\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n
\r\n\tGenerally feed additives could be divided into five groups: \r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics. \r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour. \r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production. \r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production. \r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). 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1. Introduction
Phylogenetic inference is one of the central problems in computational biology. It consists in finding the best tree that explains the evolutionary history of species from a given dataset. Various phylogenetic reconstruction methods have been proposed in the literature. Most of them use one optimality criterion (or objective function) to evaluate possible solutions in order to determine the best tree. On the other hand, several researches (Huelsenbeck, 1995; Kuhner & Felsenstein, 1994; Tateno et al., 1994) have shown important differences in the results obtained by applying distinct reconstruction methods to the same input data. Rokas et al. (2003) pointed out that there are several sources of incongruity in phylogenetic analysis: the optimality criterion employed, the data sets used and the evolutionary assumptions concerning data. In other words, according to the literature, the selection of the reconstruction method has a great inuence on the results.
In this context, a multi-objective approach can be a relevant contribution since it can search for phylogenies using more than one criterion and produce trees which are consistent with all employed criteria. Recently, Handl et al. (2006) discussed the current and future applications of multi-objective optimization in bioinformatics and computational biology problems. Poladian & Jermiin (2006) showed how multi-objective optimization can be used in phylogenetic inference from various conicting datasets. The authors highlighted that this approach reveals sources of such conicts and provides useful information for a robust inference. Coelho et al. (2007) propose a multi-objective Artificial Immune System (De Castro & Timmis, 2002) approach for the reconstruction of phylogenetic trees. The developed algorithm, called omniaiNet, was employed to find a set of Pareto-optimal trees that represent a trade-off between the minimum evolution (Kidd & Sgaramella, 1971) and the least-squares criteria (Cavalli-Sforza & Edwards, 1967). Compared to the tree found by Neighbor Joining (NJ) algorithm (Saitou & Nei, 1987), solutions obtained by omni-aiNet have better minimum evolution and least squares scores.
In this paper, we propose a multi-objective approach for phylogenetic reconstruction using maximum parsimony (Fitch, 1972) and maximum likelihood (Felsenstein, 1981) criteria. The basis of this approach and preliminary results were presented in (Cancino & Delbem, 2007a,b). The proposed technique, called PhyloMOEA, is a multi-objective evolutionary algorithm (MOEA) based on the NSGA-II (Deb, 2001). The PhyloMOEA output is a set of distinct solutions representing a trade-off between the criteria considered. Results show the found trees are statistically consistent with the maximum parsimony and maximum likelihood solutions calculated separately. Moreover, the clade supports obtained from the trees found by Phylo-MOEA approximate, in general, the clade posterior probabilities of trees inferred by Bayesian inference methods.
This paper is organized as follows. Section 2. presents a brief introduction to the phylogenetic reconstruction methods. Section 3. introduces the key concepts of genetic algorithms and their application in phylogenetic inference. Section 4. provides background information about multi-objective optimization. Section 5. presents a detailed description of PhyloMOEA. Section 6. discusses the experiment results involving four nucleotide datasets and discusses the main results. Finally, Section 7. presents conclusions and proposes future work.
2. Phylogenetic reconstruction
Phylogenetic analysis studies the evolutionary relationships among species. The data used in this analysis usually come from sequence data (nucleotide or aminoacid sequences), morphological features, or other types of data (Felsenstein, 2004). Frequently, researchers only use data from contemporary species due the information about past species is unknown. Consequently, the phylogenetic reconstruction is only an estimation process since it is based on incomplete information (Swofford et al., 1996).
The evolutionary history of species under analysis is often represented as a leaf-labelled tree, called phylogenetic tree. The actual species (or taxons) are represented by the external nodes of the tree. The past species (ancestors) are referred by internal nodes of the tree. Nodes are connected by branches which may have an associated length value, representing the evolutionary distance between the nodes connected by the branch. It is important to stress that a phylogenetic tree is a hypothesis (of many possible ones) concerning the evolutionary events in the history of species.
A phylogenetic tree can be rooted or unrooted. In a rooted tree, there is a special node called root, which defines the direction of the evolution, determining ancestral relationships among nodes. An unrooted tree only shows the relative positions of nodes without an evolutionary direction.
The main objective of the phylogenetic inference is the determination of the best tree that explains the evolutionary events of the species under analysis. Several phylogenetic reconstruction methods have been proposed in the literature. Swofford et al. (1996) separated phylogenetic reconstruction methods into two categories:
Algorithmic methods, which use well-defined steps to generate a tree. An important feature of these methods is that they go directly to the final solution without examining many alternatives in the search space. Consequently, the solutions are quickly produced by these methods. Clustering approaches like NJ (Saitou & Nei, 1987) are in this category.
Optimality criterion methods, which basically have two components: an objective function (optimality criterion) and a search mechanism. The objective function is used to score each possible solution. The search mechanism walks through the tree search space in order to find the best scored tree according to the criterion used. Optimality methods are slower than algorithmic methods, however, they often provide more accurate answers (Huelsenbeck, 1995). Examples of optimality criterion methods are maximum parsimony (Fitch, 1972), maximum likelihood (Felsenstein, 1981) and least squares (Cavalli-Sforza & Edwards, 1967).
One of the main problems in phylogenetic inference is the size of the tree search space which increases exponentially in function of the number of taxons. In the case of optimality criterion methods, this means that the search mechanism requires heuristic techniques, which are able to find adequate solutions in reasonable running time for large or even moderate datasets. Exhaustive and exact search techniques can also be employed, although their use is constrained to problems with a small number of species.
Sections 2.1, 2.2 and 2.3 present a brief review of the criteria employed in this study: maximum parsimony, maximum likelihood and Bayesian inference.
2.1. Maximum parsimony
The parsimony principle states that the simplest hypothesis concerning an observed phenomenon must always be preferred. Parsimony methods search for a tree that minimizes the number of character state changes (or evolutionary steps). This tree, called maximum parsimony tree, refers to the simplest explanation of the evolutionary history for the species in a given dataset (Felsenstein, 2004).
Let D be a dataset containing n species. Each specie has N sites, where dij is the character state of specie i at site j. Given tree T with node set V (T) and branch set E(T), the parsimony score of T is defined as (Swofford et al., 1996):
PS(T)=∑j=1N∑(v,u)∈E(T)wjC(vj,uj)E1
where wj refers to the weight of site j, vj and uj are, respectively, the character states of nodes v and u at site j for each branch (u, v) in T and C is the cost matrix, such that C(vj, uj) is the cost of changing from state vj to state uj. The leaves of T are labelled by character states of species from D, i.e., a leaf representing k-th species has a character state dkj for position j. The following properties can be noted from Equation (1):
Parsimony criterion assumes independence of sites, i.e., each site is evaluated separately;
The calculation of the parsimony score only takes into account the tree topology. Thus, the parsimony criterion does not incorporate other information, like branch lengths.
There are several variants of the parsimony criterion. One of the simplest is the Fitch parsimony (Fitch, 1972), which assumes a unitary cost matrix such that Cxy = 1 if x ≠ y; otherwise Cxy = 0. The Fitch and even other more complex variants of parsimony can be even generalized for arbitrary cost matrix and restrictions of state changes (Sankoff, 1985).
Given a tree T, it is necessary to determine the character states of its internal nodes such that PS(T) is minimized. This is also known as the small parsimony problem. In the case of the Fitch parsimony, a post-order traversal in T is enough to minimize PS(T) (this procedure is known as Fitch algorithm (Fitch, 1972)). In the case of generalized parsimony, the small parsimony problem can be solved by applying the Sankoff algorithm (Sankoff, 1985).
Having defined an algorithm to minimize PS(T) for a given tree T, we should determine the tree T* such that PS(T*) is the minimum for all tree search space. The problem of finding T* is called large parsimony problem, which was proved to be NP-hard (Felsenstein, 2004). However, several heuristic techniques have been proposed to overcome such a difficulty (Goloboff, 1996).
2.2. Maximum likelihood
Likelihood is a widely-used statistical measurement. It evaluates the probability of a hypothesis giving rise to the observed data (Swofford et al., 1996). Thus, a hypothesis with higher probability is preferred to one with lower probability. The likelihood of a phylogenetic tree, denoted by L = P(D|T,M), is the conditional probability of the sequence data D given a tree T and an evolutionary model M, which contains several parameters related to tree branch lengths and a sequence substitution model (Felsenstein, 2004). Two assumptions are necessary to compute likelihoods:
Evolution at different sites is independent;
Evolution from different tree lineages is independent, i.e., each subtree evolves separately.
Given a tree T, L(T) is calculated from the product of partial likelihoods from all sites:
L(T)=∏j=1NLj(T)E3
where Lj(T) = P(Dj|T,M) is the likelihood at site j. The site likelihoods can also be expressed as:
Lj(T)=∑rjCj(rj,r)πrjE4
where r is the root node of T, rj refers to any possible state of r at site j, rj is the frequency of state rj, and Cj(rj, r) is the conditional likelihood of the subtree rooted by r. More specifically, Cj(rj, r) is the probability that everything that is observed from node r to the leaves of T, at site j, given r has state rj. Let u and v be the immediate descendants of r, then Cj(rj, r) can be formulated as:
where uj and vj refer to any possible state of nodes u and v, respectively. trv and tru are the lengths of the branch connecting node r to nodes v and u, respectively. P(rj, uj, tru) is the probability of changing from state rj to state uj during evolutionary time tru. Similarly, P(rj, vj, trv) is the probability of changing from state rj to state vj at time tvu. Both probabilities are provided by the evolutionary model M.
An efficient method to calculate L was proposed by Felsenstein (Felsenstein, 1981) using a dynamic programming approach, where L is obtained by a post-order traversal in T. Usually, it is convenient to work with logarithmic values of L, then Equation (2) results in:
lnL(T)=∑j=1nlnLj(T)E6
The likelihood calculation presented in this section assumes that sites evolve at equal rates. However, this assumption is often violated in real sequence data (Yang, 2006). Several among site-rate variation (ASRV) approaches can be incorporated in model M. One of the most employed ASRV approaches is the discrete-gamma model (Yang, 1994) where variables rates at sites follow a Γ distribution discretized in a number of categories. Several studies (Huelsenbeck, 1995; Tateno et al., 1994) have pointed out that the use of ASRV models can improve the results of the likelihood inference. However, ASRV models also increase the computational cost of the likelihood calculations.
In order to maximize L for a given tree T, it is necessary to optimize the parameters of model M (i.e: branch lengths and parameters of the substitution model chosen), which can be achieved using classical optimization methods (Felsenstein, 2004). Finding the maximum likelihood tree in the search space is a more difficult problem. Moreover, only heuristic approaches (Guindon & Gascuel, 2003; Lemmon & Milinkovitch, 2002; Lewis, 1998; Stamatakis & Meier, 2004) are feasible for large or even moderate datasets.
2.3. Bayesian Inference
Bayesian Inference methods have been more recently applied to phylogenetic inference (Larget & Simon, 1999; Rannala & Yang, 1996). The main objective of these methods is the calculation of the posterior probability of a tree topology and a model given the data.
Let D be a dataset containing n species. Let Ti be the i-th tree topology from NT tree possible topologies for n species. Let M be the model containing parameters as branch lengths and an sequence substitution model. The posterior probability of tree Ti given D is expressed by:
where P(D|Ti,M) is the likelihood of Ti and P(Tj, M) (P(Ti, M)) refers to the prior probability of tree Tj (Ti) and the parameters of M. The prior probabilities for tree topologies and parameters of M are specified in advance. Calculating the denominator from Equation 6 involves summing over all tree topologies and integrating over all parameters of M. This calculation is feasible only for small trees. To avoid this problem, the Markov chain Monte Carlo (MCMC) methods have been employed (Yang, 2006).
