\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:!0,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:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,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). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). 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1. Introduction
Quantum Finite State Machines (QFSM) are a well known model of computation that was originally formalized by Watrous [Wat95a, Wat95b, Wat97], Kondacs [KW97] and more generally Quantum Turing Machines (QTM) have been described by Bernstein [BV97]. In particular the 2-way QFSM have been shown to be more powerful than classical FSM [KW97]. Thus the interest in quantum computational models of automata and machines is not only theoretical but has also possible applications realization of future quantum computer and robotics controllers.
In this chapter we present the evolutionary approach to the synthesis of QFSM’s specified by a quantum circuits. This approach was originally proposed by [LP09] and is possible on yet only theoretical basis. In particular this approach requires a selective qubit-initialization in a quantum register. In contrast the current methodology and approaches to practical Quantum Computation, the current practical realization of quantum computation always starts with the initialization of the whole quantum register and terminates by the measurement of either all of the qubits or by the measurement of a given subset of qubits. Moreover in general there is no reuse of any element of the quantum register.
In this text we analyze in details what type of QFSM can be successfully synthesized.
The evolutionary approach will evaluate the results based on both the correctness and the cost of the evolved machines. Multiple parameters such as type of error evaluation, synthesis constraints and evolutionary operators will be discussed when evaluating to the obtained results.
In particular we show how to synthesize QFSMs as sequence detectors and illustrate their functionality both in the quantum world and in the classical (observable) world. The application of the synthesized quantum devices is illustrated by the analysis of recognized sequences.
Finally, we provide analytic method for the used evolutionary approach and we describe the experimental protocol, and its heuristic improvements. We also discuss the results. In addition, we investigate the following aspects of the Evolutionary Quantum Logic Synthesis:
Quantum probabilistic FSM and Reversible FSM.
Hardware acceleration for the Fitness evaluation using CBLAS [cbl] and using CUBLAS [cud] (CUDA[cud] implemented Basic Linear Algebra Subprograms (BLAS)[cbl] subroutines).
2. Background in quantum computing
In Quantum Computing the information is represented by a Quantum Bit also called qubit. The wave equation is used to represent a qubit or a set of them. Equation 1 shows a general form in the Dirac notation.
|ϕ〉=eiρcosθ+ei(ρ+ψ)sinθ|ϕ〉=eiρ(cosθ+eiψsinθ)E1
In Dirac notation represents a column vector, also called a ket. The bra element denoted stands for hermitian conjugate. In this manner a bra-ket represents the inner, dot-vector product while represents the outer vector product. The general equation (1), eiρcosθ|0〉+ei(ρ+ψ)sinθ|1〉can be written as α|0〉+β|1〉with|α|2+|β|2=1and |α|2is the probability of observing the state 0 while |β|2 is the probability of observing 1.
In general, to describe basis states of a Quantum System, the Dirac notation is preferred to the vector-based Heisenberg notation. However, Heisenberg notation can be more practical to represent the exponential growth of the quantum register. Let two orthonormal quantum states be represented in the vector (Heisenberg) notation eq. 2.
|↑〉=|0〉=[10]|↓〉=|1〉=[01]E2
Different states in this vector notation are then multiplications of all possible states of the system, and for a two-qubit system we obtain (using the Kronecker product[Gru99, Gra81, NC00]) the states represented in eq. 3:
The Kronecker product exponentially increases the dimension of the space for matrices as well:
I⊗X=[1001]⊗[0101]=12[0100100000010010]E4
This tensor product operation for a parallel connection of to wires is shown in Figure 1.
Assume that qubit a (with possible states |0 and |1) is represented by|ψ〉=αa|0〉+βa|1〉 and qubit b is represented by|ψb〉=αb|0〉+βb|1〉. Each of them is represented by the
Figure 1.
Circuit representing the W\n\t\t\t\t\t\tX operation
superposition of their basis states, but put together the characteristic wave function of their combined states will be:
|ψaψb〉=αaαb|00〉+αaβb|01〉+βaαb|10〉βaβb|11〉E5
with a and b being the complex amplitudes of states of each EP respectively. As shown before, the calculations of the composed state used the Kronecker multiplication operator. Hence comes the possibility to create quantum memories with extremely large capacities and the requirement for efficient methods to calculate such large matrices.
Quantum Computation uses a set of Quantum properties. These are the measurement, the superposition and the entanglement. First, however, the principles of multi-qubit system must be introduced.
2.1. Multi-Qubit System
To illustrate the superposition let’s have a look at a more complicated system with two quantum particles a and b represented by |ψa〉=α0|0〉+βa|1〉 and |ψb〉=αb|0〉+βb|1〉 respectively. For such a system the problem space increases exponentially and is represented using the Kronecker product [Gru99].
|ψa〉⊗|ψb〉=[α0β0]⊗[α1β1]=[α0α1α0β1β0α1β0β1]E6
Thus the resulting system is represented by |ψaψb〉=αaαb|00〉+αaβb|01〉+βaαb|10〉+βaβb|11〉 (5) where the double coefficients obey the unity (completeness) rule and each of their powers represents the probability to measure the corresponding state. The superposition means that the quantum system is or can be in any or all the states at the same time. This superposition gives the massive parallel computational power to quantum computing.
2.2. Entanglement and projective measurements
Assume the above two-particle vector (two-qubit quantum system) is transformed using the quantum circuit from Figure 2.
This circuit executes first a Hadamard transform on the top qubit and then a Controlled-Not operation with the bottom qubit as the target. Depending on the initial state of the quantum register the output will be either|ψaψb〉=αaαb|00〉+βaβb|11〉or|ψaψb〉=αaβb|01〉+βaαb|10〉. Thus it is not possible to estimate with 100% probability the initial state of the quantum register.
Let |ab〉=|00〉 at level a (Figure 2). The first step is to apply the [H] gate on the qubit-a and the resulting state at level b of the circuit is
For an output 0 (on the qubit-a), the projective measurement of the first (topmost) qubit (qubit-a on Figure 2) on this stage would collapse the global state (with a single measurement) to the state |00:
If one would look to the output of the measurement on the second qubit (qubit-b), the probability for obtaining |0 or |1 is in this case the following:
Thus the expectation values for measuring both values 0 or 1 on each qubit independently are12.
If however one looks on the second and non-measured qubit (if the qubit-a is measured, it is the qubit-b, and vice versa) and calculates the output probabilities, the output is contradictory to the expectations given by standard probabilistic distribution such as a coin toss q = 1 − p. To see this let’s start in the state
[120012]E14
and measure the qubit-a and obtain a result. In this case assume the result of the measurement is given by:
|ψ〉→M0|ψ〉〈ψ|M0†M0|ψ〉=[1000]E15
Then measuring the second qubit (qubit-b) will not affect the system because the measurement of the qubit-a has collapsed the whole system into a single basis state:
|ψ〉→M|00〉E16
The probability for obtaining a |1 on the qubit-b is thus 0 and the measurement on qubit-b (after having measured qubit-a) has no effect on the system at all. The states of qubits are thus correlated. This non-locality paradox was first described by Einstein-Podolsky-Rosen work[EPR35] and is known as the EPR paradox. This particular phenomenon is one of the most powerful in quantum mechanics and quantum computing, as it allows together with superposition the speedup of finding solutions to certain types of problems. Finally, it can be noted that mathematically, the entangled state is such that it cannot be factored into simpler terms. For example, the state (|00〉+|01〉)2→12(|0〉+|1〉)|0〉 and thus it can be factored. However, the states as those introduced in eq. 15 cannot be transformed in such a manner and are thus entangled; physically implying that they are related through measurement or observation.
2.3. Single-Qubit quantum gates
We are now concerned with matrix representation of operators. The first class of important quantum operators are the one-qubit operators realized in the quantum circuit as the one-qubit (quantum) gates. Some of their matrix representations can be seen in equation 17.
Each matrix of an Operator has its inputs from the top (from left to right) and the outputs on the side (from top to bottom). Thus taking a state |ψ〉 (eq.18) and an unitary operator H (eq. 19)
|ψ〉=α|0〉+β|1〉E18
H=12[111−1]E19
the result of computation is represented in equation 20.
H|ψ〉=12[111−1][αβ]=[α+β2α−β2]E20
00011011↓↓↓↓U=00←01←10←11←[1000010000010010]E21
Equation 21 shows the inputs (input minterms) on the top of the matrix and the output minterms on the left side. Thus for an input |10 (from the top) the output is |11 (from the side).
2.4. Multi-Qubit quantum gates
The second class of quantum gates includes the Controlled-U gates. Schematic representation of such gates can be seen in Figure 3. Gates in Figure 3a – Figure 3c represent the general structures for single-control-qubit single-qubit gate, two-control-qubit single-qubit gate, single-control-qubit two-qubit gate and two-control-qubit two-qubit gate respectively. The reason for calling these gates Controlled is the fact that they are based on two operations: first there is one or more control bits and second there is a unitary transformation similar to matrices from equation 17 that is controlled. For instance the Feynman gate is a Controlled-NOT gate and has two input qubits a and b as can be seen in
Figure 3.
Schematic representation of Controlled-U gates: a) general structure of single-qubit controlled-U gate (control qubit a, target qubit, b) two-qubit controlled, single-qubit operation, c) single-qubit controlled, two-qubit target quantum gate, d) Feynman (CNOT), e) Toffoli (CCNOT), f) Fredkin. a, b, c are input qubits and a’, b’ and c’ are respective outputs.
Figure 3. Its unitary matrix with input and output minters is shown in eq. (21). Thus qubits controlling the gate are called the control qubits and the qubits on which the unitary transform is applied to are called the target qubits.
Figures 3d - Figure 3f represent special cases where the controlled unitary operator is Not, Not and Swap, respectively. The respective unitary matrices are in equations 21, 22a and 22b.
Equation 21 shows that if the input state is for instance |00 (from the top) the output is given by. Similarly for all other possible input /output combinations.
The Controlled-U gate means that while the controlled qubit a is equal to 0 the qubits on output of both wires are the same as they were before entering the gate (a’ = a, b’ = b). Now if qubit a equals to 1, the result is a’ = a and b’ = b according to matrix in equation (17.a). It can be easily verified that the CCNOT (Toffoli) gate is just a Feynman gate with one more control qubit and the Fredkin gate is a controlled swap as shown on Figure 3.
A closer look at equations (21 and 22) gives more explanation about what is described in eq. 21: CNOT, eq. 22a : Toffoli and eq. 22b : Fredkin gates. For instance, equation 21 shows that while the system is in states |00 and |01 the output of the circuit is a copy of the input. For the inputs |10 and |11 the second output is inverted and it can be seen that the right-lower corner of the matrix is the NOT gate. Similarly in the other two Controlled gates the NOT gate matrix can be found.
2.5. NMR-based quantum logic gates
The NMR (Nuclear Magnetic Resonance) technology approach to quantum computing [Moo65, PW02, DKK03] is the most advanced quantum realization technology used so far, mainly because it was used to implement the Shor algorithm [Sho94] with 7 qubits [NC00]. Yet other technologies such as Ion trap [DiV95], Josephson Junction [DiV95] or cavity QED [BZ00] are being used. The NMR quantum computing has been reviewed in details in [PW02, DKK03] and for this paper it is important that it was so far the NMR computer that allowed the most advanced algorithm (7 qubit logic operation) to be practically realized and analyzed in details. Thus it is based on this technology that the constraints of the synthesis are going to be established for the cost and function evaluation. Some prior work on synthesis has been also already published [LLK+06] and few simple cost functions have been established.
For the NMR-constrained logic synthesis the conditions are:
Single qubit operations: rotations Rx,Ry,Rz for various degrees of rotation . With each unitary rotation (Rx, Ry, Rz) represented in equation 23
Two-qubit operation; depending on approach the Interaction operator is used as Jzz or Jxy for various rotations
Thus a quantum circuit realized in NMR will be exclusively built from single qubit rotations about three axes x,y,z and from the two-neighbor-qubit operation of interaction allowing to realize such primitives as CNOT or SWAP gates. Examples of gates realized using NMR quantum primitives are shown in Figure 5 to Figure 8.
Figure 4.
Structure of the Toffoli gate
Figure 5.
Single pulse Logic gate – NOT
Figure 6.
Two-pulses logic gate – Hadamard
Figure 7.
Detailed Realization of Feynman Gate with five EM pulses.
Figure 8.
Five-pulses logic gate - Controlled-V
Also, the synthesis using the NMR computing model using EM pulses, is common to other technologies such as Ion Trap [CZ95, PW02] or Josephson Junction [BZ00]. Thus the cost model used here can be applied to synthesize circuits in various technologies, all of these technologies having the possibility to express the implemented logic as a sequence of EM pulses.
3. Quantum finite state machines
The paradigms of quantum circuits from Section 2 are applied in this paper to the synthesis of computational models such as QFSM as defined in [LPK09]. This section briefly introduces the knowledge about Quantum computational models and their properties as well as specifies the types of devices that are going to be synthesized. We describe the 1-way Quantum Finite State Machines (FSM) from both the theoretical (computational) point of view as well as from the engineering (circuit) point of view. Most of the work in this area is still on the theoretical level but the proofs of concept quantum devices [Dun98, SKT04, MC06, RCHCX+08, YCS09] allow to speculate that such models will be useful for quantum logical devices that will appear in close future.
3.1. 1-way quantum finite automata
Quantum Finite State Machines (QFSM) are a natural extension of classical (probabilistic) FSM’s. Two main types of QFSM are well known: One-way QFSM (1QFSM) [AF98, MC00] and two-way QFSM (2QFSM)[AW02, KW97]. As will be illustrated and explained the 1QFSM, can accept sequentially classical input, quantize it, process it and measures its quantum memory after each operation (Figure 9). In this work the focus is on the synthesis of the 1QFSM from Figure 9(b). From now on the general designation of QFSM will refer to 1QFSM in this work. Other type of described QFSMs will be specifically named.
Figure 9.
Schematic representation of a 1QFSM; (a) after each computation step the machine state is measured, (b) after each computation step the output is measured, (c) after each computational step the machine state and the output state are measured.
In contrast to that, the 2QFSM is designed to operate on quantum input data (allowing to put the reading head in superposition with the input tape, and requiring all the input data to be present at once for the maximum efficiency) and the measurement is done only at the end of a whole process.
Definition 3.1
Quantum State Machine - a QFSM is a tuple Γ = {Q,Λ, q0,Qac,QrjI, δ}, where Q is a finite set of states, σ is the input alphabet, δ is the transition function. The states q0 Q′, Qac Q and Qrj Q are the initial states, the set of accepting states and the set of rejected states, respectively.
The QFSM machine action maps the set of machine states and the set of input symbols into the set of complex machine next states. The computation of such machine is required to be done using unitary operators and is performed on the basis set Bq using unitary operators U, . In particular the QFSM uses a set of Unitary Operators corresponding to the input of input characters on the input tape. Thus for a given string to be processed and prior to the whole process termination (string either accepted or rejected), the overall processing can be represented as:
MUθnMUθn−1MUθn−2...MUθ3MUθ2Uθ1|q0〉E24
with MUθnbeing the application of the Uθn operator to the current state and creating the configuration Uθn|q followed by the measurement of the current state M (projecting the state into G).
The 1QFSM was proven to be less powerful or equally powerful to its classical counterpart 1FSM [Gru99, KW97] in that it can recognize the same classes of regular languages as the classical FSM can recognize.
The above described 1QFSM is also called the measure-many quantum finite automaton [KW97]. A model called measure-once quantum finite automata was also introduced and studied by Moore [MC00]. The measure-many 1QFSM is similar to the concepts of the 2QFSM. For comparison we illustrate the main differences between the 1QFSM and 2QFSM below.
Example 3.1.1 1QFSM
Let Q={|q0〉,|q1〉}be two possible states (including the accepting and rejecting states) of a single-qubit machine M and with transition functions specified by the transitions defined in eq. 25 corresponding to the state diagram in Figure 10a.
Figure 10.
a) State transition diagram for the 1QFSM defined by the transition function 25, (b) the representation of the QFSM using quantum multiplexers. Observe two control outputs |q specifying the machine action/states and the input symbols selecting the appropriate unitary transform V for {#, $, 0, 1}.
The machine M, specified in eq. 25 represents a state machine that uses the H gate when the input is 0 (V0 = H) and the Pauli-Z rotation gate when the input is 1 (V1 = Z). Observe that machine M would have different behavior for measure-once and measure-many implementation. In the measure-many case, the machine generates a quantum coin-flip while receiving input 0 and while receiving input 1 the Pauli-Z rotation is applied. Observe in the measure-once case, that for example for the string input = ”010” the many-measure machine will implement a NOT using [H][Z][H]. Note that in this approach to QFSM each input symbol {#, $, 0, 1} is represented by a unitary transform that can be seen as shown in Figure 10. No measurement is done here on |q while the sequence of quantum operators is applied to this state. The 2QFSM operates on a similar principle as the 1QFSM model but with the main difference being the application of the measurement. This is schematically shown in Figure 11 for the completeness of explanation.
