika in the Niger Delta. The dbh for trees with small girth were measured with a vernier caliper at an accuracy of 0.01 cm while the stems of larger girth were measured with tapes (Forestry suppliers Inc., Jackson, MS). The tree heights were randomly measured within the plot with EC II Haglof clinometers at an accuracy of 0.1 m.
\nThe stand basal area, which is the summation of all individual basal areas per unit ground area, was calculated as described by [43]. The area of the main plot, 400 m2 (i.e. 20 × 20 m), and the area of the sub-plots, 25 m2 (i.e. 5 × 5 m) were used as the conversion factor of 1 hectare [44]. The outcome of this calculation is in [45].
\nThe importance value (IV) of the mangroves was calculated using the equations of [43]:
\nThe importance value is a quantitative parameter used to show the significance of each species within a stand, and it includes the summation of relative density, relative frequency and relative dominance.
\nThe allometric method was used to estimate the plot AGB, since biomass was an indicator of the productivity of a mangrove stand [45, 46]. This method is used for estimating tree weight from field verifiable structural indices such as diameter at breast height (dbh) and tree height (h) [46]. The amount of standing biomass in mangrove forest is a function of the systems productivity [45]. The development of site and species specific allometric relationship is best done using harvesting method [47]. But this method was not used because of its negative effect on the environment. The above ground biomass was therefore, calculated following the equations developed by [48] and presented in 4 studies of [45].
\nThis equation is the Model 1 (diameter-height-wood density) mangrove biomass regression model. The wood specific density (\n
A comprehensive physicochemical analysis of soils collected from Buguma and Okrika was done at the laboratory where standard methods were observed to analyze the parameters.
\nA representative soil sample was collected and grinded into fine particles, such that it can pass through 0.5 mm sieve and air dried. Soil samples were weighed in duplicates of 75 g and transferred to 250 ml Erlenmeyer flask. 10 ml of K2Cr2O7 solution was accurately pipetted and dispensed into each of the flasks and swirled gently to disperse the soil. 20 ml of concentrate H2SO4 was added rapidly and directing the stream into the suspension. The soil and the reagents were mixed by swirling the flask gently for 1 min. The beaker was rotated again and the flask was allowed to stand on a sheet of asbestos for about 30 min, thereafter, 100 ml of distilled water was added. Then, 3–4 drops of indicator were added and titrated with 0.5 ml of ferrous sulphate solution. As the end point is approached, a greenish caste was observed which later changed to dark green. Thereafter, ferrous sulphate was added, drop by drop until the color changed sharply from blue to red (maroon color) in reflected light against a white background. The blank titration was prepared in the same manner using the above mentioned steps but without soil to standardize the dichromate.
\nThe result was obtained using the formula of [52].
\npH meter was used to check the acidity and alkalinity of the soil in situ. Conductivity was measured in field using conductivity meter.
\nThe KH2PO4 Extraction Method was used to analyze sulphate content of the soil.
\nThe KH2PO4 Extraction Method was used to analyze sulphate content of the soil. 2 g of soil with one tea spoon of carbon black and 40 ml of extracting solution were added into 125 ml of Erlenmeyer flask, and mechanical shaker was used to shake the mixture for 30 min. The suspension was later emptied into a funnel containing Whatman No. 40 Paper to obtain a clear filtrate. The solution was stored and phosphorus was determined using Calorimetric Method [53].
\nA portion of 0.25 g of air dried sediment samples were weighed into a Teflon inset of a microwave digestion vessel and 2 ml concentrated (90%) nitric acid (Sigma-Aldrich, Dorset, UK) were added. The metals were extracted using a microwave accelerated reaction system (MARS Xpress, CEM Corporation, Matthews, North Carolina) at 1500 W power (100%), ramped to 175°C in 5.5 min, held for 4.5 min, and allowed to cool down for 1 h. The cool digest solution was filtered through the Whatman 42 filter paper and made up to 100 ml in a volumetric flask by adding de-ionized water.
\nFor the water samples, 2 ml concentrated (90%) nitric acid (Sigma-Aldrich) was added to 0.2 ml water and the volume was made up to 10 ml with de-ionized water (X 5 dilution). Metal concentrations were analyzed by inductively coupled plasma mass spectrometry (ICP MS: model X7, Thermo Electron, Winsford-Cheshire, UK).
\nAll chemicals and reagents used were of analytical grade and of highest purity possible. Analytical blanks were prepared with each batch of the digestion set and analyzed (one blank for every set of six samples) in the same way as the samples. The analytical methodologies were confirmed using certified reference materials for sandy clay (CRM 049-050, Sigma-Aldrich RTC, Salisbury).
\nMost locations in the Niger Delta have similar mangroves species composition. Some mangrove species found include:
Scientific name | \nCommon name | \nAbundance | \nProportion (Pi) | \nLn (Pi) | \nPi Ln(Pi) | \n
---|---|---|---|---|---|
Red | \n5 | \n0.21 | \n−1.561 | \n−0.328 | \n|
Red | \n8 | \n0.33 | \n−1.109 | \n−0.366 | \n|
Red | \n2 | \n0.08 | \n−2.526 | \n−0.202 | \n|
White | \n6 | \n0.25 | \n−1.386 | \n−0.347 | \n|
Black | \n3 | \n0.13 | \n−2.040 | \n−0.265 | \n|
Total | \n\n | 24 | \n\n | H | \n1.508 | \n
Shannon wiener diversity indices (H) of major mangrove species in the Niger Delta, Nigeria.
Scientific name | \nCommon name | \nAbundance | \nProportion (Pi) | \nLn (Pi) | \nP i Ln(Pi) | \n
---|---|---|---|---|---|
Nypa palm | \n5 | \n0.83 | \n−0.186 | \n−0.154 | \n|
Date palm | \n1 | \n0.17 | \n−1.772 | \n0.366 | \n|
Total | \n\n | 6 | \n\n | H | \n0.52 | \n
Diversity indices (H) of palm species commonly found around most mangrove forest in the Niger Delta, Nigeria.
Scientific name | \nCommon name | \nAbundance | \nProportion (Pi) | \nLn (Pi) | \nP i Ln(Pi) | \n
---|---|---|---|---|---|
Corn vine | \n6 | \n0.24 | \n−1.427 | \n−0.343 | \n|
Coco plum | \n4 | \n0.16 | \n−1.833 | \n−0.293 | \n|
Silt grass | \n2 | \n0.08 | \n−2.526 | \n−0.202 | \n|
Bush knife | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Christmas tree | \n3 | \n0.12 | \n−2.120 | \n−0.254 | \n|
Melastomataceae | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Sedge | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Aquatic fern | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Bush knife | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Fern | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Christmas bush | \n1 | \n0.04 | \n−3.219 | \n−0.129 | \n|
Myrtaceae | \n3 | \n0.12 | \n−2.120 | \n−0.254 | \n|
Total | \n\n | 25 | \n\n | H | \n2.249 | \n
Shannon wiener diversity indices (H) of weed species commonly found around mangrove forest in the Niger Delta, Nigeria.
Species distribution from seaward to landward areas indicates that core mangrove species were found in the seaward side, whereas the non-mangrove species were found in the landward direction.
\nThere was gradation of heavy metal concentration along the established 20 m transect. It shows that the concentration of metals from landward to seaward directions remained unchanged while Zinc (Zn) concentration along transect fluctuate.
\nNutrient contents varied along the 20 m transect from seaward to landward directions. There was an increase in sulphate (SO4) and potassium (K) content while there was a decrease in Calcium (Ca), Magnesium (Mg), Manganese (Mn) and Phosphorous (P) contents.
\nA detailed physico-chemical analysis of the study locations is presented in Tables 5 and 6.