The MCMC algorithm walks through the tree topology and the parameter spaces. At the end of an MCMC execution, a sample of its iterations can be summarized in a straightforward way (Yang, 2006). For example, the tree topology with the highest posterior probability, called MAP tree, corresponds to the most visited tree during MCMC execution. Posterior probabilities from other tree topologies are calculated in a similar way. Moreover, it is also possible to calculate clade posterior probabilities of the MAP tree. In this case, the clade posterior probability refers to the proportion of visited trees that include the clade. Mr.Bayes (Ronquist et al., 2005) and BAMBE (Larget & Simon, 1998) are programs that implement Bayesian inference applied to phylogenetic reconstruction.
3. Genetic algorithms in phylogenetic inference
Genetic Algorithms (GAs) are metaheuristics (Alba, 2005) that can be used in phylogenetic inference. In the following paragraphs, GAs and their application to phylogenetic analysis are discussed.
Genetic Algorithms are search and machine learning techniques inspired by natural selection principles (Goldberg, 1989). They have been applied to a wide range of problems of science and engineering (Deb, 2001). A GA uses a set of individuals, called population, where each individual represents solutions for a given optimization problem. A fitness value, based on the problem objective function, is associated with each individual in the population. Individuals are internally codified using a data structure that must be able to store all relevant problem variables and represent all feasible solutions.
First, a GA creates an initial population and calculates the fitness of its individuals. Then, a new population is generated using three genetic operators: selection, crossover and mutation (Goldberg, 1989). The selection operator uses individuals\'fitness to choose adequate candidates to generate the next population. Features of the selected candidates are combined by the crossover operator and new offspring solutions are created. Then, small modifications are performed in offspring solutions by the mutation operator at a very low rate. The new individuals are stored in the next population. While crossover is useful to explore the search space, mutation can help to escape from local optima. The average fitness of the new population is expected to be better than the average fitness of the previous population. This process is repeated until a stop criterion has been reached. The selection operator leads Gas towards an optimal or near-optimal solution in the fitness landscape. The solutions found by the GA are in the final population.
Various papers have described the application of GAs to the phylogeny problem focused on one optimality criterion. Matsuda (1996) performed the first application of GAs to phylogenetic inference using the maximum likelihood criterion. Lewis (1998) proposed GAML, a GA for maximum likelihood, which introduces a sub-tree swap crossover and mutation operator based on SPR (Sub-tree Pruning and Regrafting (Swofford et al., 1996)) branch swapping. In his study, Lewis used the HKY85 (Hasegawa et al., 1985) evolutionary model whose parameters are included in the encoding of the individual. Thus, GAML optimized the tree topology, branch lengths and parameters of HKY85 model simultaneously.
Katoh et al. (2001) proposed GA-mt, a GA for maximum likelihood, which outputs multiple trees in the final population. These trees include the maximum likelihood tree and multiple alternatives that are not significantly worse compared with the best one. GA-mt also takes into account ASRV in the likelihood calculation. The crossover is a tree swap operator and the mutation is based on TBR (Tree Bisection and Reconnection (Swofford et al., 1996)) topological modifications. GA-mt employs Initial trees taken from bootstrap resampling analysis (Felsenstein, 2004).
Lemmon and Milinkovitch developed METAPIGA (Lemmon & Milinkovitch, 2002), a metapopulation GA (metaGA) for phylogenetic inference using maximum likelihood. In the proposed metaGA, several populations evolve simultaneously and cooperate in the search for the optimal solutions. METAPIGA combines advantages such as fast search for optimal trees, identification of multiple optima, fine control over algorithm speed and accuracy, production of branch support values (Felsenstein, 2004) and user-friendly interface. Another key element proposed by the authors is the consensus pruning mechanism. This procedure identifies the common regions (partitions) that are shared by trees in populations. These regions are protected against changes introduced by topological modifications. Thus, the search is only focused on the unprotected regions until no more changes are allowed. METAPIGA includes a subtree swap crossover operator and several mutation operators based on SPR, NNI (Nearest Neighbor Interchange (Swofford et al., 1996)), taxa swap and subtree swap topological changes. These operators are applied only if they do not destroy any consensus region.
Zwickl (2006) proposed a GA approach called GARLI (Genetic Algorithm for Rapid Likelihood). GARLI was developed in order to find the maximum likelihood tree for moderate and large sequence data (nucleotides, aminoacids and codon sequences). The author introduces several improvements in the topological search and branch length optimization tasks. These novel proposals reduce significantly the computational time required to perform the aforementioned tasks. For example, instead of optimizing all tree branches, GARLI optimizes a branch if the tree likelihood improvement is higher than a predetermined value. Thus, only branches that lead to a significant likelihood gain are considered for optimization. Parallel GARLI versions were also proposed.
GAs and local search were combined by Moilanen (2001) in PARSIGAL, a hybrid GA for phylogenetic inference using the maximum parsimony criterion. PARSIGAL uses a subtree exchange crossover operation and, instead of mutation, a local search approach based on NNI and TBR is employed. Using this hybrid algorithm, the GA defines the promising regions that should contain the global optimum, while the local search quickly reaches such a solution. PARSIGAL also includes heuristics for a fast recalculation of parsimony scores after topological modifications performed by the local search mechanism.
Congdon (2002) proposed a GA, called GAPHYL, which uses the parsimony criterion for the inference of phylogenetic trees. GAPHYL uses several subpopulations to avoid premature convergence, a subtree swap crossover operator and a taxa swap mutation operator. Other applications of GAs for phylogenetic inference employ distance-based optimality criterion (Cotta & Moscato, 2002).
Experimental results from the researches described above have shown that Gas have better performance and accuracy when compared to heuristics implemented in widely-used phylogenetic software, like PHYLIP (Felsenstein, 2000) and PAUP* (Swofford, 2000). Moreover, GAs are also suitable for use with several optimality criteria in order to solve multi-objective optimization problems (MOOP). Section 4. briey describes MOOPs and the application of GAs to them.
4. Multi-Objective Optimization
A MOOP deals with two or more objective functions that must be simultaneously optimized. In this context, the Pareto dominance concept is used to compare two solutions. A solution x dominates a solution y if x is not worse than y in all objectives and if it is better for at least one. Solving an MOOP implies calculating the Pareto optimal set whose elements, called Pareto optimal solutions, represent a trade-off among objective functions. Pareto optimal solutions are not dominated by any other in the search space. The curve formed by plotting these solutions in the objective function space is called Pareto front. If there is no additional information regarding the relevance of the objectives, all Pareto optimal solutions have the same importance. Deb (2001) highlights two fundamental goals in MOOP:
Finding a set of solutions as close as possible to the Pareto optimal front;
Finding a set of solutions as diverse as possible.
Many optimization techniques have been proposed to deal with MOOPs (Deb, 2001). The simplest approach transforms an MOOP into a single optimization problem using a weighted sum of objective functions. This strategy finds a single point in the Pareto front for each weight combination. Thus, several runs using different weight values are required to obtain a reasonable number of Pareto optimal solutions. Nevertheless, this method does not guarantee solution diversity in the frontier. Other classical methods to deal with MOOPs also have limitations, i.e., they need a priori knowledge of the problem, for example, target values (which are not always available).
Evolutionary algorithms for multi-objective optimization (MOEAs) have been successfully applied to both theoretical and practical MOOPs (Deb, 2001). In general, the most elaborated MOEAs are capable of finding a distributed Pareto optimal set in a single run. NSGA-II, SPEA2 (Zitzler et al., 2001), PAES (Knowles & Corne, 1999) are some of the most relevant MOEAs available in the literature.
Section 5. describes PhyloMOEA, the proposed MOEA, which is based on the NSGA-II, to solve the phylogenetic inference problem using maximum parsimony and maximum likelihood criteria.
5. PhyloMOEA
In general, optimality criterion methods solve the phylogenetic reconstruction problem as a single objective optimization problem, i.e., only a single optimality criterion (maximum parsimony, maximum likelihood, etc.) is employed to evaluate possible solutions. As a consequence, the results obtained from diverse phylogenetic methods often disagree. A feasible alternative is a multi-objective approach which takes into account several criteria simultaneously. This approach not only enables the determination of the best solution according to each criterion separately, but also finds intermediate solutions representing a trade-off among the criteria used. The following Subsections describe the proposed algorithm.
5.1. Internal encoding
A phylogenetic tree are usually represented using an unrooted tree data structure. An internal node is represented as a circular linked list, where each node has a pointer to its adjacent nodes (Felsenstein, 2004). The degree of an internal node defines the number of elements in the list.
On the other hand, PhyloMOEA employs a standard graph structure provided by the Graph Template Library (GTL) (Forster et al., 2004). GTL facilitates the implementation of genetic operators and the storage of additional information, such as branch lengths. Furthermore, parsimony and likelihood criteria can operate on rooted or unrooted trees.
5.2. Initial solutions
PhyloMOEA uses two populations, a parent population and an offspring population, as NSGA-II does. The parent population is denoted as Pi, where i refers to the i-th generation. In the first generation, solutions from P1 are created by an initialization procedure. In subsequent generations, Pi stores the best solutions found in the previous i–1 iterations. Solutions from Pi are also used to create the offspring population, denoted by Qi, by applying selection, crossover and mutation operators.
PhyloMOEA can generate initial random trees in P1; however, these trees are poor estimations of the maximum parsimony and likelihood trees. In this case, the PhyloMOEA\'s convergence is severely affected. In order to overcome this drawback, the initial solutions are provided by maximum likelihood, maximum parsimony and bootstrap analysis, which are performed before PhyloMOEA\'s execution. This strategy is usually employed by other GA-based phylogenetic programs (Katoh et al., 2001; Lemmon & Milinkovitch, 2002). There
5.3. Objective functions
PhyloMOEA calculates parsimony scores of the unrooted trees using the Fitch algorithm (Fitch, 1972). Several improvements to the original algorithm are detailed in the literature (Goloboff, 1999; Ronquist, 1998). It is possible to quickly recalculate the parsimony score after applying topological changes to the trees. Thus, unnecessary recalculations are avoided and evaluations of solutions are fast. These improvements were not implemented in PhyloMOEA.
The fitness of a solution is obtained using two values: a rank and a crowding distance (Deb, 2001). The rank value is calculated using a non-dominated sorting algorithm applied to R = Pi Qi (see Section 5.2). This algorithm divides R into several frontiers, denoted by F1, F2,…, Fj. The first frontier (F1) is formed by non- dominated solutions from R. Solutions in F1 are removed from R and the remaining solutions are employed to calculate the next set of non-dominated solutions, denoted by F2. This process is repeated in order to find F3, and so on, until R is empty. The rank value of an individual is the index of the frontier it belongs to.
Figure 1.
Sorting by non-dominance and crowding distance used in PhyloMOEA.
Solutions from the frontiers are copied to the next population Pi+1. As Pi and Qi have size N, there are 2N solutions which compete for N slots in Pi+1. Solutions from frontiers Fj=1…n are copied to Pi+1 until there are more solutions in frontier Fn than slots in Pi+1. In this case, the individuals from Fn with the highest crowding distance values are copied to Pi+1 until Pi+1 is fulfilled. The crowding distance is useful to maintain the population diversity. It reflects the density of solutions around its neighborhood. This value is calculated from a perimeter defined by the nearest neighbors in each objective. Figure 1 illustrates the non-dominated sorting algorithm and crowding distance mechanism implemented in PhyloMOEA.