Figure 11.
Schematics representing the difference between the 1QFSM and 2QFSM. On the top, the 1QFSM - for each input character read from left to right from the tape, a unitary transform U is applied on the state and the state is measured. On the bottom, the 2QFSM moves on the input tape left and right, the unitary transform U is applied on the state and only once the computation is terminated the final state is observed/measured.
3.2. Quantum logic synthesis of sequence detectors
The problem to synthesize the QFSM is to find the simplest quantum circuit for a given set of input-output sequences thus letting the state assignment problem for this machine be directly solved by our synthesis algorithm. This direct synthesis approach can be applied to binary, multiple-valued and fuzzy quantum machines with no principle differences - only fitness functions are modified in an evolutionary algorithm [LPG+03, LP05].
Let us assume that there exists a sequential oracle that represents for instance Nature, robot control or robot’s environment. In our example this oracle is specified by a state diagram in Figure 12a. This oracle can represent partial knowledge and a deterministic or probabilistic machine of any kind. Assume that there is a clearing signal (denoted by an arrow in Figure 12a) to set the oracle into its initial state. By giving initial signals and input sequences and observing output sequences the observer can create a behavior tree from Figure 12b.
Figure 12.
Example of a deterministic oracle and its diagnostic tree.
As in general this oracle is never fully known, we perform experiments with it to determine some of its input-output behaviors. Assume that the oracle from Figure 12a is represented by the sequences from the experiments. These input-output sequences are shown in eq. 26 with |iqo represents the input qubit, the state qubit and the output qubit respectively. Observe that the diagnostic tree form Figure 12(b) shows the state with {a, b} and the inputs and the outputs as 0 and 1.
As the full knowledge of the oracle is in general impossible - the oracle is approximated by sets of input-output sequences and the more such sequences that we create - the more accurate characterization of the oracle as a QFSM can be created.
The overall procedure for the detection of a sequence of length j can be summarized as follows:
Initialize all qubits of the quantum register to the initial desired state,
repeat j times:
Initialize the input qubit to a desired state and set the output qubit to |0
Apply the quantum operator on the quantum register of the QFSM
Measure the output qubit and observe the result
Using the procedure describe above one can synthesize quantum circuits for oracles being well known universal quantum gates such as Fredkin. The input-output sequences found from this oracle are next used to synthesize the QFSM from Figure 13a. Figure 13b shows the state-diagram of the machine.
Figure 13.
Example of implementation of Fredkin gate as a quantum FSM of first class. Observe the notation where |i is the input, |q is the machine state and |o is the machine output.
We will call the machine in Figure 13(a) the QFSM of the first class. This is because both the output and the input qubits are initialized after each computation. Observe that it is represented with feedback lines as in Figure 9 with input and output being initialized for each input and the state initialized only once - at the beginning of the computation. The interested reader can read more on this representation in [LP09], however it is important to understand that the feedback lines are shown here only as the equivalent notation to the classical FSM as in Figure 9. The circuit-based approach to QFSM does not require this notation as this ”loop” is represented by the fact that the quantum qubit preserves its state [LP09].
A set of input-output sequences defining partially the "Fredkin QFSM" is represented in eq. 27.
A class two QFSM has in turn the initialization In applied only to the input qubit. This way the generated sequence is now expressed not only as a function f(i,q)⊕|0〉 but rather asf(i,q)⊕|0〉. This means that now the output is directly dependent also on the previous output state. This QFSM of the second class is shown in Figure 14. The difference between the QFSM of the first and of the second class can be seen on the output qubit |0〉 where in the case of the QFSM of the first class the initialization Ino means the initialization of the output at each computation step while the class two QFSM uses I0o initializes the output only once, at the beginning of the computation.
Figure 14.
Example of implementation of Fredkin gate as a quantum FSM of second class where the output is initialized only once and the measurement is done either after each input or only completely at the end.
For instance, a class two QFSM constructed from a "Fredkin oracle" differs from the class by different possible state transition. This is shown in Table 1. The first column represent the current state of the quantum register build from the input, state and output qubits |iqo. The second column shows the state transitions of the class one QFSM. Observe that as the output qubit is always being initialized to |0 only four possible initial states exists (see eq. 27). The third column representing the state transitions of the class two QFSM and as can be seen in this case the state transition function is the full "Fredkin oracle" function.
Moreover, the difference between the first and the second class of these QFSM’s has also deeper implications. Observe that the QFSM presented in this paper, if implemented without the measurement on the output and the input qubit (the measurement is executed only after l computational steps) the QFSM becomes the well-known two-way QFSM
Table 1.
Comparison of the state transition between the class one and class two QFSMs
[KW97] because the machine can be in superposition with the input and the output. This is equivalent to stating that the reading head of a QFSM is in superposition with the input tape as required for the time-quadratic recognition of the {anbn} language [KW97].
Observe that to represent the 1-way and the 2-way QFSM in the circuit notation the main difference is in the missing measurement operations between the application of the different CU (Controlled-U) operations. This is represented in Figures 15 and 16 for 1-way and the 2-way QFSMs, respectively.
Figure 15.
Example of circuit implementing 1-way QFSM.
Figure 16.
Example of circuit implementing 2-way QFSM.
An interesting example of QFSM is a machine with quantum controls signals. For instance a circuit with the input qubit in the superposition generating the EPR quantum state [NC00] is shown in Figure 17.
Figure 17.
Example of the EPR circuit used as a QFSM.
Observe the behavior of this QFSM as both class one and class two machine given in Table 2. In this case the distinction between the class one and class two machines is negligible because any measurement of the system collapses the whole system as the result of the entanglement present in it.
Table 2.
Comparison of the state transition between the class one and class two EPR circuit QFSM
Figure 17 shows that because of the entanglement this machine has two distinct possible recognizable sequences. When the machine uses exclusively the output qubit initialized to |0 the possible initial states are only |00 and |10 because the measurement of the output state resulting in |11〉→In0|10〉 and|01〉→In0|00〉.
4. Evolutionary algorithms and quantum logic synthesis
In general the evolutionary problem solving can be split into two main categories; not separated by the methods that each of the trends are using but rather by the problem representation and by the type of problem solved. On one hand, there is the Genetic Algorithm (GA) [Gol89, GKD89] and Evolutionary strategies (ES) [Bey01, Sch95] that in general represents the information by strings of characters/integers/floats and in general attempts to solve combinatorial problems. On the other hand the design of algorithms as well as state machines was traditionally done by the Genetic Programming (GP) [Koz94, KBA99] and the Evolutionary Programming (EP) [FOW66, ES03].
Each of this approaches has its particular advantages and each of them has been already more or less successfully applied to the Quantum Logic synthesis. In the EQLS field the main body of research was done using the Genetic Programming (GP) for the synthesis of either quantum algorithms and programs [WG98, Spe04, Lei04, MCS04] or some specific types of quantum circuits[WG98, Rub01, SBS05, SBS08, LB04, MCS05]. While the GP approach has been quite active area of research the Genetic Algorithm approach is less popular and recently only [LP08, YI00] were using a Genetic Algorithm for the synthesis of quantum circuits. However, it was shown in [LP09] that it is also possible to synthesize quantum finite state machines specified as quantum circuit using a GA. The difference between the popularity of the usage between the GP and the GA for EQLS is mainly due to fact that the problem space of quantum computing is not well known and is extremely large. Thus synthesizing quantum algorithms or circuits using the circuit approach (as in GA) can be much harder than using a rule-based or a program based approach (as in GP). Thus one could conclude that the GP approach deals only with the required information (programming, logic rules, relations) while the GA circuit based approach synthesize the overall unitary operator without any regards to the structure of the required information itself.
5. Genetic algorithm
A Genetic algorithm is a set of directed random processes that make probabilistic decisions - simulated evolution. Table 3 shows the general structure of a GA algorithm used in this work and this section follows this structure with the focus on the information encoding in the individuals and on the evaluation of the designed QFSMs that are created by the GA.
Table 3.
Structure of a Genetic Algorithm
5.1. Encoding/Representation
For quantum logic synthesis the representation that we use is based on the encoding introduced in [LPMP02]. This representation allows to describe any Quantum or Reversible circuit [LPG+03, LP02]. All individuals in the GA are strings of ordered characters (each character representing a quantum gate) partitioned into parallel Blocks (Figure 18). Each block has as many inputs and outputs as the width of the quantum array (five in the case
Figure 18.
Transformation of a QC from the chromosome (on the top) encoded string, to a final quantum circuit notation representation of this QC (on the right). Here SW is a Swap gate, H is a Hadamard gate and I is a Identity. In the middle there is one CCNOT (Toffoli) gate.
of Figure 18). The chromosome of each individual is a string of characters with two types of tags. First a group of characters is used to represent the set of possible gates that can be used in the individual string representation. Second, a single character ’p’ is used as a separator between parallel blocks of quantum gates. An example of a chromosome can be seen in Figure 18. In this encoding each space (empty wire or a gate) is represented by a character with appropriate decoding shown. Our problem-specific encoding was applied to allow the construction of as simple genetic operators as possible. The advantage of these strings is that they allow encoding of an arbitrary QL or RL circuit without any additional parameters. Several such parameters were used in previous research [LPG+03, LP05] and using them made the genetic algorithm more complicated. Please note that only the possibility to move gate characters, remove and add them to the chromosome consequently make it possible to construct an arbitrary circuit and also to modify this circuit in order to optimize it.
5.2. Initialization steps of GA
The GA requires an input file (c.f. Pseudo-Code 28 and Pseudo-Code 29) which specifies all input parameters and required settings.
However, for the clarity of explanation we focus only on particular settings required for the synthesis of the QFSM. The lines (20-38) shows the to search for a QFSM recognizing a sequence, first the measurement is required (line 20), the index of the output qubit is given (line 21) and finally the desired input sequence is given. This is done by both specifying the input value (here 0 or 1) and the probability of detection (here 1). Observe that the probabilities are specified as complex numbers with only the real component defined, e.g. (1,0). The use of complex coefficients for these observation probabilities is due to the fact that as in our previous work [LP05, Luk09] it allows to specify don\'t cares. For instance the coefficient (0,1) represents a logical don\'t care.
The GA has several other settings, (common to most of GA methods) but also requires to specify circuit specific parameters. The initial circuits are created with a random size within the interval specified by a maximal (tmax) and minimal number of segments (tmin) in each individual (chromosome). Thus the size of the chromosome is not limited during the lifetime of an individual to a precise value, rather each individual has a dynamically changing genome within the bounds defined by the above variables. The presented GA is a subclass of the Messy GA [GKD89].
Another important parameter is related to the cost of the implemented Quantum Circuit. Each evolutionary run has specified the minimal cost MinCost that represents the known minimum for the target function or device. If such minimal value is not known, a small value is used so that it always underestimates a possible minimal cost of the implementation. This circuit cost value is used in the cost function described in Section 5.4.1.
The input specifications also include the elementary quantum gates to be used as components, like the single qubit H, X, Y, Z or V gates and two qubit operations such as CNOT or CV, which are the building blocks of the quantum circuits to be found. The quantum gates are represented as quantum unitary (and Hermitian) matrices with the cost specified for each gate. This is shown in eq. 29, where for each input gate the number of wires and its cost is given as well. For instance, lines 66 to 69 in eq. 29 shows the unitary matrix of the CV gate[BBC+95], line 64 shows the number of qubits of this gate and the line 65 shows its cost.
Observe that each unitary matrix is specified by complex coefficients with real and imaginary component. For instance (1, 0) represents the real state while (0.5, 0.5) represents a complex state with coefficient1+i2.
In the presented experiments various sets of Quantum gates have been used but only the most succesful runs are presented. In particular only circuits with the most common gates are shown. These gates include single-qubit gates such as Pauli rotations, the V and V† gates, two-qubit gates such as CNOT, CV and CV† and three-qubit macros such as Toffoli gates.
5.3. Evaluation of synthesis errors in sequential quantum circuits
In order to properly evaluate a a QFSM for sequence detection the measurement operation must by applied on several occasions during the detection procedure. As was explained in the section 2, a quantum system must be measured in order for the information to be obtainable and readable in the macro world. Moreover, the measurement is a vital operation if one desires to reuse a quantum state. Recently, it was proven that a unknown quantum state cannot be completely erased [PB99] but is also easily understandable by observing the nature of the quantum computing.
The simplest explanation of the impossibility of completely erase an unknown state is due to the fact that there is no such a reversible quantum operation that would bring any quantum state to let’s say the |0 state. This is because every reversible operation is a permutation (even when it contains complex coefficients) and any operation that would achieve such a state reduction is inherently non reversible and by default non-quantum. An example of such non-reversible operation is shown in eq. 30.
Ξ|ψ〉→|0〉⇔Ξ=(1100)E31
Thus measuring a quantum state allows to determine its observable and consequently allows to apply a Unitary transformation that would generate the desired state. The model of computation for Quantum Finite State Machines proposed in [LPK09] is used here as model. Figure 19 shows steps of evaluation of a sequential Quantum Circuit. Observe that this QFSM has one qubit |q for state, one qubit |i for input and one qubit |o for output. From the classical point of view this can be seen as an instance of a Mealy finite state machine.
The synthesis process generates a unitary transformation matrix U, that during the evaluation is applied to the sequence of quantum states. Observe that both the input qubit and the output qubit must be measured in order to preserve a valid quantum state qubit |q as well as allow to properly restore both the input and the output qubit. After each iteration of the computation (application of the U operator) the output qubit is set to |0 while the input qubit is set to either |0 or |1 depending on the the user specifications from the input file.
Equation 31 shows the first and the last step of the QFSM evaluation for the detection of the input sequence starting with s = {10011001110001}. Note that the detection requires that for all the input values but the last one the output qubit is set to |0 and is set to |1 for the last character.
At the end of each evaluation sequence, the state of the output qubit and of the input qubit is determined by the measurement and can be reset with desired Unitary transformation to either |0 or to |1. The machine state qubit |q is known only at the beginning of each evaluation sequence. This means that the state of the qubit can be in superposition or an orthonormal state. This also means that the machine state can be a multi qubit state that can become entangled between the various state qubits.
Figure 19.
Schematic representation of the process of evaluation of a QC as a Measure-Many (one-way )QFSM in this work.
Finally, the evaluation process is recored as a set of probabilities denoted as p0(0) and p0(1). They represent the probability of observation of the desired output 0 or 1 during the sequence detection. In particular, in this case the overall correctness of the detection can be written as:
To evaluate the error of the detector either the eq. 32 was used as a direct measure (it represents the correctness of the detection with respect to the selected observables), or a more standard calculation was used. The eq. 33 shows the standard RMS error computation. Both of these error evaluations are compared in the experimental section of this work.
p(s)=1n([∑j=0n−2(1−pj0(0))2]+(1−pn−10(1))2)E34
5.4. Fitness functions of the GA
During the search for the QFSM’s a parameterized fitness function was used. This was done in order to allow the minimization for both the error and the cost of the synthesized Quantum circuit. This "weighted function" methodology was based on our previous experience in the evolutionary quantum Logic synthesis [LPG+ 03, LP05, LP09].
The parameterization allows to select the weight with which the error of the circuit and the cost of the circuit modifies the overall fitness value. The choice of this weight is left on the user that can decide what criteria of evaluation is more important. However, we experimentally determined some optimal settings that allowed correct circuits with minimal cost to be synthesized.
5.4.1. The cost function
The cost function is based on a parameter known as the minimum cost that is provided by the user and that permits to estimate a normalization constant. This means that the cost function acts as a bonus inversely proportional to the size of the circuit to the fitness function for a given estimated and unreachable minimum. In this work the cost function is defined by
G(c)=exp−(MinCost−Cost)2Cost22E35
where Mincost is the parameter given by the user and Cost, given by∑j−1kcj, is the sum of costs of all gates in the evolved circuit. Equation 34 was experimentally determined to be sensitive enough to influence both circuits far and close to the optimal cost.
5.4.2. The weighted fitness function
The weighted fitness functions used is shown in eq. 35 and an alternative version is in eq. 36. Both equations calculate the fitness value using the fitness function and the cost function together. In this case, the error of the circuit (QFSM) is calculated with respect to the overall probability of detecting the desired sequence as specified by eq. 32.