\nStudy location | \nConductivity μs/cm | \npH | \nTOC (%) | \nP (mg/kg) | \nSO42− (mg/kg) | \nCd (mg/kg) | \nPb (mg/kg) | \nZn (mg/kg) | \nCu (mg/kg) | \nMn (mg/kg) | \nCa (mg/kg) | \nK (mg/kg) | \nMg (mg/kg) | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OK1 | \n1133 | \n5.94 | \n1.989 | \n0.07 | \n25 | \n0.06 | \n6.21 | \n4.86 | \n1.26 | \n1.52 | \n33.28 | \n54.95 | \n229.48 | \n
OK2 | \n783 | \n6.4 | \n1.716 | \n0.03 | \n28 | \n0.001 | \n0.001 | \n1.26 | \n0.001 | \n0.44 | \n45.17 | \n38.31 | \n143.73 | \n
OK3 | \n9920 | \n5.97 | \n3.315 | \n0.09 | \n60 | \n0.001 | \n0.001 | \n2.6 | \n0.001 | \n4.71 | \n36.95 | \n334.8 | \n513.2 | \n
Mean | \n3945.33 | \n6.10 | \n2.34 | \n0.06 | \n37.67 | \n0.02 | \n2.07 | \n2.91 | \n0.42 | \n2.22 | \n38.47 | \n142.69 | \n295.47 | \n
SD | \n5177.17 | \n0.26 | \n0.86 | \n0.03 | \n19.40 | \n0.03 | \n3.58 | \n1.82 | \n0.73 | \n2.22 | \n6.09 | \n166.58 | \n193.37 | \n
SE | \n2989.04 | \n0.15 | \n0.49 | \n0.02 | \n11.2 | \n0.02 | \n2.07 | \n1.05 | \n0.42 | \n1.28 | \n3.52 | \n96.18 | \n111.64 | \n
Soil physico-chemical characteristics of different mangrove forest in Okrika, Niger Delta, Nigeria. OK refers to Okrika.
Study location | \nConductivity μs/cm | \npH | \nTOC (%) | \nP (mg/kg) | \nSO42− (mg/kg) | \nCd (mg/kg) | \nPb (mg/kg) | \nZn (mg/kg) | \nCu (mg/kg) | \nMn (mg/kg) | \nCa (mg/kg) | \nK (mg/kg) | \nMg (mg/kg) | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BG1 | \n308 | \n6.53 | \n2.808 | \n0.15 | \n18 | \n1.34 | \n19.14 | \n83.97 | \n19.28 | \n51.84 | \n1149.1 | \n133.85 | \n737.35 | \n
BG2 | \n186 | \n6.83 | \n2.145 | \n0.1 | \n15 | \n0.93 | \n22.82 | \n88.55 | \n38.85 | \n62.55 | \n1156 | \n157.05 | \n715.49 | \n
BG3 | \n19,280 | \n6.58 | \n3.939 | \n0.24 | \n240 | \n0.001 | \n0.001 | \n8.4 | \n0.001 | \n4.77 | \n282.85 | \n407.4 | \n794.61 | \n
Mean | \n6591.33 | \n6.65 | \n2.96 | \n0.16 | \n91.00 | \n0.76 | \n13.99 | \n60.30 | \n19.38 | \n39.72 | \n862.65 | \n232.77 | \n749.15 | \n
SD | \n10988.9 | \n0.16 | \n0.91 | \n0.07 | \n129.05 | \n0.69 | \n12.25 | \n45.01 | \n19.43 | \n30.74 | \n502.13 | \n151.68 | \n40.86 | \n
SE | \n6344.43 | \n0.09 | \n0.52 | \n0.04 | \n74.51 | \n0.40 | \n7.07 | \n25.99 | \n11.21 | \n17.75 | \n289.91 | \n87.57 | \n23.59 | \n
Soil physico-chemical characteristics of different mangrove forest in Buguma, Niger Delta, Nigeria. BG refers to Buguma.
Stem diameter of the mangrove trees ranged from 0.01 to 16 cm.
The dominance of the red mangroves (i.e.
Baseline data on biomass will help to recognize importance of mangroves in Nigeria. Biomass differences among mangrove forests are indicator of healthy and unhealthy forest. Mangrove forest in unprotected areas seems to show unhealthy condition or fragmentation and degradation due to illegal logging and aquaculture [57, 58]. Thus, management effort of rehabilitating degraded forest must be done to improve carbon sequestration and productivity in unprotected mangroves forest.
\nFour kinds of soils found in mangrove forest in the Niger Delta include: are mud, chikoko-wet, chikoko-dry and sandy soils. Muddy soils is fine to the touch, light brown in color, wet, and mixed with litter. It can be molded into shapes because of its high plasticity and low porosity. This soil allows the growth of few weeds, and few mangrove species. The chikoko-wet is dark brown in color, rough to the touch, forms a semi mold, and often wet and has medium plasticity and low porosity. This soil is the best for the growth of red, black and white mangroves. The chikoko-dry is coffee-brown in color, rough to the touch, has particulate matter and forms no mold. It contains litter material, and has low plasticity and medium porosity. This soil does not support the growth of many plant species because of its dryness. The sandy soil is whitish to dark brown in color, rough to the touch, forms no mold, and has low plasticity, but high porosity. This soil strictly allows only grasses and other weed species grow on it. They are often found in dredged or sand filled areas.
\nMangroves have low growth in muddy soil because the soil suffocate their lenticels, which may lead to death. The case is, however, different for the weeds, which have better growth in muddy soil. A species composition study done in a sand filled area indicates that in a 20 m transect starting from the seaward to the landward direction; there was a significant difference in the number of species found. Similarly, there was a significant difference in soil physico-chemistry at eight points along the transect. The result indicates that the sandier the soil the more the number of weeds, while the swampier the soil the more the population of red mangrove trees (Table 2).
\nThe breathing root system of mangrove is built for survival in anaerobic soils. That is why the mangroves thrive in areas where other species fail that soil types influence mangrove growth. For instance, results from a fieldwork I embarked on indicates that total organic content (TOC) was higher in farm (1.99 ± 0.01%) and Nypa palm (1.87 ± 0.01%) soils than in mangrove soils (1.01–1.48%). Similarly, soil types influence the height of mangrove and nypa palm seedlings (P < 0.001), but did not influence diameter of seedlings (P > 0.05). Mangrove propagules grew best in farm soils. This shows that mangrove distribution is strongly influenced by soil types. Therefore, the more the soil type changes as a result of anthropogenic activities the more it harbors foreign species, which are non-mangroves. In addition, tidal fluctuation and soil moisture content affects the amount of organic matter in sediments [59].
\nChanges in heavy metals and nutrients can also influence the distribution of mangroves and other plant species in a wet land area. In a study carried out in dredged and sand filled site in Buguma Niger Delta, Nigeria, the result indicates that apart from zinc, which fluctuated, other heavy metals did not vary significantly along a 20 m transect from sandy to mangrove soil (P > 0.05). Mangroves play environmental role by acting as a biofilter of heavy metals [60]. Lastly, maintaining high diversity of mangroves is crucial to ensure the health and productivity of coastal zones [60].
\nThere are several adaptive features in mangroves [61] including some that are peculiar to the Niger Delta, Nigeria. The mangrove develops long root system that can easily be mistaken for a tree branch. They grow up to 3 m in height, and grow out from tree branches to the ground. This helps to provide extra support for the trees. The adventitious roots do not only grow from the base, but grow from the top of the trees to the ground. The giant roots support and provide extra surface area for atmospheric respiration during high tides when the ground roots are submerged in water. The branches hardly submerge during high tide or flooding because of the nature of the root system, which grow above the water level. The red mangrove trees are more dominant and more adapted to core mangrove soils. The red mangrove propagules have limited growth in sandy or mixed soils. They are mostly adapted to wet chikoko soil, which is slightly muddy.
\nThe red mangroves (e.g
The torpedo shape of the mangrove propagule enables it to float upright i.e. bottom down and heads up when submerged in water. This allows easy soil implantation and growth.
\nRootlets of the white mangrove trees protrude from oxygen-depleted soils like spikes to take in oxygen. This is a way of boosting their survival in a difficult and marshy environment. This characteristic is most often exhibited by the black and white mangroves, but not the red mangroves. This is because white and black mangroves are mostly found in disturbed environments, such as dump sites and sand filled areas. The stems of the red mangroves are elastic and are adapted to wear and tear. The stems and roots form a network that prevents the free movement of animals and humans within the forest. They also restrict the movement of humans and machinery during exploratory activities.