PhyloMOEA uses a tournament selection to choose individuals for reproduction. It randomly picks two individuals from Pi and chooses the best one, which has the lowest rank. If both solutions have the same rank, the solution with the longest crowding distance is preferred.
5.5. Crossover operator
The crossover operator implemented in PhyloMOEA is the same operator proposed in GAML (Lewis, 1998). It combines a subtree from two parent trees and creates two new offspring trees. Given trees T1 and T2, this operator performs the following steps:
Prune a subtree s from T1;
Remove all leaves from T2 that are also in s;
The offspring subtree T2\' is obtained by regrafting s to an edge randomly chosen from T2.
The second offspring, denoted as T2\' is created in a similar way: prune a subtree from T2 and regraft it in T1. Figure 2 illustrates this operator.
Figure 2.
Example of the crossover operator.
5.6. Mutation operator
There are three well-known topological modifications used in phylogenetic inference: NNI, SPR and TBR (See Section 3.). NNI was employed in PhyloMOEA, since it performs fewer topological modifications than the others. This mutation operator performs the following steps:
Choose an interior branch whose connected nodes i, j define two pairs of neighbors: A, B adjacent to i (A,B ≠j) and C, D adjacent to j (C,D ≠i);
Execute a swap of two nodes taken from each pair of neighbors.
Figure 3 illustrates the NNI mutation operator. This operator also modifies branch lengths in order to improve the tree likelihood value. Some branches, chosen at random, have their lengths multiplied by a factor obtained from a Γ-distribution (Lewis, 1998).
Figure 3.
Example of NNI mutation operator.
Branch lengths from trees in the final population are optimized using a non-decreasing Newton-Raphson method described by Yang (2006). Since this optimization is time-consuming, it is applied only after a PhyloMOEA execution.
6. Results
This section describes the performed tests and analysis of the results. PhyloMOEA was tested using four nucleotide datasets:
The rbcL_55 dataset comprises 55 sequences (each sequence has 1314 sites) of the rbcL chloroplast gene from green plants (Lewis, 1998);
The mtDNA_186 dataset contains 186 human mitochondrial DNA sequences (each sequence has 16608 sites) obtained from The Human Mitochondrial Genome Database (mtDB) (Ingman & Gyllensten, 2006);
The RDPII_218 dataset comprises 218 prokaryotic sequences of RNA (each sequence has 4182 sites) taken from the Ribosomal Database Project II (Cole et al., 2005);
Finally, the ZILLA_500 dataset includes 500 rbcL sequences (each sequence has 1428 sites) from plant plastids (Guindon & Gascuel, 2003).
The optimization using maximum parsimony was performed by program NONA for the four datasets. Similarly, maximum likelihood analysis was carried out using programs RAxML-V and PHYML. The discrete-gamma HKY85 model (HKY85+Γ) was used to consider ASRV. RAxML-V calculates the likelihood using the HKY85CAT model (Stamatakis, 2006), which is an approximation of the HKY85+Γ. The branch lengths of the tree obtained by RAxML - V and the parameters of the HYK85+Γ model were optimized using PHYML. The aforementioned programs include sophisticated heuristics that produce satisfactory and fast results. Table 1 shows the parsimony and likelihood scores obtained from these programs. Such values represent extreme points of the Pareto front for the two objectives (parsimony and likelihood).
Table 1.
Parsimony and likelihood scores of the phylogenies found by NONA and RAxML-V+PHYML.
The trees in the initial population were generated from a bootstrap analysis applied to each dataset by using software PHYML, which employs the BIONJ algorithm (Gascuel, 1997) to each replication. The parsimony and likelihood scores of solutions obtained by the BIONJ algorithm are close to the scores shown in Table 1. However, for RDPII_218 and ZILLA_500 datasets, the tree topologies obtained by bootstrap were not close enough to those produced by NONA and RAxML-V+PHYML. Consequently, the PhyloMOEA\'s convergence is slower in this case. TO mitigate this effect, all solutions from Table 1 were included in the initial population.
Table 2 shows the parameters of PhyloMOEA used for the experiments. The ZILLA_500 dataset requires the largest number of generations and population size since it contains a larger number of species.
Table 2.
Parameters used by PhyloMOEA in the experiments.
Due to the stochastic nature of GAs, PhyloMOEA was run 10 times for each dataset. At the end of each run, the solutions provided by PhyloMOEA could be classified into two types:
Pareto-optimal Solutions (POS), which are the non-dominated solutions of the final population;
Final Solutions (FS), which include POS and the trees that have equal parsimony scores and different likelihood scores. These trees are promising from the perspective of parsimony criterion.
Table 3 shows the best score, average score and standard deviation () for the maximum parsimony and maximum likelihood criteria for all executions. The values in bold (Table 3) indicate the parsimony and likelihood scores improved by PhyloMOEA when compared with scores from Table 1. This improvement only occurs in the mtDNA_186 dataset. On the other hand, the standard deviation of parsimony score for this dataset indicates that the best solutions found by PhyloMOEA can be inferior than the one found by NONA.
The number of FS found for each execution can also be used to evaluate the ability of PhyloMOEA to reproduce results. Table 4 shows the maximum, average and standard deviation of the number of solutions in the two types of solution sets (POS and FS) for all executions. The low standard deviation values indicate the robustness of PhyloMOEA\'s behavior.
Table 3.
Summary of the results found by PhyloMOEA for parsimony and likelihood criteria.
Table 4.
Summary of experiment results for the number of solutions found by PhyloMOEA.
Figures 4(a), 4(b), 4(c) and 4(d) show the Pareto fronts obtained in one PhyloMOEA execution for rbcL_55, mtDNA_186, RDPII_218 and ZILLA_500 datasets, respectively. Parsimony scores are represented in the horizontal axis while likelihood scores are represented in the vertical one. These Figures also show Final Solutions near the Pareto front. Since the parsimony scores are integer values, the resulting Pareto front is a discontinuous set of points. The two extreme points from the frontier represent the maximum parsimony and maximum likelihood trees found by PhyloMOEA. If both points are close to each other, a reduced number of intermediate solutions is expected. This is the case for rbcL_55 and mtDNA_186 datasets, as illustrated in Figures. 4(a) and 4(b). Moreover, Table 3 shows a smaller number of trees in the Pareto front found for both datasets. On the other hand, extreme points in RDPII_218 and ZILLA_500 datasets are distant from each other. Consequently, there is a greater number of intermediate solutions, as shown in Figs. 4(c) and 4(d) and in Table 4. Nevertheless, PhyloMOEA was able to find a relatively large number of FS for all datasets.
Figure 4.
POS and FS for the employed datasets.
Solutions from POS and FS were compared using the Shimodaira-Hasegawa test (SH test) (Shimodaira & Hasegawa, 1999). The SH-test calculates a P–value for each solution, which indicates if a tree is significantly worse than the best scored tree according to a criterion. If a tree has a P–value lower than a given bound (usually 0.05), it can be rejected. The SH-test was performed for parsimony and likelihood criteria using PHYLIP and PAML (Yang, 1997), respectively.
Tables 5 and 6 summarize the results from the applications of the SH-test to POS and FS for each dataset showing the number of non-rejected (P ≥ 0.05) and rejected (P < 0.05) trees according to parsimony and likelihood criteria. It can be noted in Table 5 that there are few rejected POS for the rcbL_55 and mtDNA_186 dataset in both criteria. This is due to the extreme solutions in the Pareto front having their parsimony and likelihood scores close and, therefore, intermediate solutions cannot be rejected. On the other hand, extreme solution scores for RDPII_218 and ZILLA_500 datasets are more distant. Thus, SH-test rejects a larger number of POS for parsimony and likelihood criteria.
In the case of the FS, the SH-test applied to parsimony and likelihood criteria rejects most of the solutions for rbcL_55, RDPII_218 and ZILLA_500 datasets. On the other hand, the SH-test for parsimony criteria does not reject most of the FS from the mtDNA_186 dataset. It reveals that parsimony scores for FS are close to the best parsimony score found. The likelihood scores of FS from the mtDNA_186 dataset are also close to the maximum likelihood score, however, the proportion of rejected solutions is greater in this case.
Table 5.
Summary of SH-test results for POS.
Table 6.
Summary of SH-test results for FS.
It can also be noted from Tables 5 and 6 that the number of non-rejected FS is greater than the number of non-rejected POS. In most of the cases, the number of non-rejected solutions is doubled. Thus, the criterion used to maintain relevant solutions for the parsimony criterion was also useful to find alternative solutions according to the likelihood criterion.
We should highlight that the SH-test was designed to be applied for one criterion, i.e. this is not a multi-criteria test. However, the SH-test shows that some of the POS are not significantly worse than the best trees resulting from a separate analysis. Thus, PhyloMOEA was able to find intermediate solutions (distinct trees) that are consistent with the best solutions obtained from the parsimony and likelihood criteria.
Clade supports were calculated using the POS and FS. The support for a clade represents the proportion of trees which include such clade (Felsenstein, 2004). These values were compared with the clade posterior probabilities resulting from a Bayesian inference analysis. This analysis was performed for four datasets using Mr.Bayes. The number of Mr.Bayes iterations was fixed to 1.000.000 for rbcL_55 and mtDNA_186 datasets, 1.500.000 for the RDPII 218 dataset and 2.000.000 for the ZILLA_500 dataset. The evolutionary model employed was HKY85+Γ. The default values of the remaining Mr.Bayes\'parameters were maintained.
The clades shared by trees found by PhyloMOEA and Mr. Bayes were classified into 7 types in order to facilitate the analysis:
Type I: clade belongs only to intermediate trees. This type of clade is not present in the maximum parsimony and maximum likelihood trees;
Type II: clade is only in the maximum parsimony tree;
Type III: clade belongs to the maximum parsimony tree and intermediate trees;
Type IV: clade is only in the maximum likelihood tree;
Type V: clade belongs to the maximum likelihood and intermediate trees;
Type VI: clade is included in both maximum parsimony and maximum likelihood trees;
Type VII: clade is contained in maximum parsimony, maximum likelihood and intermediate trees.
Tables 7–10 illustrate the results of the comparison of the clades for rbcL_55, mtDNA_186, RDPII_218 and ZILLA_500 datasets, respectively. These Tables are divided into two parts which show the results for the shared clades of Mr.Bayes trees with PhyloMOEA POS and FS, respectively. The columns of these tables displays the clade type, the number of clades for each type, the PhyloMOEA mean clade support and the Mr.Bayes mean clade posterior probability. The values in bold indicate the highest support by PhyloMOEA and Mr.Bayes.
Results from Tables 7–10 indicate that most of the clades shared between PhyloMOEA and Mr.Bayes trees belong to types I,III,V and VII. However, only clades type V and VII have average clade support larger than 0.5 in most of the cases. This imply that PhyloMOEA and Mr.Bayes support clades that are shared among maximum likelihood and/or maximum
Table 7.
PhyloMOEA and Mr.Bayes clade support for the rbcL_55 dataset.
Table 8.
PhyloMOEA and Mr.Bayes clade support for the mtDNA_186 dataset.
Table 9.
PhyloMOEA and Mr.Bayes clade support for the RDPII_218 dataset.
Table 10.