Each component of these weighted functions can be adjusted by the values of parameters and .
f3=α(1−e)+βG(c)E36
f3=α(1e+1)βG(c)E37
The reasons for these various fitness functions are the following:
to allow different selection pressures during the individual selection process,
by calibrating the cost to always underestimate the minimal possible size of the desired circuit it is possible to further manipulate the selection process.
the parameterization allows in the extreme cases to completely eliminate the cost component and thus also includes fitness functions solely based on the correctness of the circuit.
For instance the fitness function 35 is not equal to one, unless both the cost of the circuit and the error are minimal. Thus a GA using such a weighted function has more freedom for searching a solution, because the fitness function is now optimizing the circuit for two parameters. Similarly in the case of the fitness function 36 which decreases the value of the fitness of longer circuits, therefore preferring the shorter ones. Thus individuals with different circuit properties will have equal fitness value.
5.5. Other evolutionary settings
For the clarity and the focus of this paper we present the rest of the settings in the Table 4. Only the final parameters are shown and in particular only those that were used during the runs that generated the presented results. To sum it up, the SUS[Bak87] selection method was used with n = 4 individuals. The mutation operator was used both on the level of individual quantum gates but also on the level of the parallel blocks. The crossover was a two parent, two point recombination process that preserves the width of the quantum circuit by selecting cut points only between the parallel blocks.
Table 4.
Parameters of the GA used during the experiments.
5.6. CUDA acceleration
The CUDA framework was developed by NVIDIA for the growing usage of the GPU for computing tasks. The acceleration implemented in the GA is restricted only to the matrix calculation. Figure 20 shows where the CUDA acceleration is used.
Figure 20.
Schema representing the usage of the CUDA accelerator in the computation of a Quantum Circuit Matrix Representation.
The reason for using the accelerated matrix multiplication only during the matrix multiplication and not for the Kronecker matrix product is the fact that the Kronecker product is less computationally expensive as it requires only 2n ×2n multiplications while the matrix product requires 2n×2n multiplications and 2n additions. Moreover, in order to maximize the CUDA usage it is more optimal to use multiplication on matrices of the same dimensions without requiring to re-parameterize the CUDA device. This is the case in the matrix multiplication between each parallel block in a Quantum Circuit.
6. Experiments and discussion
The experiments carried in this section confirms the possibility to design classical sequence detectors using the 1-way (measure many) circuit-based QFSM’s model. The experimentation was done over a set of random sequences of various length. Each sequence was being tested for circuits with different number of state qubits. This was done in order to observe the role of embedding of the non-reversible sequence into a larger, reversible unitary matrix.
The general function that the QFSM generates on the output is described by the eq. 37
o(λ)={1ifj<length(s)0o.w.}E38
with being a symbol read from the input and j is the index of the symbol in the sequence. Thus the minimal condition for each sequence to be detected properly is that the amount of the states is large enough to embed all the zero output to one half of the truth table. this is a required consequence because the QFSM must have both the output function and the state transition function reversible.
The experimentation was done for 5 randomly generated binary sequences with 7, 10, 15, 20 and 35 binary digits each. The sequences are shown in eq. 38
Each sequence was synthesized using a circuit with 3,4,5 and 6 qubits. The six qubit circuit is large enough to embed even the largest sequence so as a reversible function is synthesizable. Figures 21, 24 and 27 shows some examples of obtained circuits for each of the sequences.
Figure 21 is an example of Quantum Circuit that was used to detect the s 7 sequence and does not use any probabilistic states. For the sake of understanding let us analyze the sequence detection procedure using this circuit. The desired sequence is s 7 = { 0 1 0 1 1 1 1 } thus the set of input states is given by{|0⊗F〉|00〉,|ϕ10〉,|ϕ00〉,|ϕ10〉,|ϕ10〉,|ϕ10〉,|ϕ10〉}, with | being the unmeasured component of the automata state. Naturally there are cases where it is going to be determined by the measurement but for the clarity it is left in symbolic form and thus allowing it to be in superposed or entangled state.
Figure 21.
s 7-sequence exact detector
The size of the QFSM is four qubits with two topmost qubits |q0, |q1 are the state qubits, |i is the input qubit and |o is the output qubit (Figure 21). Table 22 represents the consecutive states as obtained during the QFSM procedure described in this work (Section 5.3). In particular this QFSM shows that it recognize the given sequence without the use of any probabilistic or superposed states.
This can also be seen on the circuit matrix that can easily be build from the given sequence of gates. For clarity the state transition is also shown in the form of a equation (eq. 39).
Figure 22.
Four qubits s7-sequence detector with deterministic input and output states
Observe that two different steps can be clearly distinguished in eq. 39; first a standard step that acts directly on a previously generated machine state such as in steps s0, s3, s4, s5 and s6, second a step that requires explicit modification of the previous machine state, in particular a state that requires an initialization of the output and/or the input qubit, such as shown in steps s1 to s2. Observe that this particular sequence detector does not requires - for this sequence – any re-initialization of the input qubit as a result of previous step; the input qubit is not modified by the automaton. Also observe that despite the fact that this circuit can generate quantum states, these states are not generated during the sequence s7. This can be seen on Figure 23.
The states in a circle represent natural states as would be obtained by the cycle of the reversible function, while the states in the hexagons represents forced states that are obtained after modifying the input qubit. The Figure 23 also represents the forced states with one dotted arrow incoming and one outgoing dashed arrows. The arrow incoming to the forced state is indexed with a binary number representing the required input change so that the forced state is obtained. The outgoing arrow represents that the forced state is then used as a normal natural state; i.e. a Unitary transform is applied to it and the result is computed. For instance the s1 character recognition, starts with the machine in the state |0000, which is forced to |0010 and then the Unitary transformation is applied and yields |1110 state. The whole sequence detection can be in this manner analysed from eq. 39 and Figure 23.
Figure 23.
The cycle of a Reversible Circuit used as a detector for the s7 sequence
A more interesting example is shown in Figure 24. The displayed circuit also recognizes the same sequence s7 but in this case the automaton uses probabilistic and superposed quantum states. This can be seen in Table 25; this table has every row split in half so that it fits in size. For more details eq. 40 shows step by step the process of recognition performed by this automaton. Observe that as the result of the last step the output of the circuit is |o = |1 thus confirming the correct sequence has been detected.
Figure 24.
s7-sequence detector with probabilistic and superposed states
Four qubits s7-sequence detector with probabilistic input and output states
6.1. Sequence detection
The detection of a given sequence by the here formulated QFSM’s can be analyzed starting from Reversible Circuits. Assume, the initial state is 0000 for the rest of this discussion. As example take the reversible (deterministic) detector such as given in figure 21. It is obvious that the power of properly detecting a given sequence; i.e. to generate a sequence 0 × n + 1 × 1 is proportional to the cycle of this detector given by the reversible circuit for a fixed n and to the fact of having the cycle connected to a finish sequence either by a forced change of input or by a natural evolution.
To see this, just assume that the given circuit is specified by the following permutation cycle (0, 4, 8, 12)(2, 6, 10, 14)(1, 3, 5, 7, 9, 11, 13, 15). Clearly, the first cycle (0, 4, 8, 12) represents the states containing the 0 as input and 0 as output, the (2, 6, 10, 14) cycle represents the states having 1 for input and 0 as output and the last cycle represents all outputs having the output bit set to 1. The longest possible sequence this automaton can detect (without using the force states transitions) is of length 0 becausethe detecting cycle is disjoint from both the cycles identifying 0’s and 1’s.
For illustration assume the Reversible Circuit specifying the automaton be described by (0,6,4,2) (8,12,10,14,1) (3,5,7,9,11,13,15) permutation cycles. This automaton will not detect successfully any sequence if starting from the initial state 0000. This is shown in figure 26. Observe that no matter the input change of any of the state of the cycle (0,6,4,2) will always lead back to a state from the same cycle. Thus such a machine cannot generate a 1 on the output when starting in the state |0000.
Figure 26.
The cycle of a Reversible Circuit used as a detector
The Figure 26 shows that in order to have a successful detector, at least one natural transition |ϕ〉→U|ϕ\'〉 or a forced transition |ρ〉|i〉|o〉→|ρ〉|i〉|o〉 must lead to a cycle that allows to generate an output with value 1.
Now consider an Reversible Circuit defined by the permutations given by (0, 4, 8, 12, 3) (2, 6, 10, 14, 1) (5, 7, 9, 11, 13, 15). Such automaton now can detect any sequence that contains at least four consecutive 0’s or four consecutive 1’s. To maximize the length of a given sequence it is possible to allow the automaton to modify also its input qubit. In that case, as also seen in the presented protocol in the Section 5.3, the maximal complexity of the detected sequence is still equal to the sequence of maximum four 0’s and four 1’s.
The important cycles used for detection of the above specified Reversible circuit are shown in Figure 28. Observe that only two out of three cycles are shown as the last cycle contains all minterms that have 1 as output and thus can only be used as the end of sequence indicator. Also the cycles are shown only up to a final state. Thus for instance the state |0011 is not connected back to |0000 because once such state is attained the detection is terminated. Such specified detector will detect any sequence that terminates with four 1’s or four 0’s.
Figure 27.
Quantum circuits detecting the sequences s10 to s25
Figure 28.
The Reversible Circuit specified by the cycles (0,4,8,12,3) (2,6,10,14,1) (5,7,9,11,13,15) used as a detector
Finally observe that for a given sequence it is possible to design a detector that is either specified by only natural state transitions - as specified by the cycles of a reversible quantum function or by combining the cycle with forced transitions. The former method will always generate larger circuits while the later will allow more compact designs. However in the framework of the presented Quantum detectors these approaches are equivalent from the point of view of implementation. That is, at the begining of each computation cycle one need to know exactly the input quantum state. Thus the main adavantage in designing detectors with only natural state transitions resides in the fact that no initialization of the input qubit is required because it is set at the output of the previous computational cycle.
To close this discussion about the detectors it is possible to synthesize detectors using both purely Reversible or Quantum Unitary matrix. The size of the required circuit is dependent on the amount of continuous 0’s or 1’s however it is not restricted by it. It is straight forward to imagine such sequence detector that will have only smaller cycles and still will detect similar sequence. This is because if the unitary transform modifies the input qubit, smaller cycles can be combined to detect these particular sequences. For instance Figure 29 shows a portion of a detector specified by a Reversible circuit. This detector will detect among others the sequences terminating with three 0\'s or two 1\'s. Recall that only natural transitions are used for the detection procedure. Thus for instance in figure 29 |1110 changes to state |1100 when the input is changed from 1 to 0 and the consequent application of the Unitary matrix on this state generates an 1 on output. This is the final state, and it indicates that at least three 0 have been successfully detected before attaining it. The interested reader is encouraged to read more about reversible and quantum sequence detector in [LP09, LPK09].
Table 5 shows the minimum number of qubits that have been experimentally obtained in order to properly detect the sequences studied here. The sequence s7 has a sequence of four 1’s and a set of individual 1 and thus cannot be detected by less than circuit with 4 qubits.
Figure 29.
The Reversible Circuit detecting sequences ending with four 0’s or four 1’s.
Table 5.
Minimum number of qubits required to detect a sequence: experimental observations
The sequence s10 has two cycles at least: one with a sequence of two 1’s followed by a 0 and a sequence of three 0’s. It is also not possible to construct such detector on 3 qubits because the number of states required is at least 4 for both sequence and not counting the individual 0’s and 1’s. Similarly other sequences can be analyzed.
The Genetic Algorithm was run for multiple sizes for each sequence starting with three qubits. The search was terminated when a circuit satisfying the constraints was found and multiple searches were performed at the minimal width. Figure 30 shows the average of the Fitness value for the s7, s10 and s15 sequences. The drawings show each curve over 500 generation cycles required for the detection of each of the sequences after which the maximum generation is attained. Each curve is an average of 15 runs and the most interesting feature is that similarly to quantum function logic synthesis the algorithm finds a circuit that is very close to the complete solution and then stagnates before finding a solution. This problem is related to both the difficulty of synthesis as well as the fact that Quantum circuit are specified by Unitary matrices in which the error is of symmetric nature. Such error can be seen as a step function where with each step a pair of coefficient in the Unitary matrix is corrected.
Also observe how the fitness value stagnates with larger sequences with the presented qubits despite the fact that a solution was found for each sequence for the presented number of qubits. Interestingly, observe that the sequence s7 to s20 are from the same class as they have been identified by detectors of similar size. This goes back to the discussion above about the limits of a Quantum and Reversible circuit to recognize a particular class of sequences.
Figure 30.
Figures capturing the fitness average for four sequence detectors
7. Conclusion
In this paper we presented a methodology and we showed some experimental results confirming that our approach is possible in simulated environment. Also because all simulated elements of the presented experiments are based on existing Quantum operations, the simulated detectors are Quantum-realizable.
It is well known that the state assignment problem is a NP-complete problem [Esc93] and the finding a minimal State Assignment has been solved only for particular subsets of FSM’s [LPD95] or using Quantum computing [ANdMM08]. This problem is here naturally solved (without addressing it). The setup of this experimental approach automatically generates a state assignment such that when the detection is successful the state assignment is as well. This is a natural consequence of both the fact that the machine is reversible and the fact that the sequence is successfully identified.
The presented algorithm proved successful in the design of Quantum detectors. Despite the sequences were randomly generated the proposed approach was possible due to the hardware accelerated computational approach. For more details about this approach the reader can consult [LM09].
The synthesis of quantum detectors has not been completely explored and remains still an open issue mainly because Quantum computing implementation is not a well established approach. Each technology provides different possibilities and has different limitations. In some cases specification using Quantum circuits is the most appropriate in others Hamiltonians must be used. Thus one of the main remaining tasks is to completely describe Quantum detectors and formally define their issues related with implementation and define classes of circuits more approapriate for different technologies.