\nThe leaves of the red mangroves (
The mangrove soil is red in color and has life-saving gas that breathes life into the entire mangrove ecosystem. The soil has numerous fiber-like materials that hold and reinforce the soil against water erosion and tide. The combination of nutrients and red soil water with fibrous materials is what has made the mangrove a biodiversity hot spot. Therefore, if these qualities are destroyed as a result of human activities the red mangrove population will decline leading to succession [14] and entry of foreign species [64]. The surface of an undisturbed mangrove soil is slimy and facilitates the movement of creeping and swimming organisms such as mud skippers during low or ebb tides. The slimy and soft nature of the top soil also acts as a defensive mechanism to prevent the free movement of man and animals on the forest floor. The soil has some holes, which serve as air pockets and safe sanctuaries for threatened organisms (e.g. crabs, mudskippers).
\nA symbiotic relationship does exist between the red mangrove trees and black ants. Large number of black ants are always found on the leaves, branches and stems of trees, which serve as a source of food for the ants while the ants in turn provide protection for the tree against intruders. Termites also build huge termitarium on the tree trunks, which further provides extra security for the plants by warding off intruders and predators. The ants are entomophagous because they feed on other insects along their path. The ants also attack humans that climb to exploit the trees.
\nThe stems of the mangrove trees are very rigid and could withstand severe external impact or fracture during wind storm. It is also extremely difficult to cut down the trees with a machete. The trees are often cut with chain saw or brought down with bulldozers. The mangroves grow in groups, which gives them extra protection from wind storms. The closeness of the trees to each other also leads to the accumulation of large amount of ground litter materials that decompose to drive the nutrient cycle of the forest [14].
\nTree climbing skill is exhibited by red mangrove crabs (
The mangrove forest is rich in biodiversity and has organism such as monkeys, guinea fowl, periwinkle, mudskipper, crabs (
In addition to plant and animal resources the Niger Delta mangrove forest is rich in crude oil. Most oil and gas exploration activities do occur within the mangrove forest. These exploratory activities have decimated the mangroves in many locations, which may lead to extinction if this trend is not stopped [4, 5]. Over the years the mangroves had survived many environmental disturbances such as hydrocarbon pollution, deforestation, urbanization, and invasive species by adapting to very difficult conditions.
\nMangroves are adapted to hydrocarbon pollution: This is because series of studies and field observations have shown that mangroves growing in highly polluted plots had better structural characteristics, above ground biomass and species composition than mangrove trees growing in lowly polluted soil [45, 54]. It has been difficult to provide answers to the cause of this trend, but of recent it was discovered that the robust growth of mangroves in highly polluted plots is as a result of decomposition and nutrient cycling from excess defoliations as a result of oil and gas exploration. The reason is that oil spill leads to increase in litter fall, which covers the soil surface, and decomposes to enrich the soil. This condition leads to the proliferation of hydrocarbon utilizing bacteria, which detoxifies the soil and increase the soil fertility leading to a positive feedback such as increase in nutrient turnover. This leads to the rapid growth of mangroves in highly polluted soils. This study is supported by other studies which revealed that the rate of herbivory of crabs and insects on mangrove leave was higher on trees growing in highly polluted soils than in trees growing in lowly polluted soils.
\nMangrove of the Niger Delta, Nigeria is one of the most productive systems in terms of biodiversity, and ecosystem services in the world, but because of lack of data it is often not mentioned in many literatures. This chapter has brought to light the distribution of different species of mangroves between landward and seaward areas and the effect of soil physicochemistry on mangrove species distribution.
Magnetic resonance imaging (MRI) is a multimodal technique that can noninvasively reflect the structure and function of the human brain. Structural MRI (sMRI), including longitudinal (spin-lattice) relaxation time T1-weighted and transverse (spin-spin) relaxation time T2-weighted imaging, has been applied to investigate the structural features of the brain. Based on the different relaxation times of different tissue, T1-weighted and T2-weighted images can be used to reflect the volume of grey matter, white matter, as well as lesions caused by infarction or hemorrhage. Diffusion MRI (dMRI), such as diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI), can be used to measure water diffusion along different directions and tract neural fiber counts and orientation. Functional MRI (fMRI) reflects neural activity during a period of time by measuring the relative amount of deoxygenated hemoglobin and oxygenated hemoglobin in the blood flow, which is also called the blood-oxygen-level-dependent (BOLD) signal. The fMRI is becoming popular in clinical situations to investigate the functional alterations following disease or treatment.
The fMRI experiment can be categorized into task fMRI and resting-state fMRI (rs-fMRI). For task fMRI, subjects need to perform a specific task, such as finger tapping or receive external stimulation like heat or sound during the scanning session. Resting-state fMRI, on the other hand, is collected when the subject lies still in the scanner, without doing any movement or thinking anything particular, and keeping awake at all time. Researchers focus on the spontaneous neural activity reflected by the BOLD signal under resting conditions. The correlation of signals related to spatially distinct regions is commonly defined as functional connectivity (FC) [1].
In the recent two decades, several methods have been developed to analyze functional connectivity in the resting state, including seed-based analysis, independent component analysis (ICA) [2], and resting-state network (RSN) method [3, 4]. The network method characterizes brain spontaneous activity as a graph, where nodes are defined as brain regions and edges are represented as connectivity between regions. There are different ways to calculate the connectivity, including static and dynamic functional connectivity and directed connectivity. Furthermore, features proposed in network science can be adopted to characterize the brain network topology, such as graph theory attributes [5].
Resting-state fMRI has been applied to clinical research and applications [6, 7]. In clinical situations, a common research paradigm is performing group comparison and searching for inter-group significant different features. Researchers are interested in whether a group of patients is significantly different from a group of healthy controls, or whether the same group of patients shows significant recovery after treatment. The identified significant different features may be the potential biomarker to aid in diagnosis as well as treatment. More importantly, the location of the significant different feature is of great interest, since each brain region has its unique function. As a result, this requires comparing groups of brain networks and other extracted network features. In clinical research, there are two key techniques of brain network analysis, the method of network construction and significant difference analysis of groups of brain networks.
In the following sections, we first describe how to construct brain networks from resting-state fMRI data, including different node definitions and a range of connectivity measurements. Then, we present common group analysis methods of brain networks. The clinical application of brain network analysis is also reported. We also propose several future directions in brain network research and end the chapter with a conclusion.
Unlike structural and diffusion MRI, the fMRI scanning captures the BOLD signal in a period of time that typically lasts for several minutes. The collected data are a time series, and the “sampling period” is called repetition time (TR). That is, whole-brain data are collected every TR seconds. Before constructing brain networks, the data need to be preprocessed to clean out non-neural artifacts, including physiological signals like breath and heartbeat, head movements, and scanner noise. Then the nodes of the network are defined and connectivity between each pair of nodes is calculated. The whole data processing pipeline is shown in Figure 1.
Resting-state fMRI data process pipeline.
The preprocessing of fMRI data is necessary since there are non-neural noises in the signal. There are openly available toolboxes to carry out preprocessing, such as Statistical Parametric Mapping (SPM), FMRIB Software Library (FSL), and Data Processing Assistant for Resting-State fMRI (DPARSF)[8]. Common preprocessing procedures begin by removing the first 10 time points to let the subject be familiar with the scanning environment. Since the scanning of fMRI data within a repetition period (2s) is done in a slice-by-slice manner, the exact collection time of the first slice and the last slice has a time difference. To correct this difference, a procedure called slice timing correction needs to be performed. Then the head motion is corrected so that each voxel corresponds to the same brain location in the scanning series.
For group analysis, the data of different subjects need to be co-registered or normalized to the Montreal Neurological Institute (MNI) standard space. The data then undergoes smoothing using a Gaussian filter with a specified full-width-half-maximum (FWHM) value. After that, the linear trend in the signal is removed and nuisance covariates, such as white matter, cerebral spinal fluid (CSF), and global signal, are regressed out. At last, the data are filtered to keep signals within 0.01-0.08 Hz, since signals within this frequency range are reported to reflect spontaneous neural activities.