PhyloMOEA and Mr.Bayes clade support for the ZILLA_500 dataset.
parsimony and intermediate trees. Moreover, the difference between PhyloMOEA and Mr.Bayes average support is small for clades type VII; while the same difference for clades type V is greater. On the other hand, most of the clades support values for types I, II, III and VI are low.
Figures 5(a)–5(d) shows the PhyloMOEA and Mr.Bayes clade support values for rbcL_55, mtDNA_186, RDPII_218 and ZILLA_500 datasets. Only support values for clades type V and VII are displayed in these Figures. Most of the points for which PhyloMOEA clade supports approximates Mr.Bayes posterior probabilities are located around the [1,1] coordinate. Moreover, these points correspond to type VII clades.
Figure 5.
PhyloMOEA clade support vs. Mr.Bayes posterior probability values for the dataset tested.
7. Conclusions
In this paper, we proposed an MOEA approach, called PhyloMOEA which solves the phylogenetic inference problem using maximum parsimony and maximum likelihood criteria. The PhyloMOEA\'s development was motivated by several studies in the literature (Huelsenbeck, 1995; Jin & Nei, 1990; Kuhner & Felsenstein, 1994; Tateno et al., 1994), which point out that various phylogenetic inference methods lead to inconsistent solutions.
Techniques using parsimony and likelihood criteria yield to different trees when they are applied separately to the four nucleotide datasets used in the experiments. On the other hand, PhyloMOEA was applied to the four datasets and found a set of trees that represents a trade-off between these criteria. POS and FS trees obtained by PhyloMOEA were statistically evaluated using the SH-test. The results of this test suggest that several PhyloMOEA solutions are consistent with the criteria used. It is important to observe that the PhyloMOEA trees are not directly comparable with trees obtained by other phylogenetic reconstruction programs since these programs consider only one optimality criterion.
Moreover, support values for clades included in trees obtained by PhyloMOEA were calculated. The clades were classified into several types according to the type of trees the clade is in: maximum parsimony, maximum likelihood or intermediate trees. Support values were compared with clade posterior probabilities reported by Mr.Bayes for the four test datasets used. The results show that PhyloMOEA clade support closely approximates Mr.Bayes posterior probabilities if the clades found in the set of trees correspond to intermediate and maximum likelihood/maximum parsimony trees.
Despite the relevant results found by PhyloMOEA, there are aspects that could be addressed in order to improve the algorithm and corresponding results:
PhyloMOEA requires several hours to find acceptable Pareto-solutions if initial trees are poorly estimated. This problem can be improved taking into account local search strategies (Guindon & Gascuel, 2003; Stamatakis & Meier, 2004). PhyloMOEA\'s performance is also decreased by the likelihood calculation, which is computationally intensive. As mentioned in Section 5.3, there are other techniques that address this problem (Larget & Simon, 1998; Stamatakis & Meier, 2004);
The proposed algorithm does not optimize parameters of the evolution model employed in the likelihood calculation. These values can be included in each solution such that they can be optimized during the algorithm execution (Lewis, 1998);
PhyloMOEA uses only Fitch parsimony which has a unitary state change cost matrix. The use of more complex parsimony models or even generalized parsimony can improve the results (Swofford et al., 1996);
Clade support obtained from PhyloMOEA trees can be also compared with bootstrap support values. A bootstrap analysis, using parsimony and likelihood criteria separately, enables the separation of clades that best support the maximum parsimony and maximum likelihood trees. This could lead to a better comparison between PhyloMOEA and bootstrap clade support values;
This research has not investigated the metrics for convergence and diversity of the obtained Pareto front. Measurements for convergence are difficult to obtain since the Pareto front is unknown in this case. On the other hand, various diversity metrics found in the literature (Deb, 2001) can be investigated;
The experiments have shown that PhyloMOEA can make relevant contributions to phylogenetic inference. Moreover, there are remaining aspects that can be investigated to improve the current approach.
Acknowledgments
The authors would like to acknowledge the State of Sao Paulo Research Foundation (FAPESP) for the financial support provided for this research (Grants N 01/13846-0 and N 2007/08655-5).
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Genetic algorithms in phylogenetic inference",level:"1"},{id:"sec_7",title:"4. Multi-Objective Optimization",level:"1"},{id:"sec_8",title:"5. PhyloMOEA",level:"1"},{id:"sec_8_2",title:"5.1. Internal encoding",level:"2"},{id:"sec_9_2",title:"5.2. Initial solutions",level:"2"},{id:"sec_10_2",title:"5.3. Objective functions",level:"2"},{id:"sec_11_2",title:"5.4. Fitness evaluation",level:"2"},{id:"sec_12_2",title:"5.5. Crossover operator",level:"2"},{id:"sec_13_2",title:"5.6. Mutation operator",level:"2"},{id:"sec_15",title:"6. Results",level:"1"},{id:"sec_16",title:"7. Conclusions",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'AlbaE. Parallel metaheuristics a new class of algorithms. Wiley series on parallel and distributed computing. John Wiley, Hoboken, NJ, 2005\n\t\t\t\t\t0-47167-806-6.'},{id:"B2",body:'CancinoW.DelbemA. Inferring phylogenies by multi-objective evolutionary algorithms. 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A.\n\t\t\t\t\tDating of the Human{Ape Splitting by a Molecular Clock of Mitochondrial DNA.\n\t\t\t\t\tJournal of Molecular Evolution, 22\n\t\t\t\t\t160174\n\t\t\t\t\t160174 .'},{id:"B23",body:'HuelsenbeckJ.\n\t\t\t\t\tPerformance of Phylogenetic Methods in Simulation. Systematic Biology, 44\n\t\t\t\t\t1748\n\t\t\t\t\t1995'},{id:"B24",body:'IngmanM.GyllenstenU. mtDB: Human Mitochondrial Genome Database, a Resource for Population Genetics and Medical Sciences. Nucleic Acids Research, 34:D749 -D751, 2006'},{id:"B25",body:'JinL.NeiM.\n\t\t\t\t\tLimitations of the Evolutionary Parsimony Method of Phylogenetic Analysis.\n\t\t\t\t\tMolecular Biology and Evolution, 7\n\t\t\t\t\t82102\n\t\t\t\t\t1990'},{id:"B26",body:'KatohK.KumaK.MiyataT.\n\t\t\t\t\tGenetic Algorithm-Based Maximum-Likelihood Analysis for Molecular Phylogeny.\n\t\t\t\t\tJournal of Molecular Evolution, 53\n\t\t\t\t\t477484\n\t\t\t\t\t2001'},{id:"B27",body:'KiddK.SgaramellaL. Phylogenetic analysis: Concepts and Methods. 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Accelerating parallel maximun likelihood-based phylogenetic tree calculations using subtree equality vectors. In Proceedings on CD, editor, 15 IEEE/ACM Supercomputing Conference (SC2002), Baltimore, Maryland,, 11 2002.'},{id:"B47",body:'D.\n\t\t\t\t\t\t\tSwofford. PAUP* Phylogenetic Analisys Using Parsimony, 2000. CSIT Florida State University.\n\t\t\t'},{id:"B48",body:'SwoffordD.OlsenG.WaddellP. J.HillisD. Phylogeny Reconstruction. In Molecular Systematics, chapter 11, 407514\n\t\t\t\t\t407514 Sinauer, 3 edition, 1996.'},{id:"B49",body:'TatenoY.TakezakiN.NeiM. Relative Efficiences of the Maximum-Likelihood, Neighbor-Joining, and Maximum Parsimony Methods when Substitution Rate Varies with Site. Molecular Biology and Evolution, 11\n\t\t\t\t\t261267\n\t\t\t\t\t1994'},{id:"B50",body:'Z. Yang. PAML: A Program Package for Phylogenetic Analysis by Maximum Likelihood. Computer Applications in Biosciences, 13(5):555-6, 1997.'},{id:"B51",body:'YangZ.\n\t\t\t\t\tMaximum-likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods.\n\t\t\t\t\tJournal of Molecular evolution, 3\n\t\t\t\t\t39\n\t\t\t\t\t306314\n\t\t\t\t\t1994'},{id:"B52",body:'Z. Yang. Computational molecular evolution. Oxford series in ecology and evolution. Oxford University Press, Oxford, 2006.'},{id:"B53",body:'ZitzlerE.LaumannsM.ThieleL. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103 Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland, May 2001.'},{id:"B54",body:'ZwicklD. J.\n\t\t\t\t\tGenetic Algorithm Approaches for the Phylogenetic Analysis of Large Biological Sequence Datasets under the Maximum Likelihood Criterion. PhD thesis, Faculty of the Graduate School. University of Texas., 2006'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"W. Cancino",address:null,affiliation:'
Institute of Mathematics and Computer Sciences University of São Paulo, Brazil
Institute of Mathematics and Computer Sciences University of São Paulo, Brazil
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\n
1. Introduction
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World Health Organization (WHO) and UNICEF advices that infants need to be exclusively breasted for the first 6 months and breastfeeding should last minimum 2 years. Nevertheless, around the world, the rate of breastfeeding in the first 6 months is still 38% and this percentage has not changed for about 20 years. It is known that breast milk significantly contributes to infants’ physical and mental development and acts as a protector for infants against several diseases. Therefore, with the contributions of WHO and UNICEF, breast milk is being promoted in order to increase the rate of breastfeeding to 50% for the first 6 months until 2025 and studies are being carried out with regard to the importance of breast feeding in early periods of infancy [1, 2].
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Use of breast feeding as primary nutrition in early periods, namely in the first 6 months, of infancy is highly common around the world (Norway: 95%, Australia: 92%, Canada: 89%, United States: 77%) and the percentage increases gradually year by year. On the contrary, after 6 months is decreasing dramatically [3]. By all means, there are several factors affecting this case. Some of these factors are mothers’ becoming a mother young, not having breastfeeding experience, concern of insufficient breast milk, desire to feed their babies with new tastes, active work life, long working hours and perceptions of mothers created by other individuals, mother’s and baby’s health condition, babies’ becoming acquainted with pacifier and feeding bottle [4, 5, 6]. Besides these factors, depending on the development of the baby, mothers generally give their babies other nourishments as supplement to breast milk or use them as only source of nutrition for their babies. The top of these nourishments is infant formulas.
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There are several firms operating globally in the sector of infant formulas which has become a massive market today. These firms invest in research-development activities, advertising activities and develop marketing strategies in order to gain advantage in the competition [7]. It is easy to find several follow-on milk, follow-on formulas and mixed formulas for the needs of 0–6 months-old and >6 months-old babies, which are formulated either in powder or liquid form and enriched with various ingredients [8]. Infant formulas are often preferred in that they are accessible and easy to prepare; besides, they can be used by others when mother is not available for feeding. On the other hand, it is known that mothers are deeply anxious about the infant formulas although they try to make their best to choose the most appropriate formula for their babies based on their research on written and visual media, advices from others and past experiences [9, 10].
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On one hand, it is beyond argument that breast milk is the best choice for babies’ nourishment, development and health; on the other hand, it is not always the one and only choice because of various reasons. Therefore, it needs to be ensured that the adverse effects of infant formulas, which are used as supplement to breast milk or used exclusively, on babies’ health in the short-, medium- and long-term are eliminated and these formulas not to cause any health problems for babies. In this respect, certain legal regulations are designed for the production and marketing of infant formulas nationally and globally. However, in the literature, although infant formulas carry the risk with respect to furan, acrylamide, chloropropanols and polycyclic aromatic hydrocarbons, which are called thermal processing contaminants and have potential to cause various health problems for humans, this information has not been referred in the legal regulations. Considering that the contaminants in question are included in various foods that are frequently preferred in daily diets, individuals expose to these contaminants starting from very early periods of infancy and this exposure continues throughout their lives. To this end, the current review aims to evaluate the infant formulas with respect to certain thermal processing contaminants.