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Single-Qubit quantum gates",level:"2"},{id:"sec_5_2",title:"2.4. Multi-Qubit quantum gates",level:"2"},{id:"sec_6_2",title:"2.5. NMR-based quantum logic gates",level:"2"},{id:"sec_8",title:"3. Quantum finite state machines",level:"1"},{id:"sec_8_2",title:"3.1. 1-way quantum finite automata",level:"2"},{id:"sec_9_2",title:"3.2. Quantum logic synthesis of sequence detectors",level:"2"},{id:"sec_11",title:"4. Evolutionary algorithms and quantum logic synthesis",level:"1"},{id:"sec_12",title:"5. Genetic algorithm",level:"1"},{id:"sec_12_2",title:"5.1. Encoding/Representation",level:"2"},{id:"sec_13_2",title:"5.2. Initialization steps of GA",level:"2"},{id:"sec_14_2",title:"5.3. Evaluation of synthesis errors in sequential quantum circuits",level:"2"},{id:"sec_15_2",title:"5.4. Fitness functions of the GA",level:"2"},{id:"sec_15_3",title:"5.4.1. The cost function",level:"3"},{id:"sec_16_3",title:"5.4.2. The weighted fitness function",level:"3"},{id:"sec_18_2",title:"5.5. Other evolutionary settings",level:"2"},{id:"sec_19_2",title:"5.6. CUDA acceleration",level:"2"},{id:"sec_21",title:"6. Experiments and discussion",level:"1"},{id:"sec_21_2",title:"6.1. Sequence detection",level:"2"},{id:"sec_23",title:"7. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'AmbainisA.FreivaldsR.\n\t\t\t\t\t1 quantum finite automata: strengths, weaknesses and generalizations. 332341 , Nov 1998.'},{id:"B2",body:'M.P.M.\n\t\t\t\t\t\t\tAraujo, N.\n\t\t\t\t\t\t\tNedjah, L.\n\t\t\t\t\t\t\tde Macedo Mourelle. Quantum-inspired evolutionary state assignment for synchronous finite state machines. Journal of Universal Computer Science, 14(15):2532-2548, 2008.'},{id:"B3",body:'AmbainisA.WatrousJ.\n\t\t\t\t\tTwo-way finite automata with quantum and classical states. Theoretical Computer Science, 1\n\t\t\t\t\t287\n\t\t\t\t\t299311\n\t\t\t\t\t2002'},{id:"B4",body:'BakerJ. E. Reducing bias and inefficiency in the selection algorithm. In In Proceedings of the Second International Conference on Genetic Algorithms and their Application, 1421\n\t\t\t\t\t1421 1987.'},{id:"B5",body:'BarencoA.BennettC. H.CleveR.Di VincenzoD. P.MargolusN.ShorP.SleatorT.SmolinJ. A.WeinfurterH.\n\t\t\t\t\tElementary gates for quantum computation. The American Physical Society, 5\n\t\t\t\t\t34573467\n\t\t\t\t\t1995'},{id:"B6",body:'BeyerH. G.\n\t\t\t\t\tThe Theory of Evolution Strategies. Springer, 2001'},{id:"B7",body:'E.\n\t\t\t\t\t\t\tBernstein, U.\n\t\t\t\t\t\t\tVazirani. Quantum complexity theory. SIAM Journal of computing, pages 1411-1473, 1997.'},{id:"B8",body:'BlaisA.ZagoskinA. M.\n\t\t\t\t\tOperation of universal gates in a solid state quantum computer based on clean josephson junctions between d-wave superconductors. Phys. Rev. 61 61, 2000.'},{id:"B9",body:'GSL CBLAS.\n\t\t\t\t\thttp://www.gnu.org/software/gsl/manual/html node/GSLCBLAS-Library.html.'},{id:"B10",body:'NVIDIA CUDA.\n\t\t\t\t\thttp://www.nvidia.com/object/cuda learn.html.'},{id:"B11",body:'CiracJ. I.ZollerP.\n\t\t\t\t\tQuantum computation with cold trapped ions.\n\t\t\t\t\tPhysical Review letters, 74(20):4091 , 1995'},{id:"B12",body:'Di VincenzoP. Two-bit gate for quantum computation. Physical Review A, 501015 1995.'},{id:"B13",body:'DuanL. M.KuzmichA.KimbleH. J. Cavity QED and quantum-information processing with ‘hot’ trapped atoms. Physical Review 67 67, 2003.'},{id:"B14",body:'DunlaveyM. R. Simulation of finite state machines in a quantum computer, 1998'},{id:"B15",body:'EinsteinA.PodolskyB.RosenN. Can quantummechanical description of physical reality be considered complete? Phys. Rev., 47\n\t\t\t\t\t10\n\t\t\t\t\t777780 , May 1935'},{id:"B16",body:'A.E.\n\t\t\t\t\t\tEiben\n\t\t\t\t\tJ.E. Smith. Introduction to Evolutionary Computing. Springer, 2003'},{id:"B17",body:'EschermannB.\n\t\t\t\t\tState assignment for hardwired vlsi control units. ACM Comput. Surv., 4\n\t\t\t\t\t25\n\t\t\t\t\t415436\n\t\t\t\t\t1993'},{id:"B18",body:'FogelL. J.OwensA. J.WalshM. J.\n\t\t\t\t\tArtificial Intelligence through Simulated Evolution. John Wiley, 1966'},{id:"B19",body:'GoldbergD. E.KorbB.DebK. Messy genetic algorithms: Motivation, analysis and first results. Complex Systems, 3\n\t\t\t\t\t3493\n\t\t\t\t\t493530 .'},{id:"B20",body:'GoldbergD. E.\n\t\t\t\t\tGenetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, MA, 1989'},{id:"B21",body:'GrahamA.\n\t\t\t\t\tKronecker Products and Matrix Calculus With Applications. Ellis Horwood Limited, Chichester, U.K., 1981'},{id:"B22",body:'J.\n\t\t\t\t\t\t\tGruska. Quantum computing. Osborne/McGraw-Hill,U.S., 1999.'},{id:"B23",body:'KozaJ. R.BennettF. H.AndreD. Genetic Programming III: Darwinian Invention and Problem Solving. San Francisco, California: Morgan Kaufmann Publishers, 1999'},{id:"B24",body:'J.R.\n\t\t\t\t\t\t\tKoza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, 1994'},{id:"B25",body:'KondacsA.WatrousJ.\n\t\t\t\t\tOn the power of quantum finite state automata. In IEEE Symposium on Foundations of Computer Science, 6675 1997.'},{id:"B26",body:'LeierA.BanzhafW.\n\t\t\t\t\tComparison of selection strategies for evolutionary quantum circuit design. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 557568\n\t\t\t\t\t557568 2004.'},{id:"B27",body:'LeierA.\n\t\t\t\t\tEvolution of Quantum Algorithms Using Genetic Programming. PhD thesis, University of Dortmund, 2004'},{id:"B28",body:'LeeS.-JS.LeeT.Kim-SJ.LeeJ.BiamontePerkowskiM.\n\t\t\t\t\tThe cost of quantum gate primitives. Journal of Multiple Valued Logic and Soft Computing, 12(5/6):561574 , 2006'},{id:"B29",body:'MillerM.PerkowskiM.LukacM.KameyamaM. Evolutionary quantum logic synthesis: Representation vs. micro-parallelism- submitted, 2009'},{id:"B30",body:'LukacM.PerkowskiM. Evolving quantum circuit using genetic algorithm. In Proceedings of 2002 NASA/DoD Conference on Evolvable hardware, 177185 , 2002.'},{id:"B31",body:'LukacM.PerkowskiM. Combining evolutionary and exhaustive search to find the least expensive quantum circuits. In Proceedings of ULSI symposium, 2005'},{id:"B32",body:'LukacM.PerkowskiM.\n\t\t\t\t\tInductive learning of quantum behaviors. Facta Universitatis, special issue on Binary and Multiple-Valued Switching Theory and Circuit Design,\n\t\t\t\t\t2008'},{id:"B33",body:'LukacM.PerkowskiM. Quantum finite state machines as sequential quantum circuits. In Proceedings of ISMVL, 2009'},{id:"B34",body:'S.\n\t\t\t\t\t\t\tLiu, M.\n\t\t\t\t\t\t\tPedram, A. M.\n\t\t\t\t\t\t\tDespain. A fast state assignment procedure for large fsms. In DAC ‘95: Proceedings of the 32nd ACM/IEEE conference on Design automation, pages 327-332, New York, NY, USA, 1995. ACM.'},{id:"B35",body:'LukacM.PerkowskiM.GoiH.PivtoraikoM.YuC. H.ChungK.JeeH.B.KimG.Y.KimD. Evolutionary approach to quantum reversible circuit synthesis. Artif. Intell. Review., 20(3-4):361417 , 2003'},{id:"B36",body:'LukacM.PerkowskiM.KameyamaM. Sequential quantum devices: A circuit-based approach, 2009'},{id:"B37",body:'LukacM.PivtoraikoM.MishchenkoA.PerkowskiM. Automated synthesis of generalized reversible cascades using genetic algorithms. In Proceedings of Fifth Intern. Workshop on Boolean Problems, 3345\n\t\t\t\t\t3345 2002.'},{id:"B38",body:'LukacM.\n\t\t\t\t\tQuantum Logic Synthesis and Inductive Machine Learning, Ph.D. dissertation. PhD thesis, 2009'},{id:"B39",body:'MooreC.CrutchfieldJ. P.\n\t\t\t\t\tQuantum automata and quantum grammars. Theoretical Computer Science, 237\n\t\t\t\t\t275306\n\t\t\t\t\t2000'},{id:"B40",body:'MiJ.ChenC. Finite state machine implementation with single-electron tunneling technology. In ISVLSI 06 Proceedings of the IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures, page 237, Washington, DC, USA, 2006. IEEE Computer Society.'},{id:"B41",body:'MasseyP.ClarkJ. A.StepneyS.\n\t\t\t\t\tEvolving quantum circuits and programs through genetic programming. In Proceedings of the Genetic and Evolutionary Computation conference (GECCO), 569580 2004.'},{id:"B42",body:'MasseyP.ClarkJ. A.StepneyS. Evolving of a humancompetitive quantum fourier transform algorithm using genetic programming. In Proceedings of the Genetic and Evolutionary Computation conference (GECCO), 16571664 , 2005'},{id:"B43",body:'MooreG. E.\n\t\t\t\t\tCramming more components onto integrated circuits. In Electronics, April 19, 1965'},{id:"B44",body:'NielsenM. A.ChuangI. L.\n\t\t\t\t\tQuantum Computation and Quantum Information. Cambridge University Press, 2000'},{id:"B45",body:'ArunKumar.PatiSamuelL. Braunstein. Impossibility of deleting an unknown quantum state, 1999'},{id:"B46",body:'PachosJ.WaltherH.\n\t\t\t\t\tQuantum computation with trapped ions in an optical cavity.\n\t\t\t\t\tPhysical Review Letters, 8918 2002.'},{id:"B47",body:'Rong-CanY.Hong-CaiL.XiuL.Zhi-PingH.HongX.\n\t\t\t\t\tImplementing a universal quantum cloning machine via adiabatic evolution in ion-trap system. Communications in Theoretical Physics, 1\n\t\t\t\t\t49\n\t\t\t\t\t8082\n\t\t\t\t\t2008'},{id:"B48",body:'RubinsteinB. I. P. Evolving quantum circuits using genetic programming. In Congress on Evolutionary Computation (CEC2001), 114121\n\t\t\t\t\t114121 2001.'},{id:"B49",body:'StadelhoferR.BanzhafW.SuterD.\n\t\t\t\t\tQuantum and classical parallelism in parity algorithms for ensemble quantum computers. Physical Review\n\t\t\t\t\t71 71, 2005.'},{id:"B50",body:'StadelhoferR.BanzhafW.SuterD.\n\t\t\t\t\tEvolving blackbox quantum algorithms using genetic programming. Artif. Intell. Eng. Des. Anal. Manuf., 22\n\t\t\t\t\t285297\n\t\t\t\t\t2008'},{id:"B51",body:'H.P Schwefel.\n\t\t\t\t\tEvolution and Optimum Seeking. New York, Wiley & Sons, 1995'},{id:"B52",body:'ShorP. W.\n\t\t\t\t\tAlgorithms for quantum computation: Discrete logarithms and factoring. In Proc. 35 Annual Symposium on Foundations of Computer Science (Shafi Goldwasser, ed.), 124134 . IEEE Computer Society Press, 1994.'},{id:"B53",body:'SakaiA.KamakuraY.TaniguchiK.\n\t\t\t\t\tQuantum lattice-gas automata simulation of electronic wave propagation in nanostructures. 241242 Oct. 2004.'},{id:"B54",body:'SpectorL.\n\t\t\t\t\tAutomatic Quantum Computer Programming: A Genetic Programming Approach. Kluwer Academic Publishers, 2004'},{id:"B55",body:'WatrousJ.\n\t\t\t\t\tOn one-dimensional quantum cellular automata. In Proceedings of 36 36th Annual Symposium on Foundations of Computer Science, 528537 , 1995.'},{id:"B56",body:'WatrousJ.\n\t\t\t\t\tOn one-dimensional quantum cellular automata. In Proceedings of 36 36th Annual Symposium on Foundations of Computer Science (FOCS’95), 528532 , 1995.'},{id:"B57",body:'WatrousJ. On the power 2\n\t\t\t\t\t2 -way quantum finite state automata. Technical Report CS-TR-1997-1350, 1997.'},{id:"B58",body:'WilliamsC.GrayA.\n\t\t\t\t\tAutomated design of quantum circuits. In in Proceedings of QCQC 1998\n\t\t\t\t\t113125 , 1998.'},{id:"B59",body:'YakaryA.IlmazCemA. C.Say\n\t\t\t\t\tEfficient probability amplification in two-way quantum finite automata. Theoretical Computer Science, 410\n\t\t\t\t\t20\n\t\t\t\t\t19321941 . Quantum and Probabilistic Automata.'},{id:"B60",body:'YabukiT.IbaH. Genetic algorithms for quantum circuit design, evolving a simpler teleportation circuit. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 421425 2000.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Martin Lukac",address:null,affiliation:'
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Farias",authors:[null]},{id:"6262",title:"Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images",slug:"application_of_particle_swarm_optimization_in_accurate_segmentation_of_brain_mr_images",signatures:"Nosratallah Forghani, Mohamad Forouzanfar, Armin Eftekhari, Shahab Mohammad-Moradi and Mohammad Teshnehlab",authors:[null]},{id:"6263",title:"Swarm Intelligence in Portfolio Selection",slug:"swarm_intelligence_in_portfolio_selection",signatures:"Shahab Mohammad-Moradi, Hamid Khaloozadeh, Mohamad Forouzanfar, Ramezan Paravi Torghabeh and Nosratallah Forghani",authors:[null]},{id:"6264",title:"Enhanced Particle Swarm Optimization for Design and Optimization of Frequency Selective Surfaces and Artificial Magnetic Conductors",slug:"enhanced_particle_swarm_optimization_for_design_and_optimization_of_frequency_selective_surfaces_and",signatures:"Simone Genovesi, Agostino Monorchio and Raj Mittra",authors:[null]},{id:"6265",title:"Search Performance Improvement for PSO in High Dimensional Space",slug:"search_performance_improvement_for_pso_in_high_dimensional_space",signatures:"Toshiharu Hatanaka, Takeshi Korenaga, Nobuhiko Kondo and Katsuji Uosaki",authors:[null]},{id:"6266",title:"Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks by Particle Swarm Optimization",slug:"finding_base-station_locations_in_two-tiered_wireless_sensor_networks_by_particle_swarm_optimization",signatures:"Tzung-Pei Hong, Guo-Neng Shiu and Yeong-Chyi Lee",authors:[null]},{id:"6267",title:"Particle Swarm Optimization Algorithm for Transportation Problems",slug:"particle_swarm_optimization_algorithm_for_transportation_problems",signatures:"Han Huang and Zhifeng Hao",authors:[null]},{id:"6268",title:"A Particle Swarm Optimisation Approach to Graph Permutations",slug:"a_particle_swarm_optimisation_approach_to_graph_permutations",signatures:"Omar Ilaya and Cees Bil",authors:[null]},{id:"6269",title:"Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities",slug:"particle_swarm_optimization_applied_to_parameters_learning_of_probabilistic_neural_networks_for_clas",signatures:"Patrick Marques Ciarelli, Renato A. Krohling and Elias Oliveira",authors:[null]},{id:"6270",title:"Path Planning for Formations of Mobile Robots using PSO Technique",slug:"path_planning_for_formations_of_mobile_robots_using_pso_technique",signatures:"Martin Macas, Martin Saska, Lenka Lhotska, Libor Preucil and Klaus Schilling",authors:[null]},{id:"6271",title:"Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation",slug:"simultaneous_perturbation_particle_swarm_optimization_and_its_fpga_implementation",signatures:"Yutaka Maeda and Naoto Matsushita",authors:[null]},{id:"6272",title:"Particle Swarm Optimization with External Archives for Interactive Fuzzy Multiobjective Nonlinear Programming",slug:"particle_swarm_optimization_with_external_archives_for_interactive_fuzzy_multiobjective_nonlinear_pr",signatures:"Takeshi Matsui, Masatoshi Sakawa, Kosuke Kato and Koichi Tamada",authors:[null]},{id:"6273",title:"Using Opposition-based Learning with Particle Swarm Optimization and Barebones Differential Evolution",slug:"using_opposition-based_learning_with_particle_swarm_optimization_and_barebones_differential_evolutio",signatures:"Mahamed G.H. Omran",authors:[null]},{id:"6274",title:"Particle Swarm Optimization: Dynamical Analysis through Fractional Calculus",slug:"particle_swarm_optimization__dynamical_analysis_through_fractional_calculus",signatures:"E. J. Solteiro Pires, J. A. Tenreiro Machado and P. B. de Moura Oliveira",authors:[null]},{id:"6275",title:"Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling",slug:"discrete_particle_swarm_optimization_algorithm_for_flowshop_scheduling",signatures:"S.G. Ponnambalam, N. Jawahar and S. Chandrasekaran",authors:[null]},{id:"6276",title:"A Radial Basis Function Neural Network with Adaptive Structure via Particle Swarm Optimization",slug:"a_radial_basis_function_neural_network_with_adaptive_structure_via_particle_swarm_optimization",signatures:"Tsung-Ying Sun, Chan-Cheng Liu, Chun-Ling Lin, Sheng-Ta Hsieh and Cheng-Sen Huang",authors:[null]},{id:"6277",title:"A Novel Binary Coding Particle Swarm Optimization for Feeder Reconfiguration",slug:"a_novel_binary_coding_particle_swarm_optimization_for_feeder_reconfiguration",signatures:"Men-Shen Tsai and Wu-Chang Wu",authors:[null]},{id:"6279",title:"Application of Particle Swarm Optimization Algorithm in Smart Antenna Array Systems",slug:"application_of_particle_swarm_optimization_algorithm_in_smart_antenna_array_systems",signatures:"May M.M. Wagih and Hassan M. Elkamchouchi",authors:[null]}]}]},onlineFirst:{chapter:{type:"chapter",id:"68752",title:"Mediators of Impaired Adipogenesis in Obesity-Associated Insulin Resistance and T2DM",doi:"10.5772/intechopen.88746",slug:"mediators-of-impaired-adipogenesis-in-obesity-associated-insulin-resistance-and-t2dm",body:'\n
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1. Obesity-associated metabolic disease
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Rapidly changing lifestyle, accompanied by consumption of excess energy in the form of a calorie-rich high-fat diet, lower voluntary activity, and increased exposure to environmental pollutants, have led to an exponential rise in noncommunicable metabolic diseases [1]. A key component of chronic metabolic diseases is obesity that has become a global health problem associated with a range of comorbidities including insulin resistance and type 2 T2DM [2], coronary artery disease (CAD) [3], nonalcoholic fatty liver [4], cancers [5], and elevated risk of premature death [6, 7].