Although numerous preprocessing steps have been developed, there is still no consensus on the standard fMRI data preprocessing pipeline. The controversy is centered on the nuisance covariates regression, especially global signal regression (GSR)[9] and white matter signal regression [10]. Other researchers tried to optimize the preprocessing across multiple outcome measures [11], for low-frequency fluctuation analysis [12] and specific patients, such as stroke patients [13]. We have also investigated how the choices of preprocessing parameters and steps influence statistical analysis results [14]. The preprocessing of fMRI data remains to be a complex but important research topic.
The most basic node definition is the voxel in a 3D fMRI image. Each voxel within the brain can be treated as a node and the constructed voxel-based network covers the whole brain. However, since the spatial resolution of fMRI is relatively high (2mm–4mm), the number of voxels is rather large (around the magnitude of 100,000) and the constructed network requires huge computation power for further analysis. Researchers have proposed specialized methods, such as the Parallel Graph-theoretical Analysis (PAGANI) toolkit to accelerate the processing of voxel-based whole-brain networks [15].
On the other hand, the nodes of the brain network can be defined as regions in the brain. The preprocessed data of voxels within a region are averaged spatially as the signal related to this node. The region can be specified manually by drawing regions of interest (ROI). Independent component analysis (ICA) can also reveal the component region but requires specifying parameters, such as the number of components. Both methods require human intervention and depend heavily on expert knowledge.
We proposed a fuzzy node definition method in Ref. [16] for tumor-brain, named “Spatial-Neighborhood and Functional-Correlation (SNFC)” based on fuzzy connectedness. It is a self-adapting method where the network was divided into functional connection and spatial adjacency. In the SNFC method, fuzzy connectedness between two voxels acts as a measurement to decide if they belong to the same node. Each voxel in the brain could be mapped into two feature spaces—structure feature space
If
The nodes can also be defined using regions in the brain atlas to avoid the subjective error caused by human intervention and enable automatic processing for large cohorts of data. The most known brain atlas is the Brodmann atlas, created by the German anatomist Korbinian Brodmann based on cytoarchitecture [17]. Another popular brain parcellation is the Automated Anatomical Labeling (AAL) atlas [18]. The AAL atlas focuses on brain structure and the finer partition of certain cortices was proposed in AAL2 [19] and AAL3 [20]. Apart from structure, the brain atlas derived from diffusion and functional data is getting more attention. The Brainnetome Atlas was proposed based on DTI data with fine-grained parcellation [21]. Researchers also developed functional atlas, such as the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) that considered the homolog of regions in both hemispheres [22]. The above-defined network is called a region-based whole-brain network. We can also construct networks within a region. In this scenario, the voxels are defined as nodes, and the network only consists of voxels within a region. The constructed network is called a voxel-based local network, representing the topology within certain regions. We proposed a multilevel brain network joint analysis method on voxel-based whole-brain networks, voxel-based local networks as well as region-based whole-brain networks (Figure 2) [23].
Construction of multilevel functional brain networks.
Node definition has a fundamental influence on the topology of the brain network. Different atlas parcels the cerebrum and cerebellum based on different information, and it plays a key role in linking physiological regions to abstract brain network nodes. However, similar to the preprocessing of fMRI data, there is no gold standard for the node definition. Several researches have been carried out to investigate the effect of node definition on network analysis [24], resting-state networks [25], and the topology of both functional networks [26] and structural networks [27]. It is still an open question and needs more thorough research.
Edges in brain networks are represented by the connectivity between nodes. One of the most common connectivity measures is functional connectivity (FC). In 1995, Biswal et al firstly reported the correlation of intrinsic low-frequency BOLD signal fluctuation under resting-state and since then, multiple efforts have been devoted to FC analysis [1, 3]. Functional connectivity is commonly defined as the Pearson correlation between the BOLD signal of spatially distant regions. In recent years, researchers realized that FC ignores the dynamics of neural activity and developed dynamic functional connectivity (DFC) or Chronnectome [28, 29, 30]. The research on DFC is becoming popular and has attracted lots of attention.
Technically speaking, FC or static functional connectivity (SFC) is calculated using the whole time series, whereas DFC utilizes a sliding time window and the correlation of signals within the window is calculated. The window then moves from the beginning of the BOLD signal to the end, with a pre-defined step size. As a result, the connectivity shows dynamic fluctuations as the window slides, and each scanning session is associated with a series of brain networks, or a dynamic brain network. In contrast, there is only one static network related to the scanning session. The network is usually represented by a graph adjacency matrix, which is a square symmetric matrix and the
There are two major parameters regarding DFC calculation—the window length and the sliding step size. With a longer window length, the dynamics of neural activity might be averaged out while a shorter window length can capture transient signal changes. The step size controls the temporal resolution of DFC. Normally it is specified as several TRs. We investigated the optimal window width by using the small-world property as criteria [31]. Node degree distribution has exponential truncated power-law in the small-world network, and the normal human brain network shows a strong small-world property. The reasonable window width range was verified on both SNFC-based and voxel-based whole-brain networks. Results show that the smallest window width is 200 seconds and 260 seconds for normal subjects and brain tumor patients, respectively. Leonardi et al also studied the theory between window length and filter cut-off frequency during preprocessing [32]. Apart from the two window parameters, the shape of the sliding window is another concern. The rectangular window is the simplest solution, but other choices such as tapered window exist. Mokhtari et al also proposed a modulated rectangular (mRect) window to reduce spectral modulations [33].
We also proposed a dynamic network analysis method for enlarging the training samples required by an unsupervised learning classification algorithm [34], such as a classical backpropagation neural network classifier containing a hidden layer. It reached the optimal accuracy of 100% for classifying glioma patients and normal subjects.
Despite controversies, DFC has been used to investigate diseases, such as schizophrenia [35], post-traumatic stress disorder (PTSD) [36], Parkinson’s Disease [37], and autism [38]. It has also been applied to lifespan studies [39] and cognitive research [40]. From either a methodological or application view, the research on DFC is still insufficient.
As the definition implies, both SFC and DFC contain no directional information. Effective connectivity (EC) can measure the directional influence of one region toward another area by calculating the causal relationships between time series. Commonly adopted EC estimation methods are structural equation modeling (SEM) [41], dynamic causal modeling (DCM) [42], and Granger causality analysis (GCA) [43, 44]. The computation cost becomes unacceptable for SEM and DCM as the number of nodes increases [43]. Several amendments have been proposed to reduce the computation requirement of DCM recently [45, 46], but the model complexity is still challenging for clinical applications. We proposed a method based on convergent cross-mapping (CCM) that can reflect the interactions between regions in a dynamic, nonlinear, and deterministic way, which is not covered by GCA [47]. The method overview, together with the extended network-based statistic, is shown in Figure 3.
CCM-based directed connectivity estimation and extended network-based statistic method.
CCM was originally developed to detect causality in complex ecosystems [48]. It acts as a complement to GCA as CCM assumes the system to be deterministic and dynamical, while GCA works for a stochastic system and requires separability. In GCA, if removing X decreases the predictability of Y, it can be deduced that X causes Y, and in a brain network scenario, there is a directed connection from X to Y. On the other hand, in deterministic dynamic systems where CCM was developed, we can measure how well Y can estimate X to determine the causal relationship from X to Y, which then determines the directed connectivity strength from X to Y. The procedure of estimating X using Y is called cross-mapping. CCM is also applicable under situations where separability is not guaranteed. GCA, on the other hand, may produce erroneous results [49]. As for the brain, it is a dynamic system whose functional organization is poorly understood [50]. Utilizing CCM to estimate directed connectivity between regions could facilitate the investigation of brain activity as well as enable novel clinical applications.
After brain network construction, for each scanning of each subject, the preprocessed fMRI data were converted to a brain network represented by a graph adjacency matrix. The next question is how to find the difference between groups of brain networks. Here we summarize two popular methods to further analyze brain networks.
The most basic method is analyzing functional connectivity directly. Specifically, suppose we are comparing two groups of networks. Each connectivity value is extracted from every network, forming two sets of values. Statistical hypothesis testing can be adopted to decide whether this connection shows a significant difference as well as which group is higher. After performing a comparison on every connection in the network, the group difference network consisting of significant different connections is obtained. All edges with a significant difference were stored in a network for further discussion. We can also select several regions based on prior knowledge, such as the sensorimotor area or visual area, to further filter the set of significant different connections.