\n
\n
\n
2. Thermal process contaminants
\n
Besides bringing certain sensorial properties to foods, thermal process is a processing technique that eliminates or decreases the potential hazards originating from foods against consumers’ health through making foods microbiologically more reliable. However, under certain conditions thermal processing applications cause certain toxic substances called “thermal processing contaminants” (heterocyclic aromatic amines, 5-hydroxymethylfurfural, polycyclic aromatic hydrocarbons, nitrosamines, furan, acrylamide, and chloropropanols) to emerge [11, 12]. In the last 10 years, a great amount of research has focused on thermal processing contaminants and this topic is still current and important for consumers, health authorities and industries [13].
\n
\n
2.1 Furan
\n
Furan is colorless, highly volatile and flammable compound with a boiling point close to room temperature (≈31°C). It is soluble in most of the organic solvents such as alcohol and acetone. Furan with a molecular formula of C4H4O and CAS number of 110-00-9 is a heterocyclic and aromatic compound [14].
\n
Formation of furan in foods is the result of various mechanisms. It has been documented that besides the presence of reducing sugar or amino acids, thermal degradation or Maillard reaction, ascorbic acid, thermal oxidation, oxidized polyunsaturated lipids, serine and cysteine without other sources [15, 16].
\n
In the risk assessment undertaken by U.S. National Institutes of Health (NIH) and Joint FAO/WHO Expert Committee on Food Additives (JECFA) depending on the studies on laboratory animals, furan was reported to be a strong carcinogenic compound that affected several organs [17, 18]. It has been identified as “possibly carcinogen to humans” (Group 2B) by International Agency for Research on Cancer (IARC) [19].
\n
In a study conducted by The US Food and Drug Administration (FDA) in year 2004 with 334 foodstuffs, presence of furan was reported for canned and jarred baby foods, infant formulas, coffees, meats, fish, soups, sauces, vegetables and fruits and several other foodstuffs that underwent thermal processing. Particularly, the study reported that all baby foods included furan [20]. After FD reports, The European Commission Recommendation 2007/196/EC offered a suggestion to the member countries in order for tracking the toxicity, formation, analysis and the exposure of furan [21]. Based on the reports from several countries, JECFA reported the foodstuffs that included the highest furan levels; roasted coffee (powder) (814–4590 μg/kg), instant coffee (powder) (90–783 μg/kg), brewed roasted coffee (34–113 μg/kg), baby food (19–96 μg/kg), soya sauce (16–52 μg/kg), canned fish (6–76 μg/kg) and baked beans (27–581 μg/kg) [22]. According to the reports of European Food Safety Authority (EFSA) and FDA, Crews and Castle classified the foodstuffs in three categories that included furan more than 100 μg/kg; coffee, baby foods, sauces and soups. Moreover, furan was found in 262 of 273 baby foods, 70 of 71 infant foods, 28 of 42 infant formulas. The levels of furan in baby foods, infant foods and infant formulas change between the ranges of 1–112 μg/kg (mean: 28 μg/kg), 1.3–87.3 μg/kg (mean: 27 μg/kg) and 2.5–27 μg/kg (mean: 12 μg/kg), respectively [23]. Several studies reported different levels of furan in baby foods and infant formulas; EFSA 31–32, 0.2–3.2 μg/kg, Liu and Tsai 4.23–124.1, 2.4–28.7 μg/kg [24, 25]. Lambert et al. determined the furan levels of many foods including baby foods and infant formulas (Table 1) [26].
\n
\n
\n
\n\n
\n
Food group
\n
Mean (μg/kg)
\n
\n\n\n
\n
Baby foods
\n
3.3–41
\n
\n
\n
Infant formulae
\n
3.5–5.7
\n
\n
\n
Vegetables
\n
5.9–6.3
\n
\n
\n
Fish
\n
5.3–5.3
\n
\n
\n
Cereal products
\n
44–44
\n
\n
\n
Meat products
\n
7.3–7.5
\n
\n
\n
Milk products
\n
1.4–2.3
\n
\n
\n
Soups
\n
16–16
\n
\n\n
Table 1.
The mean level of furan in different food groups [26].
The data were taken directly from Lambert et al.
\n
In this respect, Table 2 displays the results of dietary exposure of furan in individuals from diverse group of ages reported by EFSA.
The mean of infants’ dietary exposure of furan was reported as 0.99–1.34 μg/kg bw per day by FAO/WHO whereas Health Canada reported this level as 1.76 μg/kg bw per day [17, 28]. Some studies reported the mean of dietary exposure of furan for 4 months, 5–6 months, 7–12 months and 13–36 months old infants as 0.14, 0.60, 0.84 and 0.37 μg/kg bw per day [29] and 0.09, 0.56, 0.80 and 0.33 μg/kg bw per day, respectively [30].
\n
\n
\n
2.2 Chloropropanols
\n
In recent years, the presence of chloropropanols (certain fatty acid esters of 3-monochloro-1,2-propanediol (3-MCPD) and the related substance glycidol, 2-monochloro-1,3-propanediol (2-MCPD), 1,3-dichloro-2-propanol (1,3-DCP) and 2,3-dichloro-1-propanol (2,3-DCP)) in foodstuffs has aroused the attention of researchers [31]. Dichloropropanols are comprised of monoesters whereas monochloropropanediols are comprised of both monoesters and diesters [32]. It has been estimated that depending on thermal processing, lipids, glycerol, triolein and lecithin that are heated with hydrochloric acid are precursors in the formation of chloropropanols in foodstuffs [33, 34]. Chloropropanols and its esters are created from lipids and chlorides in the oil refining process particularly when the deodorization process is realized under high temperatures. Moreover, glycidol can occur through dehalogenation from 3-MCPD [35].
\n
It has not been ascertained that whether chloropropanol is a carcinogenic compound. On the other hand, it is disturbing that some free chloropropanol forms in foodstuffs are potentially toxic. The JECFA reported that 1,3-DCP is a genotoxic carcinogen, however, there is not enough evidence for the toxicologic evaluation of 2-MCPD [36, 37]. In this respect, Lee and Khor found that 3-MCPD and 1,3-DCP have potential genotoxic and carcinogenic characteristics [38]. Similarly, Onami et al. suggested that 3-MCPD carries unignorable risks for human health with regard to its potential hazard [39]. In some other studies 1,3-DCP and 3-MCPD are defined as possible human carcinogens (group 2B) and similarly glycidol is referred as a probable human carcinogen (group 2A) [40, 41, 42]. One of the most comprehensive studies on the toxicologic evaluation of chloropropanols revealed that whereas the carcinogenic effect of 1,3-DCP was highly evident, for the reason that the level of its presence in foodstuffs was considerably low, 1,3-DCP did not carry a risk for human health. This comprehensive study emphasized the insufficiency of the research on the level of the presence of 2-MCPD and 2,3-DCP in foodstuffs and the toxicologic evaluation of these substances. However, current evidence suggests that these compounds can be considered within low risk group for human health for the reason that the level of the presence of these compounds in foodstuffs is low [43]. EFSA determined the tolerable daily intake (TDI) for 3-MCPD as 0.8 μg/kg bw per day, whereas JECFA suggested the provisional maximum tolerable daily intake (PMTDI) of 4 μg/kg bw/day [44, 45].
\n
Recent studies revealed that chloropropanols was found in several foodstuffs at different levels particularly in soy sauces, meat and meat products, fish and sea foods, cereals, snacks, bread, biscuits, crisps, chips, baby foods and infant formulas as well [46, 47]. Table 3 shows the levels of chloropropanols in foodstuffs reported in the comprehensive study by EFSA.
\n
\n
\n
\n
\n
\n\n
\n
Food groups
\n
3-MCPD μg/kg
\n
2-MCPD μg/kg
\n
Glycidol μg/kg
\n
\n\n\n
\n
Vegetable fats and oils
\n
1093 (1090–1095)
\n
414 (400–427)
\n
1268 (1259–1277)
\n
\n
\n
Margarine and similar products
\n
408 (406–409)
\n
159 (152–166)
\n
361 (358–364)
\n
\n
\n
Infant formulas (powder)
\n
108 (108–109)
\n
44 (31–58)
\n
87 (80–94)
\n
\n
\n
Cereal-based products and similar
\n
83 (77–90)
\n
42 (38–47)
\n
51 (50–51)
\n
\n
\n
Fried, baked or roast meat or fish products
\n
30 (26–34)
\n
10 (7–14)
\n
38 (38–39)
\n
\n
\n
Smoked meat or fish products
\n
21 (15–28)
\n
6.2 (0.5–11)
\n
17 (15–19)
\n
\n
\n
Snacks and potato products
\n
130 (123–137)
\n
79 (75–84)
\n
58 (58–59)
\n
\n\n
Table 3.
The mean level of 3-MCPD, 2-MCPD, glycidol and esters in different food groups [48].
The data were taken directly from EFSA Journal.
\n
Considering the other studies on infant formulas and chloropropanols, Zelinková et al. identified 3-MCPD as 1.04–2.03 mg/kg, Weißhaar identified glycidol as 2.6–5.3 mg/kg, and Wöhrlin et al. identified 3-MCPD as 0.42 mg/kg and 2-MCPD, 0.19 mg/kg, glycidol 0.36 mg/kg [49, 50, 51]. Table 4 displays the results suggested by EFSA regarding the dietary exposure of chloropropanols for the individuals from different age groups.
\n
\n
\n
\n
\n
\n\n
\n
Age group
\n
3-MCPD μg/kg bw per day
\n
2-MCPD μg/kg bw per day
\n
Glycidol μg/kg bw per day
\n
\n\n\n
\n
Infants
\n
0.5–1.0
\n
0.2–0.4
\n
0.4–0.8
\n
\n
\n
Toddlers
\n
0.6–1.4
\n
0.3–0.6
\n
0.4–0.9
\n
\n
\n
Other children
\n
0.5–1.5
\n
0.3–0.7
\n
0.3–0.9
\n
\n
\n
Adolescents
\n
0.2–0.7
\n
0.1–0.3
\n
0.2–0.5
\n
\n
\n
Adults
\n
0.2–0.4
\n
0.1–0.2
\n
0.2–0.3
\n
\n
\n
Elderly
\n
0.2–0.4
\n
0.1–0.2
\n
0.1–0.3
\n
\n
\n
Very elderly
\n
0.2–0.5
\n
0.1–0.2
\n
0.1–0.3
\n
\n\n
Table 4.
The mean of the dietary exposure to 3-MCPD, 2-MCPD and Glycidol [48].
The data were taken directly from EFSA Journal.
\n
EFSA revealed that the food group that contributes 50% and higher levels of 3-MCPD, 2-MCPD and glycidol exposure for infants is infant formulas and follow-on formulas, which are followed by vegetable fats and oils, besides cookies. The levels of 3-MCPD, 2-MCPD and glycidol considering the exposure from only infant formulas were calculated as 2.4, 0.7–1.3, and 1.8–2.1 μg/kg bw per day, respectively [48]. JECFA estimated the average exposure to glycidol equivalents for babies between 0.1 and 3.6 μg/kg bw/day. However, the exposure level of 3-MCPD equivalents can increase to 10 μg/kg bw/day on average for the babies that are fed by infant formulas in the early periods of their lives [45]. Spungen et al. estimated the exposure of 3-MCPD equivalents for 0–1, 2–3 and 5–6 months old babies as 10, 8, 7 μg/kg bw per day respectively, whereas the exposure of glycidol and esters were estimated 2 μg/kg bw per day and same for all age groups [52]. Arisseto et al. identified the exposure of 3-MCPD for.