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Adipose tissue is an endocrine organ that responds to obesity by secreting elevated quantities of free fatty acids, adipokines, and proinflammatory cytokines, triggering IR and risk of T2DM [8]. Obesity is also characterized by increased adiposity mediated by enlarged size of mature adipocytes (hypertrophy) and elevated number of newly recruited adipocytes (hyperplasia) [9, 10, 11, 12]. Adipose tissue dysfunction is characterized by adipocyte hypertrophy, mild chronic inflammation, and oxidative stress, causing reduced ability to generate new adipocytes from the undifferentiated precursors (preadipocytes). The impaired adipogenesis increases risk of IR and T2DM by triggering ectopic fat deposition in nonadipose tissues and proinflammatory environment characterized by impaired secretion of various adipose-derived adipokines [13].
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Obesity also represents an imbalance between the primary site of storing energy (the white fat) and the site that is specialized in energy expenditure (the brown fat) [14]. White adipocytes store fat in the form of triacylglycerols as a single fat lipid droplet that gets readily hydrolyzed by lipases when energy is needed. The resulting fatty acids are mobilized to other tissues to undergo fatty acid oxidation as a source of energy [15]. The imbalance between lipolysis and lipogenesis plays a crucial role in progression of metabolic disease including T2DM and nonalcoholic fatty liver disease [16]. The brown fat, on the other hand, uses the energy derived from fatty acid oxidation for heat generation [17].
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Adipocyte hypertrophy is associated with increased uptake of excess TAGs, which triggers fat accumulation within the larger subcutaneous adipose tissue (SAT) [18, 19, 20]. SAT therefore plays a buffering role as it prohibits progression of obesity-associated pathologies [21]. However, the buffering capacity becomes limited as impairment of SAT expansion causes IR [22, 23, 24] as the excess fat are deposited in the visceral adipose tissue (VAT) as well as ectopically in the skeletal muscle, liver, kidney, and heart tissues [25]. This is augmented by the infiltration of macrophages and activation of the innate immune cells [26], which triggers hyperinsulinemia that inhibits lipolysis and activates lipoprotein lipase (LPL). This causes further hyperinsulinemia, hypertriglyceridemia, increased IR in these tissues [27], and risk of T2DM [28].
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Although obesity is generally associated with these comorbidities, some obese individuals seem to be protected as they maintain insulin sensitivity (IS) and show lower hypertension and proatherogenic and inflammatory profiles than their equally obese pathogenic counterparts [29, 30, 31, 32]. Investigating the underlying causes for this protective phenotype could potentially help obesity-associated pathogenicity. Although still unknown, various potential mechanisms were proposed to contribute to metabolically healthy obese (MHO) phenotype. These include lower visceral and ectopic fat deposition than subcutaneous fat accumulation due to efficient SAT adipogenesis, reduced inflammatory component in the adipose tissue, healthy levels of secreted adipokines, and more active lifestyle [33]. A genetic component was also suggested to interact with various environmental factors, although not yet determined [34]. Interestingly, lean diabetics also exhibit larger adipocytes than healthy individuals, perhaps due to impaired differentiation of preadipocytes but not a result of different frequencies of stromal vascular cells, lipolysis, or levels of inflammatory mediators [35]. Current therapeutic strategies focus on treating obesity-associated diseases instead of preventing the underlying mechanisms. Therefore, understanding the molecular mediators underlying the protective phenotype in MHO individuals could provide critical information to help individuals suffering from pathological obesity (PO). In this review, we aimed to understand the role of adipogenesis in obesity-associated IR and T2DM by screening 2317 articles investigating adipogenesis and mediators of impaired adipogenesis in PubMed with the aid of Rayyne, a systematic review web application [36].
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2. The role of adipogenesis in obesity-associated IR and T2DM
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The adipose tissue is a dynamic part of the endocrine system that plays a crucial role in maintaining energy balance and nutritional homeostasis [37]. Mature adipocytes constitute the most abundant distinctive cell type in the adipose tissue, occupying 90% of its volume [38]. Other components include leukocytes, macrophages, fibroblasts, endothelial cells, and preadipocytes, which constitute the stromal vascular cells (4–6 million cells per gram of adipose tissue, half of which are immune cells) [39].
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Obesity-induced adipocyte hypertrophy is associated with impaired recruitment and differentiation of preadipocytes. Despite their abundance, preadipocytes fail to undergo terminal differentiation into mature adipocytes via the activation of the canonical Wnt signaling [40]. Preadipocytes are produced by mesenchymal stem cells (MSCs) under the influence of different signaling molecules. The mature adipocytes secrete BMP4 that triggers preadipocyte differentiation by inducing the separation of Wnt1 inducible-signaling pathway protein 2 (WISP2) and zinc finger protein 423 (ZNF423), allowing ZNF423 to translocate into the nucleus and activate peroxisome proliferator-activated receptors (PPARγ) and downstream cascade including CCAAT/enhancer-binding proteins β (C/EBPβ), δ, and α [41, 42] (\nFigure 1\n).
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Figure 1.
Schematic representation of the role of Wnt signaling in adipogenesis.
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BMP4 also plays an anti-inflammatory role by reducing tumor necrosis factor-α (TNF-α)-mediated proinflammatory cytokine induction in human adipocytes. Therefore, BMP4 plays a protective role against IR and T2DM [43]. Subsequently, PPARγ and C/EBPα activate preadipocyte differentiation and the expression of mature makers such as adiponectin, fatty acid-binding protein 4 (FABP4), glucose transporter type 4 (GLUT4), and LPL. The activation of PPARγ, therefore, maintains IS and exhibits an anti-inflammatory function, whereas IR causes impaired adipogenesis and increased risk of T2DM [44, 45].
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Insulin and downstream Akt signaling also play important roles as modulators of adipose tissue growth and adipogenesis as insulin activates glucose and free fatty acid uptake, inhibits lipolysis, and de novo fatty acid synthesis in adipocytes, and induces adipogenesis [46]. The transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) has been shown to induce energy expenditure and reduce adipose tissue growth, leading to prevention of dietary obesity and lowering adipogenesis, inflammation, and IR [47]. The inhibition of inhibitor of nuclear factor kappa-B kinase subunit β (IKKβ) in mice lowers high-fat diet-induced adipogenesis and inflammation and protects from diet-induced obesity and IR [48]. MicroRNAs (miRNAs) have been also shown to play an important role in adipogenesis, IR, and inflammation as previously reviewed [49].Tonicity-responsive enhancer-binding protein (TonEBP), a key transcription factor involved in cellular adaptation to hypertonic stress, has been suggested to influence macrophage activity, adipogenesis, and IS by inhibiting the epigenetic transition of PPARγ2 [50]. Protectin DX (PDX), a ω-3 fatty acid-derived proresolution mediator, was reported to reduce inflammation and IR via an AMPK-dependent pathway and suppress adipogenesis and lipid accumulation during 3T3-L1 differentiation [51].
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We have recently shown that higher adipogenic capacity of preadipocytes isolated from SAT and VAT from MHO individuals than PO counterparts may be one of the underlying mechanisms for MHO protection due to a greater ability to store TAGs in the SAT depot. This process was shown to be influenced by inflammatory mediators, oxidative stress, and fatty acid signaling [45, 52, 53, 54, 55].
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3. Mediators of impaired adipogenesis in IR and T2DM
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3.1 Inflammatory mediators
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3.1.1 Impaired adipogenesis in response to proinflammatory signals
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Obesity-associated comorbidities are mediated by chronic mild inflammation (\nFigure 2\n). Lipid-laden adipocytes produce increased levels of cytokines such as Interleukin 6 (IL-6), IL-β, TNF-α, monocyte chemoattractant protein-1 (MCP-1), and IL-8 [10, 56, 57] which can inhibit preadipocyte differentiation [21, 45]. The impaired adipogenesis is associated with stress of the endoplasmic reticulum (ER) and elevated expression of unfolded protein response (UPR), both can exacerbate the proinflammatory phenotype of preadipocytes and adipocytes [58]. The effect of proinflammatory phenotype varies among various fat depots. VAT is a more inflammatory tissue than SAT as it secretes higher levels of proinflammatory cytokines. Macrophage infiltration into adipose tissue is regulated through serum resistin and leptin in obese individuals with early metabolic dysfunction [59]. The presence of macrophages in VAT contributes significantly to this phonotype. The presence of macrophages in human SAT, on the other hand, is causally related to impaired preadipocyte differentiation, which in turn is associated with systemic IR [60, 61]. Adipocyte differentiation, therefore, was shown to be significantly lower in VAT than SAT. Macrophage depletion can reduce inflammatory cytokines and trigger adiponectin secretion from both SAT and VAT adipocytes, leading to the induction of preadipocyte differentiation in SAT, but not VAT. Additionally, a negative correlation between SAT adipogenesis, but not VAT, and systemic IR was observed [62]. Chronic systemic inflammation is also associated with elevated lipolysis in white adipose tissue and lipogenesis in nonadipose tissues, causing ectopic fat deposition in nonadipose tissues and imbalance in free fatty acid homeostasis and increased risk of IR [63].
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Figure 2.
Mediators of impaired adipogenesis in IR and T2DM. Most proinflammatory cytokines as well as some anti-inflammatory mediators can impair adipogenesis (1). Similarly, various mediators of oxidative stress can impact adipogenesis both positively and negatively depending on their structure (2). Fatty acid signaling plays a key role in adipogenesis but at various degrees depending on the composition of the fatty acids (3). Finally, various environmental factors can impact adipogenesis mostly negatively (4).
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Among the proinflammatory cytokines, IL-6 is produced by adipocytes, activated leukocytes, and endothelial cells [64] in obesity [65, 66, 67, 68]. IL-6 shows a synergistic effect with other mediators of metabolic disease, collectively contributing to the progression of other obesity-associated comorbidities such as CAD and T2DM [64, 69]. IL-6 impairs the LPL function leading to increased levels of circulating fat [69, 70]. Moreover, obesity-associated increase in IL-6 is linked to reduced insulin-triggered glucose uptake [60, 61]. Previous reports have indicated that insulin treatment improves the glucose transport activity of adipocytes in T2DM [21] and lowers IL-6 and TNF-α levels [53]. Although the precise mechanisms of IL-6-associated IR is not well characterized, human adipocytes from IR individuals were shown to exhibit significantly higher IL-6 expression levels [45]. IL-6 impairs insulin action by inhibiting expression of insulin receptor, insulin receptor substrate-1 (IRS-1), and GLUT4 in human preadipocytes as well as 3T3-L1 adipocytes [45, 71]. Furthermore, IL-6 was shown to reduce IS through decrease in adiponectin expression and secretion [72] and via impairment of insulin signaling in hepatocytes [73].
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Various other cytokines have been shown to impact adipogenesis [74]. The proinflammatory cytokines IL-1 β, TNF-α, and MCP1 can also influence the hyperplastic expansion of adipose tissue and impair adipogenesis [59]. IL-1β triggers a proinflammatory response in human adipose tissues, particularly in VAT depot. IL-1β also inhibits insulin signal transduction, leading to impaired IS in adipose tissue [75]. IL-1β and cyclooxygenase-2 (COX-2) play a detrimental role in adipose tissue dysfunction in obesity [76]. With obesity, levels of MCP-1 and TNF-α increase in VAT before macrophage infiltration, suggesting a highly proinflammatory phenotype of the visceral depot prior to infiltration of immune cells and macrophage phenotype switch [77]. Unlike IL-6, IL-1 β, and TNF-α, MCP-1 and MCP-1-induced protein (MCPIP) were shown to induce adipogenesis. Treatment of reactive oxygen species (ROS) inhibitor, apocynin, reduced the MCPIP-triggered adipogenesis [78]. Other cytokines involved in adipogenesis include interferon-γ (IFN-γ), a central mediator of macrophage function. Compared to obese wild-type control animals, obese IFN-γ knockouts exhibit better IS, smaller adipocyte size, and lower cytokine expression [79].
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3.1.2 Impaired adipogenesis in response to anti-inflammatory signals
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Contrary to the notion that inflammation plays a negative role in metabolism, some studies suggest that proinflammatory signals in the adipocytes are actually needed for functional adipose tissue homeostasis (\nFigure 2\n). Indeed, adipose tissue inflammation was shown in various animal models of adipose tissue-specific reduction of proinflammatory potential to be required as an adaptive response, allowing proper storage of excess fat and filtering of gut-derived endotoxins [80]. Additionally, various molecules with anti-inflammatory properties were shown to influence adipogenesis and risk of IR. Myokines, for example, secreted by skeletal muscle cells during exercise such as β-aminoisobutyric acid, can impair adipogenesis via activating AMPK signaling pathway and reducing levels of proinflammatory cytokines such as TNF-α [81]. Another example is the ubiquitin-editing enzyme A20 that impairs IL-6 secretion from adipocytes, leading to modulation of differentiation of MSCs [82]. The overexpression of A20 was also shown to reduce lipogenesis and adipogenesis via lowering levels of sterol regulatory element binding protein-1c (SREBP-1c) and aP2, causing lower fat accumulation in differentiated 3T3-L1 cells [83]. A third example is the nonerythropoietic EPO-derived peptide that plays an anti-inflammatory and anti-adipogenic roles in high-fat die mice with IR [84]. On the other hand, other anti-inflammatory molecules could rescue impaired adipogenesis. Glucose-dependent insulinotropic polypeptide (GIP), for example, is a potent activator of adipogenesis through modulation of inflammation in adipose tissue [85]. Additionally, the expression of neuronatin (Nnat), a proteolipid involved in neuronal development, in response to inflammation and dietary excess, has been suggested to play an important role in adipogenesis through lowering oxidative stress and inflammation [86].
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3.2 Oxidative stress
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Obesity leads to the accumulation of ROS, the hallmark of oxidative stress, in the adipose tissue causing impaired adipogenesis and increased risk of IR and T2DM. The balance between ROS generation and activation of endogenous antioxidants is crucial for cells undergoing adipogenesis [87] (\nFigure 2\n). The oxidative damage and changes in the expression of antioxidant enzymes with age are similar between SAT and VAT. However, preadipocytes from SAT are significantly more resistant than VAT-derived cells to cell death caused by oxidative stress [88]. Interestingly, within SAT and VAT depots, preadipocytes from insulin-sensitive obese subjects were more prone to oxidative damage than preadipocytes from equally obese insulin-resistant individuals [52, 53]. The depletion of ROS from adipose tissue in mice models of oxidative stress was associated with increased adipose tissue mass, lower ectopic fat deposition, and enhanced IS. Similarly, ROS accumulation limited the expansion of adipose tissue, leading to elevated ectopic fat accumulation and increased risk of IR [89]. Elevated ROS within the adipose tissue triggers lipid peroxidation [45] and accumulation of reactive aldehydes including the bioactive lipid peroxidation product 4-hydroxynonenal (4-HNE) [90]. Elevated 4-HNE causes damage of cell structure and function through the formation of the stable adducts 4-hydroxyalkenals with proteins, phospholipids, and DNA [91, 92]. Increased 4-HNE levels have been associated with impaired adipogenesis and IR [53, 93, 94, 95, 96]. Another marker of oxidative damage is 8-hydroxy-2-deoxyguanosine (8-OHdG) which was recently shown to exert anti-inflammatory effects, by reducing TNF-α-induced IR in vitro. It was also shown to reduce adipose tissue mass in vivo through activation of adipose triglyceride lipase and lowering the expression of fatty acid synthase [97]. Levels of cholesterol oxidation-derived oxysterols increase in adipose tissues of T2DM patients and act as inhibitors of adipogenesis through activation of Wnt pathway [98]. Heme oxygenase (HO), a major cytoprotective enzyme, functions upstream of Wnt signaling and lowers lipogenesis and adipogenesis, decreasing lipid accumulation and levels of proinflammatory cytokines [99].