Another method is calculating graph theory attributes. Graph theory characterizes the topology of the network by nodal and global attributes. Common node level graph theory attributes are betweenness centrality, clustering coefficient, local efficiency, modularity, and weighted degree, while the network level graph theory attributes include global efficiency and characteristic path length. Small-worldness is also a common index used in brain network analysis. For multilevel brain networks, we define intra-region features as the attributes calculated at voxel-based local networks, and the attributes calculated at region-based whole-brain networks are called inter-region features. We can calculate the global feature of the voxel-based local network (intra-region features), and the nodal feature of the region-based whole-brain network (inter-region features). As a result, for each graph attribute, we obtain a feature vector whose length equals the number of nodes in the network, representing the whole-brain network feature.
After obtaining feature vectors of graph theory attributes, we can perform a statistical comparison on each region similar to FC analysis. The feature at each region is extracted, forming two sets of values; and statistical testing is used to find significant regions or significant different features. Moreover, the clinical relevance of the features can be evaluated by assessing the correlation of features and clinical scores, which produces features with significant correlation. The intersection of significant different and significant correlated features is selected for further discussion and following analysis.
We also investigated methods to analyze dynamic graph theory attributes [51]. For dynamic brain networks, at each sliding window location, the obtained brain network is static, and graph theory attributes can be calculated. As the window slides, graph theory attributes at each window location are estimated, forming the dynamic graph theory attributes of the dynamic network. To combine static and dynamic attributes together with clinical scores, we proposed an analysis framework [51]. The strength and stability of dynamic graph attributes were calculated. We found significant different and correlated features for both static and dynamic networks, as well as their intersection. The resulting features were further analyzed using receiver-operating curves (ROC) to test their ability in classification.
A controversy regarding the above analysis method is the multiple comparison problem. For each single statistical comparison with a 0.05 significance level, there is a 0.05 chance of obtaining a false positive. However, when performing multiple statistical comparisons at the same time, the chance of getting at least one false positive would become higher as the number of comparisons increases. To tackle this problem, correction methods, such as Bonferroni correction and false discovery rate (FDR) correction, were proposed. The basic idea behind these correction methods is to decrease the single comparison significance level according to the number of comparisons. However, since the amount of comparison is related to the number of nodes in the network, and certain features show high within group variance, directly applying correction might result in no significant result. We argue that statistical comparison can be seen as a feature selection procedure. The significant or selected features are then fed into the next module, such as a classifier. During feature selection, we should keep as much useful information as possible. The uncorrected significant features are preliminary scanning results and taking the intersection of significant different and correlated features further select clinically relevant information. Searching for intersected significant features might be an alternative method to multiple comparison correction.
For brain networks, to overcome the multiple comparison issue, network-based statistics (NBS) was proposed, enabling direct comparison of groups of brain networks [52]. NBS assumes that the effect or the group difference forms a certain structure instead of distributed single connections. The edge-wise comparison is performed first and the links are thresholded according to the test statistics or p-values obtained from the edge-wise comparison, producing a binarized difference network. It then searches for structures or connected components in the binarized difference network. The size of the component, defined as the number of edges or nodes, is used to determine if the component is significant by a permutation test, where group labels of samples are randomly shuffled and the same procedure is performed to search for the maximum component size. The permutation is repeated 5000 times and the empirical distribution of the component size is obtained. An empirical p-value can be assigned to the original connected component by calculating the ratio of the number of permutations, where the maximal size is larger than the original size, to the total permutation number.
Compared with edge-wise comparison and direct edge-wise correction, NBS provides higher statistical power at the cost of coarser spatial resolution in detecting differences [52]. In other words, NBS can only declare the connected component as a whole to be significant. It draws no conclusion on the significance of each single connection within the component. However, the original NBS only works for symmetric adjacency matrices, which corresponds to functional connectivity.
Based on directed connectivity, we proposed the extended-NBS (e-NBS) to search for altered connected components in groups of directed networks [47]. The method overview is shown in Figure 3. We search for strongly connected components (SCC) and weakly connected components (WCC) with and without direction information. A classical depth-first search algorithm was adopted when searching for SCCs and WCCs. The edge-wise p-value was utilized to filter for candidate connections and construct a difference network. Since there is no consensus on how to choose the pre-defined p-value threshold, we changed it within a certain range to test method performance. Specifically, an edge is kept if the p-value is less than the pre-define p-value threshold. For edge-wise comparison, we also tried to use two-sample t-test and the non-parametric Mann-Whitney test. The e-NBS method, together with the CCM-based directed connection estimation method, was verified using a dataset of spinal cord injury patients and healthy controls.
Moreover, we note that given the framework of e-NBS, one can define connected components that suit research needs. For example, in a study of motor function alteration following spinal cord injury, researchers are interested in connections related to sensorimotor areas and visual regions. The connected component can be defined as significant different connections related to these regions of interest. Furthermore, we can define two-step connected components that comprise connections directly related to the ROIs in the binarized difference network, and connections related to regions (first level nodes) that connect with ROIs (Figure 4). Either way, the permutation test in e-NBS makes it possible to draw conclusion on the significance of the defined component.
Two-step connected component. The first level node is directly connected to the ROI in the binarized difference network, while the second level node is connected with the first-level node.
The resting-state fMRI has been applied to clinical research and applications, mainly investigating pathophysiological mechanisms and searching for sensitive biomarkers for early diagnosis [6, 7]. The prognosis predictability of rs-fMRI is intriguing as well [53, 54, 55]. In glioma research, resting-state fMRI has also shown potential in diagnosis and treatment planning. Here we introduce three examples of applications and related works.
It has been shown that changes in both brain function and structure occur following central nervous lesions, such as spinal cord injury [56] and cerebral stroke [57]. According to the theory of neuroplasticity, the brain function continues to change during rehabilitation, and it is the theoretical and physiological basis for individualized neurorehabilitation as well as assistive rehabilitation technologies, such as transcranial direct current stimulation (tDCS) [58, 59, 60] and brain-computer interfaces (BCI) [61, 62]. We performed a study on spinal cord injury patients and investigated the alteration of grey matter volume extracted from structural MRI and functional connectivity related to the sensorimotor area, combining clinical assessments [63]. We found that that the alteration of anatomical structure features and the brain network connectivity in the sensorimotor area were non-concomitant following spinal cord injury, and the functional connectivity within the sensorimotor area had a significant correlation with clinical sensory scores, indicating the potential of FC as a prediction biomarker.
Another issue related to neurorehabilitation is the automated objective evaluation of rehabilitation progress. Traditionally, patient recovery is assessed by clinical measurements, which can only reflect behavioral improvements and might include subjective bias. We proposed a distance-based rehabilitation evaluation method that takes resting-state fMRI data of patients and healthy controls as input (Figure 5) [64]. We hypothesize that the sample point distribution of patients and healthy controls in the feature space is dichotomous. A support vector machine (SVM) classifier was first trained using significantly different functional connectivity of healthy controls and the first scanning session of patients. The distance of the patient sample points to the separating hyperplane was calculated and used to evaluate patient recovery. If the patient recovered, the sample point of the patient would move toward healthy controls and the distance would decrease. The method was verified using both group level and individual longitudinal data, and the distance evaluation was consistent with clinical measurements.
Method overview of the distance-based rehabilitation evaluation framework.
On the other hand, a stroke could lead to certain movement disabilities. Motor-related brain function alteration after stroke and during recovery is of great interest. Brain-Computer Interface (BCI) systems are helpful in motor recovery, possibly by stimulating neuroplasticity following brain activity [65]. The brain network reorganization of stroke patients after BCI training is of great significance. We conducted an experiment to investigate the functional changes after BCI training and their relations to clinical scores [66]. Functional connectivity was calculated using data collected before and after training and we searched for significant increased FC in groups with and without BCI training. The correlation between FC and clinical scores was also calculated. We found increased FC between certain cerebral and subcortical regions and the inter-hemisphere FC was positively correlated with motor scores.