\n
0–5, 6–11 months old babies as 2.49, 1.05 μg/kg bw/day, respectively and the glycidol exposure as 3.65, 1.64 μg/kg bw/day [53].
\n
\n
\n
2.3 Acrylamide
\n
Acrylamide (AA), which was identified for the first time as a chemical compound in 1893 in Germany, is a chemical agent used extensively in such sectors as dams, tunnels, water treatment, paper and textile [54]. The presence of AA in foods for the first time was identified in 2002 by a group of researchers in Sweden [55]. Acrylamide formation in foods is explained through several mechanisms. The most important of all these mechanisms is especially Maillard reaction, which is performed in thermal processing with the presence of asparagines amino acid and reducing sugar [56]. It has been revealed that, apart from this mechanism, acrolein, B-alanine, aspartic acid, pyruvic acid and carnosine cause AA formation through various reactions [57].
\n
In the experimental studies carried out with animals, a positive dose-response relationship between AA and the cancer in multi-organs and tissues was found [58, 59]. In epidemiological studies conducted with humans, it was suggested that AA could seriously affect fetal development [60] and neurological changes [61]. On the other hand, there is not a clear consensus on the relationship between AA and cancer yet. Whereas, some studies reveal that AA increases the risk of contracting ovarian cancer [62], lung cancer [63] and the cancers related to digestive and respiratory systems [64], some other studies determine that AA has no positive relationship with several types of cancer [65, 66, 67]. However, IARC classifies AA as a probable human carcinogen (group 2A) [68].
\n
EFSA reported the results of the study that show AA levels in several foodstuffs in 2015 (Table 5).
\n
\n
\n
\n\n
\n
Food groups
\n
Mean (μg/kg)
\n
\n\n\n
\n
Potato fried products (except potato crisps and snacks)
\n
308 (303–313)
\n
\n
\n
Potato crisps and snacks
\n
389 (388–389)
\n
\n
\n
Soft bread
\n
42 (36–49)
\n
\n
\n
Breakfast cereals
\n
161 (157–164)
\n
\n
\n
Biscuits, crackers, crisp bread and similar
\n
265 (261–269)
\n
\n
\n
Coffee (dry)
\n
522 (521–523)
\n
\n
\n
Coffee substitutes (dry)
\n
1499
\n
\n
\n
Baby foods, other than cereal-based
\n
24 (17–31)
\n
\n
\n
Processed cereal-based baby foods
\n
73 (70–76)
\n
\n
\n
Other products based on potatoes, cereals and cocoa
In the other studies on infant formulas, different acrylamide levels were reported; Fohgelberg et al. found 3.5–223 μg/kg and Elias et al. found <LOD (limit of detection)-353 μg/kg [70, 71]. Likewise, Table 6 displays the results of dietary AA exposure of the individuals from different age groups reported by EFSA.
Mojska et al. calculated the daily dietary intake of acrylamide for 6, 7, 8, 9 and 10–12 months old babies as 17.46, 20.87, 21.65, 29.06 and 38.05 μg/person/day, respectively [72]. Considering the other studies on AA exposure, Health Canada estimated the AA exposure for <1 years and 1–3 years old babies as 0.211, 0.609 μg/kg bw per day, respectively, and Sirot et al. found the AA exposure levels for 1–4, 5–6, 7–12 and 13–36 months-old babies as 0.14, 0.03, 0.40 and 0.07 μg/kg bw per day, respectively [30, 73].
\n
\n
\n
\n
3. Acrylamide, furan and chloropropanol exposure caused by breast milk
\n
It is estimated that babies are exposed to contaminants coming from breast milk from the first seconds of their lives. This exposure varies depending on the impact of many factors such as the age of the mother, dietary habits, living space, and environmental contaminants etc. on the compounds in breast milk. Therefore, breast milk is globally monitored as a biomarker for exposure and sheds light on exposure evaluation studies [74, 75].
\n
The number of studies on the acrylamide level of breast milk is very limited. Sörgel et al. detected acrylamide in milk of mothers consuming foods that contain high levels of acrylamide such as potato chips, French fries etc. They stated that 10 to 50% of acrylamide occurring in pregnant women due to nutrition is transferred to the fetus through blood and it can reach μg/L in breast milk. They state that acrylamide exposure caused by breast milk continues until the end of breastfeeding, and therefore nursing mothers should avoid foods containing acrylamide until uncertainties about acrylamide are eliminated [76]. Fohgelberg et al. stated that traces of acrylamide were detected in all breast milk samples, the acrylamide level was determined as 0.51 μg/kg only in one sample while the acrylamide levels in the other 18 samples were under the limit of quantification (0.5 μg/kg). The mean acrylamide level in breast milk was assumed to be 0.25 μg/kg in the study and the mean acrylamide exposure was estimated as 0.04 μg/kg bw per day (the mean body weight is calculated as 5.5 kg) for infants that are fed only with breast milk during the early breastfeeding period. The results revealed the importance of breastfeeding as a way of preventing the baby from being exposed to acrylamide as the level of acrylamide in breast milk is very low [70].
\n
The source and possible consequences of 3-MCPD in breast milk have not been entirely explored yet. However, it has been stated that dietary habits of mothers are an important factor for presence of 3-MCPD in breast milk [34, 77]. Zelinkova et al. determined the 3-MCPD level between 11 and 76 μg/kg and the mean amount as 35.5 μg/kg in 12 breast milk samples. They determined the 3-MCPD exposure caused by breast milk in babies (breastfed for up to 4 months) as 26,625 μg/day (average daily intake of mother’s milk by the baby is about 750 mL) and 8.19 μg/kg bw per day [77]. Jędrkiewicz et al. stated that 2-MCPD and 3-MCPD reached 2.2 mg/kg in breast milk and therefore it was highly difficult for babies to avoid chloropropanols [78].
\n
Polychlorinated dibenzofurans (PCDFs), which are another contaminant in breast milk, have been examined in studies together with polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins (PCDDs). A lot of studies can be found in the literature on this subject compared to acrylamide and chloropropanols. As breast milk is the first and most important way of conveying PCBs and PCDD/Fs to babies, WHO has been conducting global studies on dioxin detection in breast milk since 1987 [79]. Costopoulou et al. reported that the countries with the highest level of PCBs and PCDD/Fs in breast milk are Egypt, the Netherlands, Belgium, Luxemburg, and Italy {respectively, 22.3, 18.27, 16.92, 14.97, 12.66 pg/g [fat WHO-TEQ (toxicity equivalent)]} while the countries with the lowest levels are Fiji, Brazil, the Philippines, Australia, and Bulgaria (respectively 3.34, 3.92, 3.94, 5.57 and 6.14 pg/g fat WHO-TEQ) [80]. WHO has estimated the range of tolerable daily dose as 1–4 pg TEQ/kg bs per day for babies exposed to dioxin contaminants such as PCDD/Fs and PCBs [81]. Focant et al. calculated the average concentration for total TEQ (PCDD/Fs and PCBs) as 17.81 pg/g and the daily intake of PCDD/Fs and PCBs as 62.3 TEQ/kg bw per day [82]. In a study they conducted in China (Guangdong Province), Huang et al. predicted the mean EDI level of PCDD/PCBs resulting from breast milk as 54.3 pg TEQ/kg bw per day [83].
\n
\n
\n
4. Conclusion
\n
The current review evaluated infant formulas that have an important place in the diets of babies, with respect to the thermal processing contaminants; furan, chloropropanols and acrylamide, which have become one of the foci of researchers. When the results of the studies regarding the exposure of these contaminants are evaluated, it is suggested that babies are in the risk group, who are highly exposed to these contaminants because of their low body weight compared to other individuals, besides; there are no alternative foods to infant formulas in their daily diet. In the light of the evidence revealed by the previous studies, the current review proposes that regarding the furan, chloropropanols and acrylamide, infant formulas can be a concern for baby health. Nevertheless, the review further suggests that it is important to decrease the level of thermal processing contaminants or specify certain upper limits and determine these regulations by law for the individual health and the health of the overall society. Furthermore, the current review emphasized that infant formulas are not alternatives to breast milk and educating mothers in this respect is critically important for the health of next generations. One last thing to emphasize is the need to raise the awareness of breastfeeding mothers in avoiding the consumption of foods that have a rich content in terms of the abovementioned contaminants.