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Conversely, ROS was also shown to enhance adipogenesis by lowering sirtuin 1 (Sirt1) expression [100, 101]. Heme-induced oxidative stress was shown to inhibit Sirt1, leading to increased adipogenesis [102]. The expression of deleted in bladder cancer protein 1 (DBC1), another inhibitor of the Sirt1, is reduced with obesity, leading to lower adipogenesis and VAT dysfunction [103]. Sirt3 plays a crucial role in mitochondrial function. Silencing of Sirt3 can cause adipocyte dysfunction which impairs adipogenesis and causes IR [104]. Nonselenocysteine-containing phospholipid hydroperoxide glutathione peroxidase (NPGPx) is a sensor of oxidative stress. Lack of NPGPx causes elevation in ROS and promotion of adipogenesis through ROS-dependent dimerization of protein kinase A regulatory subunits and activation of C/EBPβ [105]. Additional evidence suggesting ROS involvement in promotion of adipogenesis comes from antioxidant supplementation experiments where lower levels of ROS resulting from antioxidants contribute to adipose tissue dysfunction and IR [106]. Indeed, antioxidant supplementation exhibited a negative impact when used before induction of oxidative stress as a result of lowering physiological ROS levels because ROS plays a role as second messengers in adipogenesis, lipid metabolism, and insulin signaling [107]. For example, the supplementation with N-acetylcysteine, a known antioxidant and precursor of glutathione, was shown to reduce fat deposition during adipogenic differentiation of mouse fibroblasts [108]. Activation of beta-3 adrenergic receptor (β3-AR) enhances ROS accumulation in cultured adipocytes. Antioxidants enhance β3-AR-triggered mitochondrial ROS production, suggesting that chronic supplementation of antioxidants could indeed generate an elevation in oxidative stress associated with mitochondrial dysfunction in adipocyte [109]. On the other hand, glutathione depletion was shown to inhibit adipogenesis as the result of lowering cell proliferation during the initial mitotic clonal expansion of the adipocyte differentiation process [110].
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3.3 Fatty acid signaling
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The main role of adipocytes is TAG storage. Although TAGs do not function as signaling molecules per se, the lipid intermediates generated during lipogenesis and lipolysis influence intracellular insulin signaling and participate in progression of IR. These include free fatty acids, diacylglycerols (DAGs), and ceramides [111].
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Lipolysis-driven efflux of fatty acids triggers TAG synthesis and causes stress of the ER and activation of June kinase pathway in the adipose tissues [112, 113]. This leads to an elevation in the levels of both DAGs and ceramides and progression of IR in adipocytes [114]. Ceramides were shown to influence lipid-mediated IR in muscles. Delta 4-desaturase, sphingolipid 1 (DEGS1) is a desaturase that mediates ceramide biosynthetic pathway. Ablation of DEGS1 in preadipocytes prevented adipogenesis and decreased lipid accumulation [115]. There are essential enzymes responsible for TAG hydrolysis including hormone-sensitive lipase (HSL), adipose triglyceride lipase (ATGL), and monoglyceride lipase (MGL) [116]. ATGL regulates lipolysis by transcription factor specificity protein 1 (Sp1). Insulin-mediated transcription of Sp1 is critical for this regulation. In mature adipocytes, PPARγ reverses transcriptional repression by Sp1 at the ATGL promoter, leading to stimulation of ATGL mRNA expression. During obesity and IR, the transcription of ATGL becomes downregulated. The extent of the downregulation depends on interactions between Sp1 and PPARγ [117].
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A number of factors influence the function of fatty acids in regulating adipogenesis. The number of carbons and the position and number of double bounds are crucial determinants of properties of the fatty acids. Changes in fatty acids including elongation, desaturation, β-oxidation, peroxidation, and incorporation into phospo- and complex lipids can play an essential role in their metabolic function. Fatty acids and their metabolites can control protein expression involved in lipid and energy metabolism by influencing gene transcription, mRNA processing, and posttranslational modifications [118, 119, 120, 121]. Most fatty acids activate all three members of the PPAR family [122, 123, 124, 125]. Polyunsaturated fatty acids (PUFAs), except for erucic acid, are more potent stimulators of PPARγ than monounsaturated fatty acids (MUFAs) and saturated fatty acids [122, 123, 124, 125, 126] (\nFigure 2\n). The optimal binding affinity is reached with 16–20 carbon-containing compounds. DHA too was shown to stimulate PPARs [124]. Various studies have reported the beneficial effects of PUFAs on lipid-related human disorders [127, 128, 129, 130, 131], which largely depend on the structure of the fatty acids and their metabolic properties. PUFAs can inhibit lipogenic gene transcription by downregulating the expression SREBPs [132, 133, 134, 135] and act as antagonists of liver X receptors (LXR) [136, 137] and as agonists for PPARs [122, 123, 124, 138, 139]. PUFAs, but not saturated or MUFAs, inhibit lipogenic genes by downregulating SREBP-1c. PPAR alpha plays an important role in metabolic adaptation to fasting by enhancing mitochondrial and peroxisomal fatty acid oxidation and ketogenesis [140]. Dietary PUFAs were also shown to stimulate expression of PPARα target genes, induce β-oxidation, and lower plasma TAGs [141, 142, 143, 144, 145, 146, 147, 148, 149]. Fatty acids can also play a role as modulators of kinase signaling pathways [150, 151, 152, 153, 154, 155].
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Arachidonic acid (AA), a polyunsaturated omega-6 fatty acid, is the major PUFA that has been implicated in the regulation of adipogenesis. Short exposure of 3T3-L1 mouse preadipocytes to AA triggers adipocyte differentiation, associated with increase in (FABP4/aP2). Calcium, protein kinase C, and ERK play critical role in this pathway through which AA induces the expression of adipocyte protein 2 (aP2) [156]. AA binds to PPAR-γ2 to stimulate GLUT4 expression in HepG2 cell line, exhibiting an alternative insulin-independent activation of GLUT4 [157]. AA cascade is then controlled by cyclooxygenases enzymes, lipoxygenases, and P450 epoxygenases. When AA is generated from plasma membrane via phospholipases and then metabolized by prostaglandin G/H synthase, different prostaglandins are produced, causing opposing effects on adipocyte differentiation. The proadipogenic effect of AA is mediated by prostaglandin product (prostacyclin) and is thus cyclooxygenase dependent [158, 159, 160]. Among prostaglandin classes, 15-deoxy-Δ12,14-prostaglandin J2 (15-d-PGJ2) was shown to be proadipogenic [161, 162]. On the other hand, prostaglandin F2α (PGF2α) was shown to exert anti-adipogenic effects in primary preadipocytes [163, 164, 165], 1246 cells [164], and 3T3-L1 cells [166, 167, 168]. The anti-adipogenic effect of PGF2α is mediated through prostaglandin F receptor-mediated elevation in intracellular calcium and DNA synthesis [168] and activation of MAPK, causing reduction in PPARγ phosphorylation [169]. The role of prostaglandin E2 (PGE2), the third main prostaglandin, in adipogenesis is controversial as PGE2 exhibits antilipolytic effect in mature adipocytes but shows no effect on preadipocytes [170]. However, it was recently demonstrated that PGE2 inhibited adipogenesis of 3T3-L1 cells [171, 172]. Epoxyeicosatrienoic acids (EETs), AA metabolites, and AA-derived cytochrome P450 (CYP) epoxygenase metabolites exert anti-inflammatory effects in the vasculature. The expression of CYP2J, a member of P450 subfamily with a role in the bioactivation of AA in extrahepatic tissues, inhibits NF-κB and MAPK signaling pathways and activates of PPARγ, thus reducing IR and diabetic phenotype [173]. n-3 PUFAs, on the other hand, reduce adipose growth and play a role in adipogenesis in various rodent studies [174, 175, 176, 177, 178, 179, 180, 181, 182, 183].
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Medium-chain fatty acids (MCFAs) (C8–C10) bind the PPARγ ligand binding domain in vitro, causing full inhibition of phosphorylation of PPARγ by cyclin-dependent kinase 5 (cdk5) and reversal of IR in adipose tissue. MCFAs that bind PPARγ also inhibit thiazolidinedione-dependent adipogenesis in vitro [184]. On the other hand, MUFAs were shown to induce adipogenesis and enhance TAG accumulation in 3T3-L1 mouse preadipocytes. Levels of TAGs were greater in cells treated with c-22:1 than c18:1 and c-20:1. Among the c-22:1 fatty acids, c9–22:1 treatment showed higher fat accumulation, associated with increased expression of adipogenic and lipogenic transcription factors, such as PPARγ and C/EBPα and SREBP-1. However, c-20:1 FAs exhibited less effect than c-18:1 and c-22:1 [185]. Alpha-lipoic acid (ALA) activates insulin signaling pathway and exerts insulin-like properties in adipose and muscle cells. However, 3T3-L1 preadipocytes treated with LA exhibit lower insulin-induced differentiation by modulating activity and/or expression of various anti-adipogenic transcription factors mainly through activating the MAPK pathways that negatively regulate PPARγ and C/EBPα [141]. 10-oxo-12(Z)-octadecenoic acid, a linoleic acid metabolite, triggered adipocyte differentiation through PPARγ activation and elevated adiponectin secretion and insulin-triggered glucose uptake [142]. Dietary n-3 fatty acids showed more effective activation of PPARα in the liver of rodents [143, 144, 145] than n-6 fatty acids [146]. \nFigure 3\n summarizes the effect of various fatty acid species on the proadipogenic capacity of 3T3L-1 cells in the presence or absence of insulin (Madsen et al.) [147].
\n
Figure 3.
Adipogenic capacity of various fatty acids in 3T3L-1 cells in the absence or presence of 1 μg/ml insulin in differentiation medium (MDI) containing 0.5 mM isobutyl-1-methylxanthine and 1 μM dexamethasone in DMEM and 10% FBS. 100 μM palmitic acid (palm), oleic acid (ole), erucic acid, linoleic acid (LA), arachidonic acid (AA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), or 1 μM rosiglitazone (rosi) dissolved in DMSO were added when differentiation was induced at day 0 and were present throughout the differentiation period (adapted from Madsen et al.) [147].
\n
Lipidomics studies were performed to investigate differences between SAT and VAT depots. These studies have shown evidence of depot-specific enrichment of certain species of TAGs, glycerophospholipids, and sphingolipids and specific correlations between certain lipid species and body mass index, inflammation, and IS [148, 149]. We have recently shown in human SAT and omental (OM) adipose tissue biopsies from 64 obese individuals a number of TAGs that changed with increased risk IR and T2DM including C46:4, C48:5, C48:4, C38:1, C50:3, C40:2, C56:3, C56:4, C56:7, and C58:7. Enrichment analysis showed C12:0 fatty acid to be associated with TAGs that are least abundant in T2DM. Our data also indicated that C18:3 was present in both depleted and enriched TAGs in T2DM [55]. Secretion of interleukin IL-6 was found to be significantly lower after treatment with C18:2, C22:6, and C16:0 through blocking NF-κB and activating PPARγ [186]. Our data also showed positive correlations between C56:4 and C57:4, both containing C18:2 and C16:0, with SC adipogenic capacity. OM adipogenic capacity was associated with C49:1, C38:0, and C56:2, containing C16:0, C18:1, and C14:0 [55]. \nTable 1\n summarizes a list of TAGs associated with SAT and OM adipogenic capacity. These fatty acids were reported to stimulate adipogenesis in rodents [187, 188, 189, 190, 191] and potentially in human preadipocytes.
List of TAGs associated with IR, SC, and OM adipogenic capacity.
\n
\n
\n
\n
4. Environmental factors
\n
Various types of environmental factors were shown to influence adipogenesis. These include environmental pollutants. Among the environmental pollutants, polybrominated diphenyl ethers (PBDEs) represent a widely used type of flame retardants in commercial products and a main source of environmental contaminants. PBDEs accumulate in adipose tissue, potentially changing its endocrine function causing elevation in the risk of IR. We have previously shown that specific congeners of PBDEs (28, 47, 99, and 153) were predominant in VAT from obese individuals and that PBDEs 99, 28, and 47 were elevated in obese IR compared to obese IS. Treatment of human VAT-derived preadipocytes from obese IS individuals with PBDE28 inhibited insulin signaling and reduced adipogenesis [54]. In addition to PBDEs, evidence linking accumulation of other persistent organic pollutants (POPs) and risk of IR and T2DM was previously described [54, 192]. Additionally, the association between inorganic arsenic exposure and the risk of T2DM and obesity was previously reported [193]. Arsenic-induced T2DM is suggested to be mediated by inflammation, oxidative stress, and apoptosis, playing a significant role in the pathogenesis of obesity. Arsenic inhibits adipogenesis and enhances lipolysis, leading to obesity. Other reports have suggested that arsenic may induce lipodystrophy [193]. Another evidence suggests that uremic toxin-treated 3T3-L1 cells and MSC-derived adipocytes exhibit impaired adipogenesis and apoptosis through activation of the Na/K-ATPase/ROS amplification cycle [194]. Other types of environmental pollutants include organotins, widely used antifouling biocides for ships and fishing nets, play a role as endocrine disruptors as they bind to PPARγ/RXRα, induce adipogenesis, and repress inflammatory genes in different mammalian cells [195].
\n
\n
\n
5. Conclusion
\n
The pathology of obesity-associated IR and T2DM involves ectopic fat deposition in response to elevated energy intake and poor fat storage. The latter is due to impaired adipogenesis as newly recruited preadipocytes become unable to differentiate into fully functional adipocytes. This review presents several factors that influence adipogenesis in pathological obesity including inflammatory mediators, oxidative stress, fatty acid signaling, and other environmental factors. Most proinflammatory cytokines such as IL-6, IL-1β, TNF-α, IL-8, and IFNγ as well as some anti-inflammatory mediators including β-aminoisobutyric acid, A20 enzyme, and EPO have been shown to impair adipogenesis, leading to adipocyte hypertrophy, ectopic fat accumulation, and increased risk of IR and T2DM. However, basal level of adipose tissue inflammation has been shown to be required for normal adipogenesis and functional adipose tissue homeostasis. Similarly, various mediators of oxidative stress were shown to impact adipogenesis positively such as lipid peroxidation product 4-HNE and negatively such as the marker of oxidative damage 8-OHdG. Targeting lipid peroxidation products was shown to reverse impairment of adipogenesis and sustain IS. However, complete depletion of oxidative stress could also lead to impairment of adipogenesis as basal oxidative stress was shown to be required for normal adipogenesis. Fatty acid signaling also plays a very important role in adipogenesis as various fatty acid species such as PUFAs, MUFAs, and MCFAs were shown to regulate preadipocyte differentiation at various degrees depending on their composition. Finally, various environmental factors were suggested to impact adipogenesis, mainly through triggering inflammation and oxidative stress, leading to impairment of adipogenesis and increased risk of IR.
\n
\n
\n
Competing interests
\n
The authors declare that they have no competing interests.
\n
\n
\n
Authors’ contributions
\n
All authors participated in reviewing the literature and preparing and approving the manuscript. MAE is responsible for the integrity of the work as a whole.