Multiple system atrophy (MSA) is a neurodegenerative disease typically characterized by parkinsonism, cerebellar ataxia syndrome, and autonomic nervous dysfunction [67]. It is further divided into two subtypes, MSA with predominant parkinsonism (MSA-P) and MSA with predominant cerebellar ataxia (MSA-C) [67]. Previous studies mainly investigated the structural abnormalities related to MSA patients and compared subtypes of MSA with Parkinson’s Disease (PD) as well as healthy controls [68, 69, 70, 71]. The functional alteration induced by MSA is also studied by calculating regional homogeneity (ReHo) [72], the amplitude of low-frequency fluctuations (ALFF) [73], as well as functional and effective connectivity [74, 75].
The dynamic functional features of MSA-C patients not thoroughly investigated before. We conducted an experiment on MSA-C patients and proposed a method to combine static and dynamic functional connectivity features, as well as clinical scores (Figure 6) [51]. The static and dynamic brain networks were constructed using methods described in section 2.3 and static and dynamic graph theory attributes were calculated. Statistical comparisons and correlation analysis were carried out and significant different and correlated features were found. The significant regions mainly covered the cerebellum and certain cerebral areas, which is consistent with prior knowledge. The dynamic features showed the highest area under the curve (AUC) value during receiver-operating characteristic (ROC) analysis, indicating the potential of dynamic features in disease diagnosis.
The coalition analysis of rs-fMRI data combining static, dynamic functional connectivity as well as clinical information.
Apart from structural and functional analysis, multimodal research on MSA is getting more attention. We also tried to combine structural, diffusion, tractography, and functional features extracted from T1, DTI, and fMRI to search for sensitive biomarkers for MSA-C patient diagnosis (Figure 7) [76]. The T1 data were processed to produce grey matter and white matter probability maps. We performed tractography on DTI data and counted the number of tracts crossing each brain region. The fraction anisotropy (FA) and mean diffusivity (MD) maps were also obtained. For rs-fMRI, we calculated functional connectivity and constructed brain networks. The extended network-based statistics for the undirected network were adopted to search for significant different connected components between the two groups. By using the AAL atlas, feature maps extracted from different modalities were converted to feature vectors and networks. After that, significant analysis was performed with false discovery rate correction and we identified significant different features, mainly distributed in cerebellar and certain cerebral regions. The correlation of these features with clinical scores was also tested. We also searched for sensitive biomarkers in disease diagnosis by applying a nested leave-one-out cross-validation framework and evaluated classification performance using the significant features of each region with a support vector machine (SVM) classifier, as shown in Figure 7. The identified biomarkers were mainly cerebellar regions. Different modalities contain complementary information. Merging multimodal data and clinical variables together can further reveal the neurological alteration related to the disease as well as increase the accuracy, robustness, and generalization of the disease diagnosis algorithm.
The multimodal MRI feature fusion framework and the nested leave-one-out cross-validation procedure. GMV: grey matter volume; WMV: white matter volume; FA: fractional anisotropy; MD: mean diffusivity; NBS: network-based statistics; LOOCV: leave-one-out cross-validation.
Glioma stems from the canceration of neurogliocyte and is the most common tumor in the human brain [77]. It has an intensive impact on the structure of the brain and further on the corresponding physiological functions. Different locations of the glioma will result in different functional alterations and prognosis outcomes. For a high-level glioma, it is highly likely to relapse even after being excised in a surgery [78]. As a result, it is necessary to analyze the brain function changes according to the location and volume of glioma for both diagnosis and treatment. We proposed a framework of multilevel functional network analysis to find the functional network characteristics of glioma patients [79]. The multilevel network consists of a hemisphere functional network, glioma voxel local network, and glioma region local network, as illustrated in Figure 8. The hemisphere functional network was constructed based on regions from a single hemisphere in the AAL atlas excluding cerebellar parcellation (Figure 9). The glioma voxel local network is constructed at the voxel level in the region of glioma that is extracted by a tumor segmentation method. And glioma region local network is also constructed at the voxel level, but within each atlas region containing the glioma. A ratio, defined as the number of voxels in an AAL area that belongs to the segmented glioma region over the total voxel number of the area, is used as the threshold for selecting areas containing the glioma in the AAL atlas.
A framework of multilevel functional network analysis for finding the functional network characteristics of glioma patients.
The process of the construction of the hemisphere functional networks is based on the AAL atlas of a glioma patient. The green dots stand for the nodes of the functional network. The yellow line segments represent the weighted edges whose thickness reflects the weight. The colored area shows the tumor region and different colors reflect the possibility of whether a voxel belongs to the tumor.
Network features, including connectivity strength, characteristic path length, average nodal betweenness centrality, and average nodal clustering coefficient, were calculated for all networks. The network connectivity strength was defined as the average z-scores of all edges. Network characteristic path length equals the average of shortest paths between each pair of nodes in the network. Nodal attributes, including betweenness centrality and clustering coefficients, are calculated at each node within the network and averaged as network features. For hemisphere functional networks, both static and dynamic functional connectivity were investigated. Since the period of the BOLD signal induced by the hemodynamic response of neuronal activity is about 20s [80], during the reconstruction of dynamic networks, a sliding window with a length of 50s and a step size of 10s was selected. Each glioma patient received functional scanning lasting for 460s. As a result, the sliding time window extracted 46 sub-signals with a length of 50s and constructed dynamic brain networks with 46-time slices.
In this study, 38 patients with tumors in one side of the brain were enrolled. We constructed 38 positive and 38 negative hemisphere functional networks. Among these patients, 15 subjects had glioma area segmentation. Moreover, 15 healthy subjects were collected as the control group. The local network analysis was performed on 15 patients with segmentation and 15 healthy controls. We used the two-sample t-test to evaluate the significant difference of each feature between hemisphere functional networks constructed on the healthy side and the glioma side. The glioma voxel local networks and glioma region local networks were constructed at the same location of glioma segmentation in data collected from healthy controls as well. Statistical comparison was performed to compare network features of glioma voxel local networks and glioma region local networks from patients and healthy controls. There were 41 glioma region local networks constructed from 15 patients, and for comparison, 41 local networks were estimated from healthy controls.
We also investigated the classification performance using hemisphere functional networks. Given that the sample size is small (38 networks with glioma and 38 networks with healthy tissue), linear support vector machine (SVM) was chosen as the classifier. Static and dynamic network features were extracted and aligned into a feature vector of dimensions 4 and 184 (
Results revealed by the multilevel functional network analysis method showed that the existence of glioma changed certain features of the normal functional networks. Our work finds that glioma weakened the connection strength of the global and local functional networks. Moreover, it decreased the clustering degree of the nodes in both local functional networks, indicating that glioma may destruct the non-randomness and the small-world property of brain networks.
Previous studies have already investigated how glioma alters functional connectivity [80, 81, 82, 83]. We find that glioma attenuates the connectivity of functional networks, which is in accordance with previous studies. Moreover, we also involved network features other than connectivity. Our study emphasized the characteristic features, such as betweenness centrality, clustering coefficient, and characteristic path length, which were not covered by previous research.
Despite progress in recent years, there are lots of work to be done in developing new methods for constructing and analyzing brain networks, as well as performing group and individualized analysis. In this section, we propose some possible directions in the field of brain network research.
Network science has been used to analyze brain networks and advanced methods need to be developed to characterize the topological features of brain networks. The algebraic topological data analysis (TDA) method provides a new way to analyze the interactions between a set of nodes instead of bilateral connections. TDA could act as a complement to graph theoretical analysis in describing the topology characteristic of brain networks. More advanced network theory concepts, such as algebraic topology, have also been introduced to the analysis of brain networks [5]. Moreover, artificial neural networks and deep learning methods have been shown to be powerful in analyzing graph data. On the one hand, before network construction, models, such as Recurrent Neural Network (RNN) and Transformer, that were originally proposed to process sequential data, such as natural language and voice, can be applied to analyze the BOLD time series, both with and without preprocessing. Since the network perspective mainly models the inter-relationships between signals of spatially distinct regions, applying deep learning models directly to the time series could possibly extract information complementary to statistical dependency, as described by functional connectivity. On the other hand, after constructing brain networks using functional connectivity, directed connectivity, or DTI fiber tracking, Graph Neural Network (GNN) or Graph Convolutional Network (GCN) could be utilized to merge these multimodal networks and combine both edge-wise features (connections) and nodal features, such as graph theory attributes. GNN was proposed to directly analyze graphs that can model relationships between nodes and perform inference on node, edge, or graph level. Applying GNN to brain networks, especially multilevel static and dynamic brain networks, could possibly extract useful features and enable multimodal information fusion.