\n
\n\n',keywords:"infant formulas, furan, chloropropanols, 3-MCPD, 2-MCPD, glycidol, acrylamide, exposure",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73072.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73072.xml",downloadPdfUrl:"/chapter/pdf-download/73072",previewPdfUrl:"/chapter/pdf-preview/73072",totalDownloads:125,totalViews:0,totalCrossrefCites:0,dateSubmitted:"April 17th 2020",dateReviewed:"July 17th 2020",datePrePublished:"September 16th 2020",datePublished:"October 28th 2020",dateFinished:null,readingETA:"0",abstract:"This review attempted to evaluate the exposure of thermal processing contaminants such as furan, chloropropanols and acrylamide from infant formulas. Furan, chloropropanols and acrylamide exist at varying levels in several types of foods that are consumed in daily diet including infant formulas. The consumption of these foods leads to the exposure to the thermal processing contaminants. In this sense, it is apparent that humans face hidden danger through dietary exposure throughout their lives. Infants are considered as the age group that expose to the highest levels of these substances as a result of the fact that they have low body weight and consume infant formulas in their diets as alternative nutrition. The review emphasizes that the infant formulas are not innocent, on the contrary, they can be considered as safety critical for infants considering that infant formulas include furan, chloropropanols and acrylamide. Therefore, this review suggests that in this sense all shareholders’ (university, non-governmental organizations, public and private sector) acting in concert with each other is crucially important for the health of individuals and overall society.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73072",risUrl:"/chapter/ris/73072",signatures:"Burhan Başaran",book:{id:"9805",title:"Infant Feeding",subtitle:"Breast versus Formula",fullTitle:"Infant Feeding - Breast versus Formula",slug:"infant-feeding-breast-versus-formula",publishedDate:"October 28th 2020",bookSignature:"Isam Jaber Al-Zwaini, Zaid Rasheed Al-Ani and Walter Hurley",coverURL:"https://cdn.intechopen.com/books/images_new/9805.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"30993",title:"Prof.",name:"Isam Jaber",middleName:null,surname:"Al-Zwaini",slug:"isam-jaber-al-zwaini",fullName:"Isam Jaber Al-Zwaini"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"208659",title:"Mr.",name:"Burhan",middleName:null,surname:"Başaran",fullName:"Burhan Başaran",slug:"burhan-basaran",email:"burhan.basaran@erdogan.edu.tr",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Thermal process contaminants",level:"1"},{id:"sec_2_2",title:"2.1 Furan",level:"2"},{id:"sec_3_2",title:"2.2 Chloropropanols",level:"2"},{id:"sec_4_2",title:"2.3 Acrylamide",level:"2"},{id:"sec_6",title:"3. Acrylamide, furan and chloropropanol exposure caused by breast milk",level:"1"},{id:"sec_7",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'\nWorld Health Organization (WHO). Country Implementation of the International Code of Marketing of Breast-Milk Substitutes: Status Report. 2011. Available from: https://apps.who.int/iris/bitstream/handle/10665/85621/9789241505987_eng.pdf [Accessed: 25 May 2020]\n'},{id:"B2",body:'\nUNICEF. Improving Child Nutrition: The Achievable Imperative for Global Progress. New York: United Nations Publications; 2013. pp. 1-14\n'},{id:"B3",body:'\nHealth Canada. 2013. Breastfeeeding in Canada. Available from: https://www150.statcan.gc.ca/n1/pub/82-624-x/2013001/article/11879-eng.htm [Accessed: 25 May 2020]\n'},{id:"B4",body:'\nBai DL, Fong DY, Tarrant M. Factors associated with breastfeeding duration and exclusivity in mothers returning to paid employment postpartum. Maternal and Child Health Journal. 2015;19(5):990-999\n'},{id:"B5",body:'\nLou Z, Zeng G, Huang L, Wang Y, Zhou L, Kavanagh KF. Maternal reported indicators and causes of insufficient milk supply. Journal of Human Lactation. 2014;30(4):466-473\n'},{id:"B6",body:'\nBraimoh J, Davies L. When ‘breast’ is no longer ‘best’: Post-partum constructions of infant-feeding in the hospital. Social Science & Medicine. 2014;123:82-89\n'},{id:"B7",body:'\nAbrams SA, Daniels SR. Protecting vulnerable infants by ensuring safe infant formula use. The Journal of Pediatrics. 2019;211:201-206\n'},{id:"B8",body:'\nTraves D. Understanding infant formula. Paediatrics and Child Health. 2019;29(9):384-388\n'},{id:"B9",body:'\nLee E. Health, morality, and infant feeding: British mothers’ experiences of formula milk use in the early weeks. Sociology of Health & Illness. 2007;29(7):1075-1090\n'},{id:"B10",body:'\nLee E, Furedi F. Mothers’ Experience of, and Attitudes to, Using Infant Formula in the Early Months. England: School of Social Policy, Sociology and Social Research, University of Kent. 2005. pp. 1-93\n'},{id:"B11",body:'\nPerez Locas C. Mechanism of formation of thermally generated potential toxicants in food related model systems [PhD thesis]. McGill University. 2008. Available from: https://escholarship.mcgill.ca/concern/theses/z890rx30j?locale=en [Accessed: 23 May 2020]\n'},{id:"B12",body:'\nStuder A, Blank I, Stadler RH. Thermal processing contaminants in foodstuffs and potential strategies of control. Czech Journal of Food Sciences. 2004;22(I):1\n'},{id:"B13",body:'\nMogol BA, Gökmen V. Thermal process contaminants: Acrylamide, chloropropanols and furan. Current Opinion in Food Science. 2016;7:86-92\n'},{id:"B14",body:'\nNational Institutes of Health (NIH). 2020. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/Furan [Accessed: 20 May 2020]\n'},{id:"B15",body:'\nBecalski A, Seaman S. Furan precursors in food: A model study and development of a simple headspace method for determination of furan. Journal of AOAC International. 2005;88(1):102-106\n'},{id:"B16",body:'\nPerez Locas C, Yaylayan VA. Origin and mechanistic pathways of formation of the parent furan a food toxicant. Journal of Agricultural and Food Chemistry. 2004;52(22):6830-6836\n'},{id:"B17",body:'\nJoint, FAO, WHO Expert Committee on Food Additives World Health Organization. Safety Evaluation of Certain Contaminants in Food: Prepared by the Seventy-Second Meeting of the Joint FAO/WHO Expert Committee on Food Additives (JECFA). 2011. Available from: https://apps.who.int/iris/bitstream/handle/10665/44520/9789241660631_eng.pdf [Accessed: 20 May 2020]\n'},{id:"B18",body:'\nNational Toxicology Program. Toxicology and Carcinogenesis Studies of Furan (CAS No. 110-00-9) in F344 Rats and B6C3F1 Mice (Gavage Studies). Vol. 402. National Toxicology Program Technical Report Series; 1993. p. 1\n'},{id:"B19",body:'\nInternational Agency for Research on Cancer (IARC). Monographs on the Evaluation of Carcinogenic Risks to Humans. 1995. Available from: https://monographs.iarc.fr/list-of-classifications [Accessed: 20 May 2020]\n'},{id:"B20",body:'\nThe US Food and Drug Administration (FDA). Exploratory Data on Furan in Food. 2004. Available from: https://www.fda.gov/food/chemicals/exploratory-data-furan-food [Accessed: 20 May 2020]\n'},{id:"B21",body:'\nThe European Commission Recommendation. 2007/196/EC, on the Monitoring of the Presence of Furan in Foodstuffs. 2007. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32007H0196 [Accessed: 20 May 2020]\n'},{id:"B22",body:'\nFood and Agriculture Organization of the United Nations/World Health Organization (FAO/WHO). Joint FAO/ WHO Expert Committee on Food Additives (JECFA) Rome Feb 2010: Furan. Technical Report Series 959/Food Additives Series 63. 2011. Available from: http://www.fao.org/3/a-at868e.pdf [Accessed: 20 May 2020]\n'},{id:"B23",body:'\nCrews C, Castle L. A review of the occurrence, formation and analysis of furan in heat-processed foods. Trends in Food Science & Technology. 2007;18(7):365-372\n'},{id:"B24",body:'\nLiu YT, Tsai SW. Assessment of dietary furan exposures from heat processed foods in Taiwan. Chemosphere. 2010;79(1):54-59\n'},{id:"B25",body:'\nEuropean Food Safety Authority. Update on furan levels in food from monitoring years 2004-2010 and exposure assessment. EFSA Journal. 2011;9(9):2347\n'},{id:"B26",body:'\nLambert M, Inthavong C, Desbourdes C, Hommet F, Sirot V, Leblanc JC, et al. Levels of furan in foods from the first French Total diet study on infants and toddlers. Food Chemistry. 2018;266:381-388\n'},{id:"B27",body:'\nEFSA Panel on Contaminants in the Food Chain (CONTAM), Knutsen HK, Alexander J, Barregård L, Bignami M, Brüschweiler B, et al. Risks for public health related to the presence of furan and methylfurans in food. EFSA Journal. 2017;15(10):e05005\n'},{id:"B28",body:'\nHealth Canada. Furan. 2016. Available from: https://www.canada.ca/en/health-canada/services/food-nutrition/food-safety/chemical-contaminants/food-processing-induced-chemicals/furan.html [Accessed: 18 May 2020]\n'},{id:"B29",body:'\nAgence nationale de securit e sanitaire alimentation, environnement, travail (Anses). 2016. Etude de l’alimentation totale infantile. Available from: http://www.quasaprove.org/moodle/pluginfile.php/1421/mod_resource/content/1/EATi_Synth%C3%A8se%20et%20Conclusions.pdf [Accessed: 18 May 2020]\n'},{id:"B30",body:'\nSirot V, Rivière G, Leconte S, Vin K, Traore T, Jean J, et al. French infant total diet study: Dietary exposure to heat-induced compounds (acrylamide, furan and polycyclic aromatic hydrocarbons) and associated health risks. Food and Chemical Toxicology. 2019;130:308-316\n'},{id:"B31",body:'\nYau JCW, Kwong KP, Chung SWC, Ho YY, Xiao Y. Dietary exposure to chloropropanols of secondary school students in Hong Kong. Food Additives and Contaminants: Part B Surveillance. 2008;1(2):93-99\n'},{id:"B32",body:'\nSeefelder W, Scholz G, Schilter B. Structural diversity of dietary fatty esters of chloropropanols and related substances. European Journal of Lipid Science and Technology. 2011;113(3):319-322\n'},{id:"B33",body:'\nDestaillats F, Craft BD, Sandoz L, Nagy K. Formation mechanisms of monochloropropanediol (MCPD) fatty acid diesters in refined palm (Elaeis guineensis) oil and related fractions. Food Additives & Contaminants: Part A. 2012;29(1):29-37\n'},{id:"B34",body:'\nRahn AKK, Yaylayan VA. What do we know about the molecular mechanism of 3-MCPD ester formation? European Journal of Lipid Science and Technology. 2011;113(3):323-329\n'},{id:"B35",body:'\nPudel F, Benecke P, Fehling P, Freudenstein A, Mattheaus B, Schwaf A. On the necessity of edible oil refining and possible sources of 3-MCPD and glycidyl esters. European Journal of Lipid Science and Technology. 2011;113(3):368e373\n'},{id:"B36",body:'\nThe Joint Food and Agriculture Organization/World Health Organization Expert Committee on Food Additives. 3-Chloro-1,2-Propanediol. Safety Evaluation of Certain Food Additives and Contaminants. WHO Food Additives Series 48. Geneva: WHO; 2001\n'},{id:"B37",body:'\nThe Joint Food and Agriculture Organization/World Health Organization Expert Committee on Food Additives. Sixty-Seventh Meeting – Summary and Conclusions. Rome: FAO; 2006. Available from: ftp://ftp.fao.org/ag/agn/jecfa/jecfa67_final.pdf [Accessed: 18 May 2020]\n'},{id:"B38",body:'\nLee BQ , Khor SM. 3-Chloropropane-1, 2-diol (3-MCPD) in soy sauce: A review on the formation, reduction, and detection of this potential carcinogen. Comprehensive Reviews in Food Science and Food Safety. 2015;14(1):48-66\n'},{id:"B39",body:'\nOnami S, Cho YM, Toyoda T, Horibata K, Ishii Y, Umemura T, et al. Absence of in vivo genotoxicity of 3-monochloropropane-1, 2-diol and associated fatty acid esters in a 4-week comprehensive toxicity study using F344 gpt delta rats. Mutagenesis. 2014;29(4):295-302\n'},{id:"B40",body:'\nInternational Agency for Research on Cancer (IARC). Monographs on the Evaluation of Carcinogenic Risks to Humans. 2013. 