\n
\n
Abbreviations
COX-2
cyclooxygenase-2
15-d-PGJ2
15-deoxy-Δ12,14-prostaglandin J2
4-HNE
4-hydroxynonenal
8-OHdG
8-hydroxy-2-deoxyguanosine
AA
arachidonic acid
ATGL
adipose triglyceride lipase
BMP4
bone morphogenetic protein 4
C/EBP
CCAAT/enhancer-binding protein
CAD
Coronary artery disease
cdk5
cyclin-dependent kinase 5
DAGs
diacylglycerols
DBC1
deleted in bladder cancer protein 1
DHA
docosahexaenoic acid
DMEM
dexamethasone
DMSO
dimethyl sulfoxide
EETs
epoxyeicosatrienoic acids
EPA
eicosapentaenoic acid
EPO
nonerythropoietic derived peptide
ER
endoplasmic reticulum
FABP4
fatty acid-binding protein 4
GIP
glucose-dependent insulinotropic polypeptide
HSL
hormone-sensitive lipase
IFN-γ
interferon-γ
IKKβ
inhibitor of nuclear factor kappa-B kinase subunit β
IL-6
interleukin 6
IR
insulin resistance
IS
insulin sensitive
LA
linoleic acid
LPL
lipoprotein lipase
LXR
liver X receptors
MCFAs
medium chain fatty acids
MCP-1
monocyte chemoattractant protein-1
MCPIP
Mcp-1-induced protein
MDI
insulin in differentiation medium
MGL
monoglyceride lipase
MHO
metabolically healthy obese
miRNAs
microRNAs
MUFAs
monounsaturated fatty acids
NF-kappa-B
nuclear factor kappa-light-chain enhancer of activated B cells
\n',keywords:"adipogenesis, mediators, inflammation, oxidative stress, fatty acids",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68752.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68752.xml",downloadPdfUrl:"/chapter/pdf-download/68752",previewPdfUrl:"/chapter/pdf-preview/68752",totalDownloads:513,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 13th 2019",dateReviewed:"July 22nd 2019",datePrePublished:"August 28th 2019",datePublished:"November 6th 2019",dateFinished:null,readingETA:"0",abstract:"Obesity has become a global health issue due to its high prevalence and associated comorbidities including insulin resistance (IR) and type 2 diabetes mellitus (T2DM). Obesity is associated with the expansion of adipose tissues through hypertrophy of mature adipocytes and differentiation of local preadipocytes in a process known as adipogenesis to store excess triacylglycerols (TAGs). Impairment of adipogenesis leads to ectopic fat deposition in skeletal muscles, liver, and kidneys, triggering IR in these tissues and increased risk of T2DM. Many factors contribute to impaired adipogenesis including obesity-associated mild chronic inflammation, oxidative stress, and fatty acid signaling. This review summarizes recent literature covering mediators of impaired adipogenesis and underlying molecular pathways.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68752",risUrl:"/chapter/ris/68752",signatures:"Haya Al-Sulaiti, Alexander S. Dömling and Mohamed A. Elrayess",book:{id:"8797",title:"Adipose Tissue",subtitle:"An Update",fullTitle:"Adipose Tissue - An Update",slug:"adipose-tissue-an-update",publishedDate:"November 6th 2019",bookSignature:"Leszek Szablewski",coverURL:"https://cdn.intechopen.com/books/images_new/8797.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"304943",title:"Dr.",name:"Mohamed",middleName:null,surname:"Elrayess",fullName:"Mohamed Elrayess",slug:"mohamed-elrayess",email:"maelrayess@hotmail.com",position:null,institution:null},{id:"304960",title:"Ms.",name:"Haya",middleName:null,surname:"Al-Sulaiti",fullName:"Haya Al-Sulaiti",slug:"haya-al-sulaiti",email:"h.al-sulaiti@rug.nl",position:null,institution:{name:"University of Groningen",institutionURL:null,country:{name:"Netherlands"}}},{id:"304961",title:"Prof.",name:"Alex",middleName:null,surname:"Domling",fullName:"Alex Domling",slug:"alex-domling",email:"a.s.s.domling@rug.nl",position:null,institution:{name:"University of Groningen",institutionURL:null,country:{name:"Netherlands"}}}],sections:[{id:"sec_1",title:"1. Obesity-associated metabolic disease",level:"1"},{id:"sec_2",title:"2. The role of adipogenesis in obesity-associated IR and T2DM",level:"1"},{id:"sec_3",title:"3. Mediators of impaired adipogenesis in IR and T2DM",level:"1"},{id:"sec_3_2",title:"3.1 Inflammatory mediators",level:"2"},{id:"sec_3_3",title:"3.1.1 Impaired adipogenesis in response to proinflammatory signals",level:"3"},{id:"sec_4_3",title:"3.1.2 Impaired adipogenesis in response to anti-inflammatory signals",level:"3"},{id:"sec_6_2",title:"3.2 Oxidative stress",level:"2"},{id:"sec_7_2",title:"3.3 Fatty acid signaling",level:"2"},{id:"sec_9",title:"4. Environmental factors",level:"1"},{id:"sec_10",title:"5. Conclusion",level:"1"},{id:"sec_11",title:"Competing interests",level:"1"},{id:"sec_12",title:"Authors’ contributions",level:"1"},{id:"sec_15",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'\nMaire B et al. Nutritional transition and non-communicable diet-related chronic diseases in developing countries. Santé. 2002;12(1):45-55\n'},{id:"B2",body:'\nKodama S et al. Quantitative relationship between body weight gain in adulthood and incident type 2 diabetes: A meta-analysis. Obesity Reviews. 2014;15(3):202-214\n'},{id:"B3",body:'\nBogers RP et al. Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels: A meta-analysis of 21 cohort studies including more than 300 000 persons. Archives of Internal Medicine. 2007;167(16):1720-1728\n'},{id:"B4",body:'\nTsuneto A et al. Fatty liver incidence and predictive variables. Hypertension Research. 2010;33(6):638-643\n'},{id:"B5",body:'\nEliassen AH et al. Adult weight change and risk of postmenopausal breast cancer. Journal of the American Medical Association. 2006;296(2):193-201\n'},{id:"B6",body:'\nMcGee DL, Diverse Populations C. Body mass index and mortality: A meta-analysis based on person-level data from twenty-six observational studies. Annals of Epidemiology. 2005;15(2):87-97\n'},{id:"B7",body:'\nAdams KF et al. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. The New England Journal of Medicine. 2006;355(8):763-778\n'},{id:"B8",body:'\nMakki K, Froguel P, Wolowczuk I. Adipose tissue in obesity-related inflammation and insulin resistance: Cells, cytokines, and chemokines. ISRN Inflammation. 2013;2013:139239\n'},{id:"B9",body:'\nJo J et al. Hypertrophy and/or hyperplasia: Dynamics of adipose tissue growth. PLoS Computational Biology. 2009;5(3):e1000324\n'},{id:"B10",body:'\nBjorntorp P. Effects of age, sex, and clinical conditions on adipose tissue cellularity in man. Metabolism. 1974;23(11):1091-1102\n'},{id:"B11",body:'\nSpalding KL et al. Dynamics of fat cell turnover in humans. Nature. 2008;453(7196):783-787\n'},{id:"B12",body:'\nRutkowski JM, Stern JH, Scherer PE. The cell biology of fat expansion. The Journal of Cell Biology. 2015;208(5):501-512\n'},{id:"B13",body:'\nMurdolo G et al. Oxidative stress and lipid peroxidation by-products at the crossroad between adipose organ dysregulation and obesity-linked insulin resistance. Biochimie. 2013;95(3):585-594\n'},{id:"B14",body:'\nElattar S, Satyanarayana A. Can brown fat win the battle against white fat? Journal of Cellular Physiology. 2015;230(10):2311-2317\n'},{id:"B15",body:'\nAhmadian M, Wang Y, Sul HS. Lipolysis in adipocytes. The International Journal of Biochemistry and Cell Biology. 2010;42(5):555-559\n'},{id:"B16",body:'\nSaponaro C et al. The subtle balance between lipolysis and lipogenesis: A critical point in metabolic homeostasis. Nutrients. 2015;7(11):9453-9474\n'},{id:"B17",body:'\nRosen ED, Spiegelman BM. Adipocytes as regulators of energy balance and glucose homeostasis. Nature. 2006;444(7121):847-853\n'},{id:"B18",body:'\nOkuno A et al. Troglitazone increases the number of small adipocytes without the change of white adipose tissue mass in obese Zucker rats. The Journal of Clinical Investigation. 1998;101(6):1354-1361\n'},{id:"B19",body:'\nTontonoz P, Hu E, Spiegelman BM. Stimulation of adipogenesis in fibroblasts by PPAR gamma 2, a lipid-activated transcription factor. Cell. 1994;79(7):1147-1156\n'},{id:"B20",body:'\nCinti S et al. Adipocyte death defines macrophage localization and function in adipose tissue of obese mice and humans. Journal of Lipid Research. 2005;46(11):2347-2355\n'},{id:"B21",body:'\nRadcke S, Dillon JF, Murray AL. A systematic review of the prevalence of mildly abnormal liver function tests and associated health outcomes. European Journal of Gastroenterology and Hepatology. 2015;27(1):1-7\n'},{id:"B22",body:'\nVigouroux C et al. Molecular mechanisms of human lipodystrophies: From adipocyte lipid droplet to oxidative stress and lipotoxicity. The International Journal of Biochemistry and Cell Biology. 2011;43(6):862-876\n'},{id:"B23",body:'\nVirtue S, Vidal-Puig A. Adipose tissue expandability, lipotoxicity and the metabolic syndrome—An allostatic perspective. Biochimica et Biophysica Acta. 2010;1801(3):338-349\n'},{id:"B24",body:'\nXue P et al. Adipose deficiency of Nrf2 in Ob/Ob mice results in severe metabolic syndrome. Diabetes. 2013;62(3):845-854\n'},{id:"B25",body:'\nHocking S et al. Adiposity and insulin resistance in humans: The role of the different tissue and cellular lipid depots. Endocrine Reviews. 2013;34(4):463-500\n'},{id:"B26",body:'\nKursawe R et al. A role of the inflammasome in the low storage capacity of the abdominal subcutaneous adipose tissue in obese adolescents. Diabetes. 2016;65(3):610-618\n'},{id:"B27",body:'\nSnel M et al. Ectopic fat and insulin resistance: Pathophysiology and effect of diet and lifestyle interventions. International Journal of Endocrinology. 2012;2012:983814\n'},{id:"B28",body:'\nGuilherme A et al. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nature Reviews. Molecular Cell Biology. 2008;9(5):367-377\n'},{id:"B29",body:'\nBogardus C et al. Relationship between degree of obesity and in vivo insulin action in man. The American Journal of Physiology. 1985;248(3 Pt 1):E286-E291\n'},{id:"B30",body:'\nSamocha-Bonet D et al. Insulin-sensitive obesity in humans—A ‘favorable fat’ phenotype? Trends in Endocrinology and Metabolism. 2012;23(3):116-124\n'},{id:"B31",body:'\nKarelis AD et al. The metabolically healthy but obese individual presents a favorable inflammation profile. The Journal of Clinical Endocrinology and Metabolism. 2005;90(7):4145-4150\n'},{id:"B32",body:'\nStefan N et al. Identification and characterization of metabolically benign obesity in humans. Archives of Internal Medicine. 2008;168(15):1609-1616\n'},{id:"B33",body:'\nStefan N et al. Metabolically healthy obesity: Epidemiology, mechanisms, and clinical implications. The Lancet Diabetes and Endocrinology. 2013;1(2):152-162\n'},{id:"B34",body:'\nJung CH, Lee WJ, Song KH. Metabolically healthy obesity: A friend or foe? The Korean Journal of Internal Medicine. 2017;32(4):611-621\n'},{id:"B35",body:'\nAcosta JR et al. Increased fat cell size: A major phenotype of subcutaneous white adipose tissue in non-obese individuals with type 2 diabetes. Diabetologia. 2016;59(3):560-570\n'},{id:"B36",body:'\nOuzzani M et al. Rayyan-a web and mobile app for systematic reviews. Systematic Reviews. 2016;5(1):210\n'},{id:"B37",body:'\nCoelho M, Oliveira T, Fernandes R. Biochemistry of adipose tissue: An endocrine organ. Archives of Medical Science. 2013;9(2):191-200\n'},{id:"B38",body:'\nYuan Y, Gao J, Ogawa R. Mechanobiology and mechanotherapy of adipose tissue-effect of mechanical force on fat tissue engineering. Plastic and Reconstructive Surgery. Global Open. 2015;3(12):e578\n'},{id:"B39",body:'\nHan S et al. Adipose-derived stromal vascular fraction cells: Update on clinical utility and efficacy. Critical Reviews in Eukaryotic Gene Expression. 2015;25(2):145-152\n'},{id:"B40",body:'\nGustafson B et al. Restricted adipogenesis in hypertrophic obesity: The role of WISP2, WNT, and BMP4. Diabetes. 2013;62(9):2997-3004\n'},{id:"B41",body:'\nHammarstedt A et al. WISP2 regulates preadipocyte commitment and PPAR gamma activation by BMP4. Proceedings of the National Academy of Sciences of the United States of America. 2013;110(7):2563-2568\n'},{id:"B42",body:'\nGupta RK et al. Transcriptional control of preadipocyte determination by Zfp423. Nature. 2010;464(7288):619-623\n'},{id:"B43",body:'\nBaraban E et al. Anti-inflammatory properties of bone morphogenetic protein 4 in human adipocytes. International Journal of Obesity (2005). 2016;40(2):319-327\n'},{id:"B44",body:'\nGustafson B et al. Insulin resistance and impaired adipogenesis. Trends in Endocrinology and Metabolism. 2015;26(4):193-200\n'},{id:"B45",body:'\nAlmuraikhy S et al. Interleukin-6 induces impairment in human subcutaneous adipogenesis in obesity-associated insulin resistance. Diabetologia. 2016;59(11):2406-2416\n'},{id:"B46",body:'\nPeng X et al. Thioredoxin reductase 1 suppresses adipocyte differentiation and insulin responsiveness. Scientific Reports. 2016;6:28080\n'},{id:"B47",body:'\nTang T et al. Uncoupling of inflammation and insulin resistance by NF-kappaB in transgenic mice through elevated energy expenditure. The Journal of Biological Chemistry. 2010;285(7):4637-4644\n'},{id:"B48",body:'\nHelsley RN et al. Targeting IκB kinase β in adipocyte lineage cells for treatment of obesity and metabolic dysfunctions. Stem Cells (Dayton, Ohio). 2016;34(7):1883-1895\n'},{id:"B49",body:'\nHilton C, Neville MJ, Karpe F. MicroRNAs in adipose tissue: Their role in adipogenesis and obesity. International Journal of Obesity (2005). 2013;37(3):325-332\n'},{id:"B50",body:'\nLee JH et al. TonEBP suppresses adipogenesis and insulin sensitivity by blocking epigenetic transition of PPARγ2. Scientific Reports. 2015;5:10937\n'},{id:"B51",body:'\nJung TW et al. Protectin DX attenuates LPS-induced inflammation and insulin resistance in adipocytes via AMPK-mediated suppression of the NF-κB pathway. American Journal of Physiology. Endocrinology and Metabolism. 2018;315(4):E543-E551\n'},{id:"B52",body:'\nElrayess MA et al. 4-hydroxynonenal causes impairment of human subcutaneous adipogenesis and induction of adipocyte insulin resistance. Free Radical Biology and Medicine. 2017;104:129-137\n'},{id:"B53",body:'\nJaganjac M et al. Combined metformin and insulin treatment reverses metabolically impaired omental adipogenesis and accumulation of 4-hydroxynonenal in obese diabetic patients. Redox Biology. 2017;12:483-490\n'},{id:"B54",body:'\nHelaleh M et al. Association of polybrominated diphenyl ethers in two fat compartments with increased risk of insulin resistance in obese individuals. Chemosphere. 2018;209:268-276\n'},{id:"B55",body:'\nAl-Sulaiti H et al. Triglyceride profiling in adipose tissues from obese insulin sensitive, insulin resistant and type 2 diabetes mellitus individuals. Journal of Translational Medicine. 2018;16(1):175\n'},{id:"B56",body:'\nAcosta JR et al. Increased fat cell size: A major phenotype of subcutaneous white adipose tissue in non-obese individuals with type 2 diabetes. Diabetologia. 2016;59(3):560-570\n'},{id:"B57",body:'\nFlower L et al. Stimulation of interleukin-6 release by interleukin-1beta from isolated human adipocytes. Cytokine. 2003;21(1):32-37\n'},{id:"B58",body:'\nLongo M et al. Pathologic endoplasmic reticulum stress induced by glucotoxic insults inhibits adipocyte differentiation and induces an inflammatory phenotype. Biochimica et Biophysica Acta. 2016;1863(6 Pt A):1146-1156\n'},{id:"B59",body:'\nKang YE et al. The roles of adipokines, proinflammatory cytokines, and adipose tissue macrophages in obesity-associated insulin resistance in modest obesity and early metabolic dysfunction. PLoS One. 2016;11(4):e0154003\n'},{id:"B60",body:'\nKern PA et al. Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance. American Journal of Physiology. Endocrinology and Metabolism. 2001;280(5):E745-E751\n'},{id:"B61",body:'\nFasshauer M et al. Interleukin (IL)-6 mRNA expression is stimulated by insulin, isoproterenol, tumour necrosis factor alpha, growth hormone, and IL-6 in 3T3-L1 adipocytes. Hormone and Metabolic Research. 2003;35(3):147-152\n'},{id:"B62",body:'\nLiu LF et al. Adipose tissue macrophages impair preadipocyte differentiation in humans. PLoS One. 2017;12(2):e0170728\n'},{id:"B63",body:'\nMei M et al. Inflammatory stress exacerbates ectopic lipid deposition in C57BL/6J mice. Lipids in Health and Disease. 