On the application side, multiple group comparison methods have been developed. However, for clinical application, individualized diagnosis and treatment are crucial. How to transform conclusions derived from group research into individual situations is a challenging question. We define “healthy templates” as a set of methods to delineate characteristics of a healthy population. The healthy templates describe the distribution of features of healthy people and need to be built for each feature extracted from different modalities. In its most basic form, the healthy template can be a value range given a specific feature. Subjects whose feature value falls within this value range would be considered to be normal, similar to the interpretation of a blood test result. Open-source datasets are valuable resources in the construction of healthy templates. However, the site effect of MRI data is a crucial issue and multi-site data harmonization techniques need to be adopted when combining data from different scanning locations. Several methods have been proposed for harmonization but their utility remains to be tested [84, 85]. With low variance healthy templates, individualized precise treatment planning and prognosis prediction would become possible.
The human brain is modeled as a functionally inter-connected network. Resting-state functional magnetic resonance imaging enables observing brain spontaneous activity
This work was supported by a grant from the Tsinghua University Initiative Scientific Research Program (No. 20131089382) and the National Natural Science Foundation of China (No. 61171002, 60372023). We would like to thank Zexuan Hao and Ziliang Zhang from the Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University for useful advice during the experiment and manuscript development.
The authors declare no conflict of interest.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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\n\n2. SUBMIT YOUR MANUSCRIPT
\n\nAfter approval, you will proceed in submitting your full-length manuscript. 50-130 pages for compacts, 130-500 for Monographs & Edited Books.Your full-length manuscript must follow IntechOpen's Author Guidelines and comply with our publishing rules. Once the manuscript is submitted, but before it is forwarded for peer review, it will be screened for plagiarism.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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Here, the main purpose of cooperative control protocol is to track a time-variant reference trajectory while maintaining a desired formation. The reference trajectory is defined to a leader, which has at least one information connection with one of the agents in the network. The design procedure includes a robust adaptive law for estimating the unknown nonlinear terms of each agent’s dynamics in a model-free format, that is, without the use of any regressors. Moreover, an observer is designed to have an approximation on the values of control parameters for the leader at the agents without connection to the leader. The entire design procedure is analysed successfully for the stability using Lyapunov stability theorem. Finally, the simulation results for the application of the proposed method on a network of nonholonomic wheeled mobile robots (WMR) are presented. Desirable leader-following tracking and geometric formation control performance have been successfully demonstrated through simulated group of wheeled mobile robots.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Muhammad Nasiruddin bin Mahyuddin and Ali Safaei",authors:[{id:"204129",title:"Dr.",name:"Muhammad Nasiruddin",middleName:null,surname:"Mahyuddin",slug:"muhammad-nasiruddin-mahyuddin",fullName:"Muhammad Nasiruddin Mahyuddin"},{id:"206489",title:"Mr.",name:"Ali",middleName:null,surname:"Safaei",slug:"ali-safaei",fullName:"Ali Safaei"}]},{id:"55754",doi:"10.5772/intechopen.69356",title:"Resilience Enhancement in Cyber-Physical Systems: A Multiagent-Based Framework",slug:"resilience-enhancement-in-cyber-physical-systems-a-multiagent-based-framework",totalDownloads:1404,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"The growing developments on networked devices, with different communication platforms and capabilities, made the cyber-physical systems an integrating part of most critical industrial infrastructures. Given their increasing integration with corporate networks, in which the industry 4.0 is the most recent driving force, new uncertainties, not only from the tangible physical world, but also from a cyber space perspective, are brought into play. In order to improve the overall resilience of a cyber-physical system, this work proposes a framework based on a distributed middleware that integrates a multiagent topology, where each agent is responsible for coordinating and executing specific tasks. In this framework, both physical and cyber vulnerabilities alike are considered, and the achievement of a correct state awareness and minimum levels of acceptable operation, in response to physical or malicious disturbances, are guaranteed. Experimental results collected with an IPv6-based simulator comprising several distributed computational devices and heterogeneous communication networks show the relevance and inherent benefits of this approach.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Fábio Emanuel Pais Januário, Joaquim Leitão, Alberto Cardoso and\nPaulo Gil",authors:[{id:"202364",title:"Ph.D. Student",name:"Fábio",middleName:null,surname:"Januário",slug:"fabio-januario",fullName:"Fábio Januário"},{id:"203414",title:"Prof.",name:"Alberto",middleName:null,surname:"Cardoso",slug:"alberto-cardoso",fullName:"Alberto Cardoso"},{id:"203415",title:"Prof.",name:"Paulo",middleName:null,surname:"Gil",slug:"paulo-gil",fullName:"Paulo Gil"},{id:"206481",title:"MSc.",name:"Joaquim",middleName:null,surname:"Leitão",slug:"joaquim-leitao",fullName:"Joaquim Leitão"}]},{id:"56255",doi:"10.5772/intechopen.69844",title:"Time Critical Mass Evacuation Simulation Combining A Multi- Agent System and High-Performance Computing",slug:"time-critical-mass-evacuation-simulation-combining-a-multi-agent-system-and-high-performance-computi",totalDownloads:1393,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter presents an application of multi-agent systems to simulate tsunami-triggered mass evacuations of large urban areas. The main objective is to quantitatively evaluate various strategies to accelerate evacuation in case of a tsunami with a short arrival time, taking most influential factors into account. Considering the large number of lives in fatal danger, instead of widely used simple agents in 1D networks, we use a high-resolution model of environment and complex agents so that wide range of influencing factors can be taken into account. A brief description of the multi-agent system is provided using a mathematical framework as means to easily and unambiguously refer to the main components of the system. The environment of the multi-agent system, which mimics the physical world of evacuees, is modelled as a hybrid of a high-resolution grid and a graph connecting traversable spaces. This hybrid of raster and vector data structures enables modelling large domain in a scalable manner. The agents, which mimic the heterogeneous crowd of evacuees, are composed of different combinations of basic constituent functions for modelling interaction with each other and environment, decision-making, etc. The results of tuning and validating of constituent functions for pedestrian-pedestrian, car-car and car-pedestrian interactions are presented. A scalable high-performance computing (HPC) extension to address the high-computational demand of complex agents and high-resolution model of environment is briefly explained. Finally, demonstrative applications that highlight the need for including sub-meter details in the environment, different modes of evacuation and behavioural differences are presented.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Leonel Aguilar, Maddegedara Lalith and Muneo Hori",authors:[{id:"203530",title:"Prof.",name:"Maddgedara",middleName:null,surname:"Lalith",slug:"maddgedara-lalith",fullName:"Maddgedara Lalith"},{id:"204168",title:"Dr.",name:"Leonel",middleName:null,surname:"Aguilar",slug:"leonel-aguilar",fullName:"Leonel Aguilar"},{id:"204169",title:"Prof.",name:"Muneo",middleName:null,surname:"Hori",slug:"muneo-hori",fullName:"Muneo Hori"}]},{id:"56327",doi:"10.5772/intechopen.69845",title:"Motion Coordination Problems with Collision Avoidance for Multi-Agent Systems",slug:"motion-coordination-problems-with-collision-avoidance-for-multi-agent-systems",totalDownloads:1393,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter studies the collision avoidance problem in the motion coordination control strategies for multi-agent systems. The proposed control strategies are decentralised, since agents have no global knowledge of the goal to achieve, knowing only the position and velocity of some agents. These control strategies allow a set of mobile agents achieve formations, formation tracking and containment. For the collision avoidance, we add a repulsive vector field of the unstable focus type to the motion coordination control strategies. We use formation graphs to represent interactions between agents. The results are presented for the front points of differential-drive mobile robots. The theoretical results are verified by numerical simulation.