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Available from: https://efsa.onlinelibrary.wiley.com/doi/pdf/10.2903/j.efsa.2018.5083 [Accessed: 25 May 2020]\n'},{id:"B45",body:'\nFood Additives. Evaluation of Certain Contaminants in Food (Eighty-Third Report of the Joint FAO/WHO Expert Committee on Food Additives). WHO Technical Report Series, No.1002; 2017\n'},{id:"B46",body:'\nFAO/WHO. Joint FAO/WHO Food Standards Programme-Codex Committee on Contaminants in Food. Discussion Paper on Chloropropanols Derived from the Manufacture of Acid-HVP and the Heat Processing of Foods. CX/CF 07/1/13. Rome: Codex Alimentarius Commission; 2007. Available from: http://www.fao.org/tempref/codex/Meetings/CCCF/cccf1/cf01_13e.pdf [Accessed: 25 May 2020]\n'},{id:"B47",body:'\nFood Standards Australia New Zealand (FSANZ). Chloropropanols in Food, an Analysis of the Public Health Risk, Technical Report Series No. 15. 2003. Available from: http://www.foodstandards.gov.au/_srcfiles/Chloropropanol%20Report%20%28no%20appendices%29-%2011%20Sep%2003b-2.pdf [Accessed: 25 May 2020]\n'},{id:"B48",body:'\nHoogenboom LAP. Scientific opinion: Risks for human health related to the presence of 3-and 2-monochloropropanediol (MCPD), and their fatty acid esters, and glycidyl fatty acid esters in food. EFSA Journal. 2016;14(5):4426\n'},{id:"B49",body:'\nWöhrlin F, Fry H, Lahrssen-Wiederholt M, Preiß-Weigert A. Occurrence of fatty acid esters of 3-MCPD, 2-MCPD and glycidol in infant formula. Food Additives & Contaminants: Part A. 2015;32(11):1810-1822\n'},{id:"B50",body:'\nZelinková Z, Dolezal M, Velísek J. Occurrence of 3-chloropropane-1,2-diol fatty acid esters in infant and baby foods. European Food Research and Technology. 2009;228:571-578\n'},{id:"B51",body:'\nWeißhaar R. Fatty acid esters of 3-MCPD: Overview of occurrence and exposure estimates. European Journal of Lipid Science and Technology. 2011;113:304-308\n'},{id:"B52",body:'\nSpungen JH, MacMahon S, Leigh J, Flannery B, Kim G, Chirtel S, et al. Estimated US infant exposures to 3-MCPD esters and glycidyl esters from consumption of infant formula. Food Additives & Contaminants: Part A. 2018;35(6):1085-1092\n'},{id:"B53",body:'\nArisseto AP, Silva WC, Scaranelo GR, Vicente E. 3-MCPD and glycidyl esters in infant formulas from the Brazilian market: Occurrence and risk assessment. Food Control. 2017;77:76-81\n'},{id:"B54",body:'\nNational Institutes of Health (NIH). 2018. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/acrylamide#section=Top [Accessed: 19 May 2020]\n'},{id:"B55",body:'\nTareke E, Rydberg P, Karlsson P, Eriksson S, Tornqvist M. Analysis of acrylamide, a carcinogen formed in heated foodstuffs. Journal of Agricultural and Food Chemistry. 2002;51(17):4998-5006\n'},{id:"B56",body:'\nStadler RH, Blank I, Varga N, Robert F, Hau J, Guy PA, et al. Food chemistry: Acrylamide from Maillard reaction products. Nature. 2002;419(6906):449-450\n'},{id:"B57",body:'\nGuenther H, Anklam E, Wenzl T, Stadler RH. Acrylamide in coffee: Review of progress in analysis. Formation and level reduction. Food Additives and Contaminants. 2007;24(1):60-70\n'},{id:"B58",body:'\nNational Toxicology Program (NTP). NTP Technical Report on the Toxicology and Carcinogenesis. Studies of Glycidamide (CAS No. 5694-00-8) in F344/N Nctr Rats and B6C3F1/Nctr Mice (Drinking Water Studies). NTP TR 588. National Institutes of Health. Public Health Service. U.S. Department of Health and Human Services; 2014. Available from: http://ntp.niehs.nih.gov/ntp/htdocs/lt_rpts/tr588_508.pdf [Accessed: 19 May 2020]\n'},{id:"B59",body:'\nVon Tungeln LS, Doerge DR, da Costa GG, Matilde Marques M, Witt WM, Koturbash I, et al. Tumorigenicity of acrylamide and its metabolite glycidamide in the neonatal mouse bioassay. International Journal of Cancer. 2012;131:2008-2015\n'},{id:"B60",body:'\nKadawathagedara M, Botton J, de Lauzon-Guillain B, Meltzer HM, Alexander J, Brantsaeter AL, et al. Dietary acrylamide intake during pregnancy and postnatal growth and obesity: Results from the Norwegian mother and child cohort study (MoBa). Environment International. 2018;113:325-334\n'},{id:"B61",body:'\nGoffeng LO, Alvestrand M, Ulvestad B, Sorensen KA, Skaug V, Kjuus H. Self-reported symptoms and neuropsychological function among tunnel workers previously exposed to acrylamide and N-methylolacrylamide. Scandinavian Journal of Work, Environment and Health. 2011;37:136-146\n'},{id:"B62",body:'\nWilson KM, Mucci LA, Rosner BA, Willett WC. A prospective study of dietary acrylamide intake and the risk of breast, endometrial, and ovarian cancers. Cancer Epidemiology, Biomarkers & Prevention. 2010;19(10):2503-2515\n'},{id:"B63",body:'\nHirvonen T, Kontto J, Jestoi M, Valsta L, Peltonen K, Pietinen P, et al. Dietary acrylamide intake and the risk of cancer among Finnish male smokers. Cancer Causes & Control. 2010;21(12):2223-2229\n'},{id:"B64",body:'\nLiu ZM, Tse LA, Ho SC, Wu S, Chen B, Chan D, et al. Dietary acrylamide exposure was associated with increased cancer mortality in Chinese elderly men and women: A 11-year prospective study of Mr. and Ms. OS Hong Kong. Journal of Cancer Research and Clinical Oncology. 2017;143(11):2317-2326\n'},{id:"B65",body:'\nMucci LA, Sandin S, Bälter K, Adami HO, Magnusson C, Weiderpass E. Acrylamide intake and breast cancer risk in Swedish women. JAMA. 2005;293(11):1322-1327\n'},{id:"B66",body:'\nSchouten LJ, Hogervorst JG, Konings EJ, Goldbohm RA, van den Brandt PA. Dietary acrylamide intake and the risk of head-neck and thyroid cancers: Results from the Netherlands cohort study. American Journal of Epidemiology. 2009;170(7):873-884\n'},{id:"B67",body:'\nKotemori A, Ishihara J, Zha L, Liu R, Sawada N, Iwasaki M, et al. Dietary acrylamide intake and the risk of endometrial or ovarian cancers in Japanese women. Cancer Science. 2018;109(10):3316-3325\n'},{id:"B68",body:'\nInternational Agency for Research on Cancer (IARC). 1994. Available from: https://monographs.iarc.fr/list-of-classifications [Accessed: 21 May 2020]\n'},{id:"B69",body:'\nEFSA Panel on Contaminants in the Food Chain (CONTAM). Scientific opinion on acrylamide in food. EFSA Journal. 2015;13(6):4104\n'},{id:"B70",body:'\nFohgelberg P, Rosén J, Hellenäs KE, Abramsson-Zetterberg L. The acrylamide intake via some common baby food for children in Sweden during their first year of life—An improved method for analysis of acrylamide. Food and Chemical Toxicology. 2005;43(6):951-959\n'},{id:"B71",body:'\nElias A, Roasto M, Reinik M, Nelis K, Nurk E, Elias T. Acrylamide in commercial foods and intake by infants in Estonia. Food Additives & Contaminants: Part A. 2017;34(11):1875-1884\n'},{id:"B72",body:'\nMojska H, Gielecińska I, Stoś K. Determination of acrylamide level in commercial baby foods and an assessment of infant dietary exposure. Food and Chemical Toxicology. 2012;50(8):2722-2728\n'},{id:"B73",body:'\nHealth Canada. Health Canada\'s Revised Exposure Assessment of Acrylamide in Food. Bureau of Chemical Safety. Food Directorate. Health Products and Food Branch; 2012. pp. 1-19. Available from: https://www.canada.ca/content/dam/hc-sc/migration/hc-sc/fn-an/alt_formats/pdf/securit/chem-chim/food-aliment/acrylamide/rev-eval-exposure-exposition-eng.pdf [Accessed: 15 May 2020]\n'},{id:"B74",body:'\nShen H, Guan R, Ding G, Chen Q , Lou X, Chen Z, et al. Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) and polychlorinated biphenyls (PCBs) in Zhejiang foods (2006-2015): Market basket and polluted areas. Science of the Total Environment. 2017;574:120-127\n'},{id:"B75",body:'\nSchuhmacher M, Mari M, Nadal M, Domingo JL. Concentrations of dioxins and furans in breast milk of women living near a hazardous waste incinerator in Catalonia, Spain. Environment International. 2019;125:334-341\n'},{id:"B76",body:'\nSörgel F, Weissenbacher R, Kinzig-Schippers M, Hofmann A, Illauer M, Skott A, et al. Acrylamide: Increased concentrations in homemade food and first evidence of its variable absorption from food, variable metabolism and placental and breast milk transfer in humans. Chemotherapy. 2002;48(6):267-274\n'},{id:"B77",body:'\nZelinková Z, Novotný O, Schůrek J, Velíšek J, Hajšlová J, Doležal M. Occurrence of 3-MCPD fatty acid esters in human breast milk. Food Additives & Contaminants: Part A. 2008;25:669-676\n'},{id:"B78",body:'\nJędrkiewicz R, Głowacz-Różyńska A, Gromadzka J, Kloskowski A, Namieśnik J. Indirect determination of MCPD fatty acid esters in lipid fractions of commercially available infant formulas for the assessment of infants’ health risk. Food Analytical Methods. 2016;9(12):3460-3469\n'},{id:"B79",body:'\nvan den Berg M, Kypke K, Kotz A, Tritscher A, Lee SY, Magulova K, et al. WHO/UNEP global surveys of PCDDs, PCDFs, PCBs and DDTs in human milk and benefit–risk evaluation of breastfeeding. Archives of Toxicology. 2017;91(1):83-96\n'},{id:"B80",body:'\nCostopoulou D, Vassiliadou I, Papadopoulos A, Makropoulos V, Leondiadis L. Levels of dioxins, furans and PCBs in human serum and milk of people living in Greece. Chemosphere. 2006;65(9):1462-1469\n'},{id:"B81",body:'\nvan Leeuwen FR, Feeley M, Schrenk D, Larsen JC, Farland W, Younes M. Dioxins: WHO’s tolerable daily intake (TDI) revisited. Chemosphere. 2000;40(9-11):1095-1101\n'},{id:"B82",body:'\nFocant JF, Fréry N, Bidondo ML, Eppe G, Scholl G, Saoudi A, et al. Levels of polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans and polychlorinated biphenyls in human milk from different regions of France. Science of the Total Environment. 2013;452:155-162\n'},{id:"B83",body:'\nHuang R, Wang P, Zhang J, Chen S, Zhu P, Huo W, et al. The human body burden of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) in residents’ human milk from Guangdong Province, China. Toxicology Research. 2019;8(4):552-559\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Burhan Başaran",address:"burhan.basaran@erdogan.edu.tr",affiliation:'
'}],corrections:null},book:{id:"9805",title:"Infant Feeding",subtitle:"Breast versus Formula",fullTitle:"Infant Feeding - Breast versus Formula",slug:"infant-feeding-breast-versus-formula",publishedDate:"October 28th 2020",bookSignature:"Isam Jaber Al-Zwaini, Zaid Rasheed Al-Ani and Walter Hurley",coverURL:"https://cdn.intechopen.com/books/images_new/9805.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"30993",title:"Prof.",name:"Isam Jaber",middleName:null,surname:"Al-Zwaini",slug:"isam-jaber-al-zwaini",fullName:"Isam Jaber Al-Zwaini"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"111640",title:"MSc.",name:"David",middleName:"Paul",surname:"McAllindon",email:"david.mcallindon@nrc.gc.ca",fullName:"David McAllindon",slug:"david-mcallindon",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"National Research Council Canada",institutionURL:null,country:{name:"Canada"}}},booksEdited:[],chaptersAuthored:[{title:"Directions in Research into Response Selection Slowing in Schizophrenia",slug:"directions-in-research-into-response-selection-slowing-in-schizophrenia",abstract:null,signatures:"D.P. 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He was trained at the Department of Psychiatry Case Western Reserve University as Research Associate from 1993 to 1995, and was appointed Visiting Professor in the Department of Psychiatry, Vanderbilt University from 2000 to 2002, as the founder of the augmentation pharmacotherapy to ameliorate cognitive impairment of schizophrenia. His research fields include cognitive neuroscience and psychopharmacology, as represented by more than 100 peer-reviewed articles and book chapters. 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\\n
\\n\\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at oapf@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at oapf@intechopen.com
\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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