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Jesús Santiaguillo-Salinas and Eduardo Aranda-Bricaire",authors:[{id:"16943",title:"Prof.",name:"Eduardo",middleName:null,surname:"Aranda-Bricaire",slug:"eduardo-aranda-bricaire",fullName:"Eduardo Aranda-Bricaire"},{id:"204125",title:"Ph.D.",name:"Jesús",middleName:null,surname:"Santiaguillo-Salinas",slug:"jesus-santiaguillo-salinas",fullName:"Jesús Santiaguillo-Salinas"}]}],mostDownloadedChaptersLast30Days:[{id:"56573",title:"Introductory Chapter: Multi-Agent Systems",slug:"introductory-chapter-multi-agent-systems",totalDownloads:2383,totalCrossrefCites:4,totalDimensionsCites:8,abstract:null,book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Jorge Rocha, Inês Boavida-Portugal and Eduardo Gomes",authors:[{id:"145918",title:"Ph.D.",name:"Jorge",middleName:null,surname:"Rocha",slug:"jorge-rocha",fullName:"Jorge Rocha"},{id:"214969",title:"Prof.",name:"Inês",middleName:null,surname:"Boavida-Portugal",slug:"ines-boavida-portugal",fullName:"Inês Boavida-Portugal"},{id:"214971",title:"MSc.",name:"Eduardo",middleName:null,surname:"Gomes",slug:"eduardo-gomes",fullName:"Eduardo Gomes"}]},{id:"55920",title:"Hybrid Architecture to Support Context‐Aware Systems",slug:"hybrid-architecture-to-support-context-aware-systems",totalDownloads:1623,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Any system that is said to be context‐aware is capable of monitoring continuously the surrounding environment, that is, capable of prompt reaction to events and changing conditions of the environment. The main objective of a context‐aware system is to be continuously recognizing the state of the environment and the users present, in order to adjust the environment to an ideal state and to provide personalized information and services to users considering the user profile. In this chapter, we describe an architecture that relies on the incorporation of intelligent multi‐agent systems (MAS), sensor networks, mobile sensors, actuators, Web services and ontologies. We describe the interaction of these technologies into the architecture aiming at facilitating the construction of context‐aware systems.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Maricela Bravo, José A. Reyes‐Ortiz, Leonardo Sánchez‐Martínez\nand Roberto A. Alcántara‐Ramírez",authors:[{id:"204084",title:"Dr.",name:"Maricela",middleName:null,surname:"Bravo",slug:"maricela-bravo",fullName:"Maricela Bravo"},{id:"204085",title:"Dr.",name:"Alejandro",middleName:null,surname:"Reyes",slug:"alejandro-reyes",fullName:"Alejandro Reyes"},{id:"204086",title:"Dr.",name:"Leonardo",middleName:null,surname:"Sánchez",slug:"leonardo-sanchez",fullName:"Leonardo Sánchez"},{id:"204087",title:"MSc.",name:"Roberto",middleName:null,surname:"Alcántara-Ramírez",slug:"roberto-alcantara-ramirez",fullName:"Roberto Alcántara-Ramírez"}]},{id:"56184",title:"Multiagent Systems in Automotive Applications",slug:"multiagent-systems-in-automotive-applications",totalDownloads:1758,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The multiagent systems have proved to be a useful tool in the design of solutions to problems of distributed nature. In a distributed system, it is possible that the data, the control actions or even both, be distributed. The concept of agent is a suitable notion for capturing situations where the global knowledge about the status of a system is complex or even impossible to acquire in a single entity. In automotive applications, there exist a great number of scenarios of distributed nature, such as the traffic coordination, routes load balancing problems, traffic negotiation among the infrastructure and cars, to mention a few. Even more, the autonomous driving features of the new generation of cars will require the new methods of car to car communication, car to infrastructure negotiation, and even infrastructure to infrastructure communication. This chapter proposes the application of multiagent system techniques to some problems in the automotive field.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Raul Campos‐Rodriguez, Luis Gonzalez‐Jimenez, Francisco\nCervantes‐Alvarez, Francisco Amezcua‐Garcia and Miguel\nFernandez‐Garcia",authors:[{id:"178524",title:"Dr.",name:"Raul",middleName:null,surname:"Campos-Rodriguez",slug:"raul-campos-rodriguez",fullName:"Raul Campos-Rodriguez"},{id:"204584",title:"Dr.",name:"Francisco",middleName:null,surname:"Cervantes Alvarez",slug:"francisco-cervantes-alvarez",fullName:"Francisco Cervantes Alvarez"},{id:"204586",title:"Mr.",name:"Francisco",middleName:null,surname:"Amezcua Garcia",slug:"francisco-amezcua-garcia",fullName:"Francisco Amezcua Garcia"},{id:"204587",title:"BSc.",name:"Miguel",middleName:null,surname:"Fernandez Garcia",slug:"miguel-fernandez-garcia",fullName:"Miguel Fernandez Garcia"},{id:"207383",title:"Dr.",name:"Luis",middleName:null,surname:"Gonzalez-Jimenez",slug:"luis-gonzalez-jimenez",fullName:"Luis Gonzalez-Jimenez"}]},{id:"56297",title:"Multiagent System for Image Mining",slug:"multiagent-system-for-image-mining",totalDownloads:1361,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Nicksson Ckayo Arrais de Freitas and Marcelino Pereira dos Santos\nSilva",authors:[{id:"203201",title:"B.Sc.",name:"Nicksson",middleName:"Ckayo Arrais",surname:"De Freitas",slug:"nicksson-de-freitas",fullName:"Nicksson De Freitas"},{id:"203204",title:"Dr.",name:"Marcelino",middleName:null,surname:"Silva",slug:"marcelino-silva",fullName:"Marcelino Silva"}]},{id:"56255",title:"Time Critical Mass Evacuation Simulation Combining A Multi- Agent System and High-Performance Computing",slug:"time-critical-mass-evacuation-simulation-combining-a-multi-agent-system-and-high-performance-computi",totalDownloads:1392,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter presents an application of multi-agent systems to simulate tsunami-triggered mass evacuations of large urban areas. The main objective is to quantitatively evaluate various strategies to accelerate evacuation in case of a tsunami with a short arrival time, taking most influential factors into account. Considering the large number of lives in fatal danger, instead of widely used simple agents in 1D networks, we use a high-resolution model of environment and complex agents so that wide range of influencing factors can be taken into account. A brief description of the multi-agent system is provided using a mathematical framework as means to easily and unambiguously refer to the main components of the system. The environment of the multi-agent system, which mimics the physical world of evacuees, is modelled as a hybrid of a high-resolution grid and a graph connecting traversable spaces. This hybrid of raster and vector data structures enables modelling large domain in a scalable manner. The agents, which mimic the heterogeneous crowd of evacuees, are composed of different combinations of basic constituent functions for modelling interaction with each other and environment, decision-making, etc. The results of tuning and validating of constituent functions for pedestrian-pedestrian, car-car and car-pedestrian interactions are presented. A scalable high-performance computing (HPC) extension to address the high-computational demand of complex agents and high-resolution model of environment is briefly explained. Finally, demonstrative applications that highlight the need for including sub-meter details in the environment, different modes of evacuation and behavioural differences are presented.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Leonel Aguilar, Maddegedara Lalith and Muneo Hori",authors:[{id:"203530",title:"Prof.",name:"Maddgedara",middleName:null,surname:"Lalith",slug:"maddgedara-lalith",fullName:"Maddgedara Lalith"},{id:"204168",title:"Dr.",name:"Leonel",middleName:null,surname:"Aguilar",slug:"leonel-aguilar",fullName:"Leonel Aguilar"},{id:"204169",title:"Prof.",name:"Muneo",middleName:null,surname:"Hori",slug:"muneo-hori",fullName:"Muneo Hori"}]}],onlineFirstChaptersFilter:{topicId:"526",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:18,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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She has been a faculty member at the University of California, Riverside in the School of Education since 2016. Her research focuses on translational studies to explore the reward system in ASD, as well as how anxiety contributes to social challenges in ASD. She also investigates how behavioral interventions affect neural activity, behavior, and school performance in children with ASD. She is also involved in the diagnosis of children with ASD and is a licensed clinical psychologist in California. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. 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