2011;10:110\n'},{id:"B64",body:'\nPradhan AD et al. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. Journal of the American Medical Association. 2001;286(3):327-334\n'},{id:"B65",body:'\nKopp HP et al. Impact of weight loss on inflammatory proteins and their association with the insulin resistance syndrome in morbidly obese patients. Arteriosclerosis, Thrombosis, and Vascular Biology. 2003;23(6):1042-1047\n'},{id:"B66",body:'\nRoytblat L et al. Raised interleukin-6 levels in obese patients. Obesity Research. 2000;8(9):673-675\n'},{id:"B67",body:'\nLaimer M et al. Markers of chronic inflammation and obesity: A prospective study on the reversibility of this association in middle-aged women undergoing weight loss by surgical intervention. International Journal of Obesity and Related Metabolic Disorders. 2002;26(5):659-662\n'},{id:"B68",body:'\nBastard JP et al. Elevated levels of interleukin 6 are reduced in serum and subcutaneous adipose tissue of obese women after weight loss. The Journal of Clinical Endocrinology and Metabolism. 2000;85(9):3338-3342\n'},{id:"B69",body:'\nYudkin JS et al. Inflammation, obesity, stress and coronary heart disease: Is interleukin-6 the link? Atherosclerosis. 2000;148(2):209-214\n'},{id:"B70",body:'\nPepys MB, Hirschfield GM. C-reactive protein: A critical update. The Journal of Clinical Investigation. 2003;111(12):1805-1812\n'},{id:"B71",body:'\nRotter V, Nagaev I, Smith U. Interleukin-6 (IL-6) induces insulin resistance in 3T3-L1 adipocytes and is, like IL-8 and tumor necrosis factor-alpha, overexpressed in human fat cells from insulin-resistant subjects. The Journal of Biological Chemistry. 2003;278(46):45777-45784\n'},{id:"B72",body:'\nFasshauer M et al. Adiponectin gene expression and secretion is inhibited by interleukin-6 in 3T3-L1 adipocytes. Biochemical and Biophysical Research Communications. 2003;301(4):1045-1050\n'},{id:"B73",body:'\nSenn JJ et al. Interleukin-6 induces cellular insulin resistance in hepatocytes. Diabetes. 2002;51(12):3391-3399\n'},{id:"B74",body:'\nGustafson B, Smith U. Cytokines promote Wnt signaling and inflammation and impair the normal differentiation and lipid accumulation in 3T3-L1 preadipocytes. The Journal of Biological Chemistry. 2006;281(14):9507-9516\n'},{id:"B75",body:'\nBing C. Is interleukin-1β a culprit in macrophage-adipocyte crosstalk in obesity? Adipocytes. 2015;4(2):149-152\n'},{id:"B76",body:'\nLabrecque J et al. Interleukin-1β and prostaglandin-synthesizing enzymes as modulators of human omental and subcutaneous adipose tissue function. Prostaglandins, Leukotrienes, and Essential Fatty Acids. 2019;141:9-16\n'},{id:"B77",body:'\nBruun JM et al. Monocyte chemoattractant protein-1 release is higher in visceral than subcutaneous human adipose tissue (AT): Implication of macrophages resident in the AT. The Journal of Clinical Endocrinology and Metabolism. 2005;90(4):2282-2289\n'},{id:"B78",body:'\nYounce C, Kolattukudy P. MCP-1 induced protein promotes adipogenesis via oxidative stress, endoplasmic reticulum stress and autophagy. Cellular Physiology and Biochemistry : International Journal of Experimental Cellular Physiology, Biochemistry, and Pharmacology. 2012;30(2):307-320\n'},{id:"B79",body:'\nO’Rourke RW et al. Systemic inflammation and insulin sensitivity in obese IFN-γ knockout mice. Metabolism: Clinical and Experimental. 2012;61(8):1152-1161\n'},{id:"B80",body:'\nHarkins JM et al. Expression of interleukin-6 is greater in preadipocytes than in adipocytes of 3T3-L1 cells and C57BL/6J and Ob/Ob mice. The Journal of Nutrition. 2004;134(10):2673-2677\n'},{id:"B81",body:'\nJung TW et al. β-Aminoisobutyric acid attenuates LPS-induced inflammation and insulin resistance in adipocytes through AMPK-mediated pathway. Journal of Biomedical Science. 2018;25(1):27\n'},{id:"B82",body:'\nDang R-J et al. A20 plays a critical role in the immunoregulatory function of mesenchymal stem cells. Journal of Cellular and Molecular Medicine. 2016;20(8):1550-1560\n'},{id:"B83",body:'\nAi L et al. A20 reduces lipid storage and inflammation in hypertrophic adipocytes via p38 and Akt signaling. Molecular and Cellular Biochemistry. 2016;420(1):73-83\n'},{id:"B84",body:'\nLiu Y et al. Nonerythropoietic erythropoietin-derived peptide suppresses adipogenesis, inflammation, obesity and insulin resistance. Scientific Reports. 2015;5:15134\n'},{id:"B85",body:'\nAhlqvist E et al. Link between GIP and osteopontin in adipose tissue and insulin resistance. Diabetes. 2013;62(6):2088-2094\n'},{id:"B86",body:'\nLi X et al. Bio-informatics analysis of a gene co-expression module in adipose tissue containing the diet-responsive gene Nnat. BMC Systems Biology. 2010;4:175\n'},{id:"B87",body:'\nHiguchi M et al. Differentiation of human adipose-derived stem cells into fat involves reactive oxygen species and Forkhead box O1 mediated upregulation of antioxidant enzymes. Stem Cells and Development. 2013;22(6):878-888\n'},{id:"B88",body:'\nLiu R et al. Dynamic differences in oxidative stress and the regulation of metabolism with age in visceral versus subcutaneous adipose. Redox Biology. 2015;6:401-408\n'},{id:"B89",body:'\nOkuno Y et al. Oxidative stress inhibits healthy adipose expansion through suppression of SREBF1-mediated lipogenic pathway. Diabetes. 2018;67(6):1113-1127\n'},{id:"B90",body:'\nTchkonia T et al. Fat tissue, aging, and cellular senescence. Aging Cell. 2010;9(5):667-684\n'},{id:"B91",body:'\nFurukawa S et al. Increased oxidative stress in obesity and its impact on metabolic syndrome. The Journal of Clinical Investigation. 2004;114(12):1752-1761\n'},{id:"B92",body:'\nGueraud F et al. Chemistry and biochemistry of lipid peroxidation products. Free Radical Research. 2010;44(10):1098-1124\n'},{id:"B93",body:'\nSalans LB, Knittle JL, Hirsch J. The role of adipose cell size and adipose tissue insulin sensitivity in the carbohydrate intolerance of human obesity. The Journal of Clinical Investigation. 1968;47(1):153-165\n'},{id:"B94",body:'\nHigdon A et al. Cell signalling by reactive lipid species: New concepts and molecular mechanisms. The Biochemical Journal. 2012;442(3):453-464\n'},{id:"B95",body:'\nBauer G, Zarkovic N. Revealing mechanisms of selective, concentration-dependent potentials of 4-hydroxy-2-nonenal to induce apoptosis in cancer cells through inactivation of membrane-associated catalase. Free Radical Biology and Medicine. 2015;81:128-144\n'},{id:"B96",body:'\nChen ZH, Niki E. 4-hydroxynonenal (4-HNE) has been widely accepted as an inducer of oxidative stress. Is this the whole truth about it or can 4-HNE also exert protective effects? IUBMB Life. 2006;58(5-6):372-373\n'},{id:"B97",body:'\nHuh JY et al. 8-Hydroxy-2-deoxyguanosine ameliorates high-fat diet-induced insulin resistance and adipocyte dysfunction in mice. Biochemical and Biophysical Research Communications. 2017;491(4):890-896\n'},{id:"B98",body:'\nMurdolo G et al. Free radical-derived oxysterols: Novel adipokines modulating adipogenic differentiation of adipose precursor cells. The Journal of Clinical Endocrinology and Metabolism. 2016;101(12):4974-4983\n'},{id:"B99",body:'\nVanella L et al. Increased heme-oxygenase 1 expression in mesenchymal stem cell-derived adipocytes decreases differentiation and lipid accumulation via upregulation of the canonical Wnt signaling cascade. Stem Cell Research and Therapy. 2013;4(2):28\n'},{id:"B100",body:'\nLin C-H et al. Oxidative stress induces imbalance of adipogenic/osteoblastic lineage commitment in mesenchymal stem cells through decreasing SIRT1 functions. Journal of Cellular and Molecular Medicine. 2018;22(2):786-796\n'},{id:"B101",body:'\nDenu RA, Hematti P. Effects of oxidative stress on mesenchymal stem cell biology. Oxidative Medicine and Cellular Longevity. 2016;2016:2989076\n'},{id:"B102",body:'\nPuri N et al. Heme induced oxidative stress attenuates sirtuin1 and enhances adipogenesis in mesenchymal stem cells and mouse pre-adipocytes. Journal of Cellular Biochemistry. 2012;113(6):1926-1935\n'},{id:"B103",body:'\nMoreno-Navarrete JM et al. Deleted in breast cancer 1 plays a functional role in adipocyte differentiation. American Journal of Physiology. Endocrinology and Metabolism. 2015;308(7):E554-E561\n'},{id:"B104",body:'\nWu Y-T et al. Depletion of Sirt3 leads to the impairment of adipogenic differentiation and insulin resistance via interfering mitochondrial function of adipose-derived human mesenchymal stem cells. Free Radical Research. 2018;52(11):1398-1415\n'},{id:"B105",body:'\nChang Y-C et al. Deficiency of NPGPx, an oxidative stress sensor, leads to obesity in mice and human. EMBO Molecular Medicine. 2013;5(8):1165-1179\n'},{id:"B106",body:'\nCastro JP, Grune T, Speckmann B. The two faces of reactive oxygen species (ROS) in adipocyte function and dysfunction. Biological Chemistry. 2016;397(8):709-724\n'},{id:"B107",body:'\nAlcala M et al. Short-term vitamin E treatment impairs reactive oxygen species signaling required for adipose tissue expansion, resulting in fatty liver and insulin resistance in obese mice. PLoS One. 2017;12(10):e0186579\n'},{id:"B108",body:'\nPieralisi A et al. N-acetylcysteine inhibits lipid accumulation in mouse embryonic adipocytes. Redox Biology. 2016;9:39-44\n'},{id:"B109",body:'\nPeris E et al. Antioxidant treatment induces reductive stress associated with mitochondrial dysfunction in adipocytes. The Journal of Biological Chemistry. 2019;294(7):2340-2352\n'},{id:"B110",body:'\nFindeisen HM et al. Oxidative stress accumulates in adipose tissue during aging and inhibits adipogenesis. PLoS One. 2011;6(4):e18532\n'},{id:"B111",body:'\nZhang C, Klett EL, Coleman RA. Lipid signals and insulin resistance. Journal of Clinical Lipidology. 2013;8(6):659-667\n'},{id:"B112",body:'\nJiao P et al. FFA-induced adipocyte inflammation and insulin resistance: Involvement of ER stress and IKKbeta pathways. Obesity (Silver Spring). 2011;19(3):483-491\n'},{id:"B113",body:'\nFuruhashi M, Hotamisligil GS. Fatty acid-binding proteins: Role in metabolic diseases and potential as drug targets. Nature Reviews. Drug Discovery. 2008;7(6):489-503\n'},{id:"B114",body:'\nSummers SA. Ceramides in insulin resistance and lipotoxicity. Progress in Lipid Research. 2006;45(1):42-72\n'},{id:"B115",body:'\nBarbarroja N et al. Increased dihydroceramide/ceramide ratio mediated by defective expression of degs1 impairs adipocyte differentiation and function. Diabetes. 2015;64(4):1180-1192\n'},{id:"B116",body:'\nPapackova Z, Cahova M. Fatty acid signaling: The new function of intracellular lipases. International Journal of Molecular Sciences. 2015;16(2):3831-3855\n'},{id:"B117",body:'\nRoy D et al. Coordinated transcriptional control of adipocyte triglyceride lipase (Atgl) by transcription factors Sp1 and peroxisome proliferator-activated receptor γ (PPARγ) during adipocyte differentiation. The Journal of Biological Chemistry. 2017;292(36):14827-14835\n'},{id:"B118",body:'\nClarke SD. Polyunsaturated fatty acid regulation of gene transcription: A molecular mechanism to improve the metabolic syndrome. The Journal of Nutrition. 2001;131(4):1129-1132\n'},{id:"B119",body:'\nClarke SD. The multi-dimensional regulation of gene expression by fatty acids: Polyunsaturated fats as nutrient sensors. Current Opinion in Lipidology. 2004;15(1):13-18\n'},{id:"B120",body:'\nKersten S. Effects of fatty acids on gene expression: Role of peroxisome proliferator-activated receptor alpha, liver X receptor alpha and sterol regulatory element-binding protein-1c. The Proceedings of the Nutrition Society. 2002;61(3):371-374\n'},{id:"B121",body:'\nWahle KW, Rotondo D, Heys SD. Polyunsaturated fatty acids and gene expression in mammalian systems. The Proceedings of the Nutrition Society. 2003;62(2):349-360\n'},{id:"B122",body:'\nForman BM, Chen J, Evans RM. Hypolipidemic drugs, polyunsaturated fatty acids, and eicosanoids are ligands for peroxisome proliferator-activated receptors alpha and delta. Proceedings of the National Academy of Sciences of the United States of America. 1997;94(9):4312-4317\n'},{id:"B123",body:'\nJohnson TE et al. Structural requirements and cell-type specificity for ligand activation of peroxisome proliferator-activated receptors. The Journal of Steroid Biochemistry and Molecular Biology. 1997;63(1):1-8\n'},{id:"B124",body:'\nYu K et al. Differential activation of peroxisome proliferator-activated receptors by eicosanoids. Journal of Biological Chemistry. 1995;270(41):23975-23983\n'},{id:"B125",body:'\nKliewer SA et al. Fatty acids and eicosanoids regulate gene expression through direct interactions with peroxisome proliferator-activated receptors alpha and gamma. Proceedings of the National Academy of Sciences of the United States of America. 1997;94(9):4318-4323\n'},{id:"B126",body:'\nKeller H et al. Fatty acids and retinoids control lipid metabolism through activation of peroxisome proliferator-activated receptor-retinoid X receptor heterodimers. Proceedings of the National Academy of Sciences of the United States of America. 1993;90(6):2160-2164\n'},{id:"B127",body:'\nRoynette CE et al. n-3 polyunsaturated fatty acids and colon cancer prevention. Clinical Nutrition. 2004;23(2):139-151\n'},{id:"B128",body:'\nHirafuji M et al. Cardiovascular protective effects of n-3 polyunsaturated fatty acids with special emphasis on docosahexaenoic acid. Journal of Pharmacological Sciences. 2003;92(4):308-316\n'},{id:"B129",body:'\nAbeywardena MY, Head RJ. Long-chain n-3 polyunsaturated fatty acids and blood vessel function. Cardiovascular Research. 2001;52(3):361-371\n'},{id:"B130",body:'\nBucher HC et al. N-3 polyunsaturated fatty acids in coronary heart disease: A meta-analysis of randomized controlled trials. The American Journal of Medicine. 2002;112(4):298-304\n'},{id:"B131",body:'\nLarsson SC et al. Dietary long-chain n-3 fatty acids for the prevention of cancer: A review of potential mechanisms. The American Journal of Clinical Nutrition. 2004;79(6):935-945\n'},{id:"B132",body:'\nWorgall TS et al. Polyunsaturated fatty acids decrease expression of promoters with sterol regulatory elements by decreasing levels of mature sterol regulatory element-binding protein. The Journal of Biological Chemistry. 1998;273(40):25537-25540\n'},{id:"B133",body:'\nHannah VC et al. Unsaturated fatty acids down-regulate srebp isoforms 1a and 1c by two mechanisms in HEK-293 cells. Journal of Biological Chemistry. 2001;276(6):4365-4372\n'},{id:"B134",body:'\nMater MK et al. Sterol response element-binding protein 1c (SREBP1c) is involved in the polyunsaturated fatty acid suppression of hepatic S14 gene transcription. Journal of Biological Chemistry. 1999;274(46):32725-32732\n'},{id:"B135",body:'\nXu J et al. Sterol regulatory element binding protein-1 expression is suppressed by dietary polyunsaturated fatty acids. A mechanism for the coordinate suppression of lipogenic genes by polyunsaturated fats. Journal of Biological Chemistry. 1999;274(33):23577-23583\n'},{id:"B136",body:'\nOu J et al. Unsaturated fatty acids inhibit transcription of the sterol regulatory element-binding protein-1c (SREBP-1c) gene by antagonizing ligand-dependent activation of the LXR. Proceedings of the National Academy of Sciences. 2001;98(11):6027-6032\n'},{id:"B137",body:'\nYoshikawa T et al. Polyunsaturated fatty acids suppress sterol regulatory element-binding protein 1c promoter activity by inhibition of liver X receptor (LXR) binding to LXR response elements. The Journal of Biological Chemistry. 2002;277(3):1705-1711\n'},{id:"B138",body:'\nBarak Y et al. PPAR gamma is required for placental, cardiac, and adipose tissue development. Molecular Cell. 1999;4(4):585-595\n'},{id:"B139",body:'\nGöttlicher M et al. Structural and metabolic requirements for activators of the peroxisome proliferator-activated receptor. Biochemical Pharmacology. 1993;46(12):2177-2184\n'},{id:"B140",body:'\nNakamura MT et al. Mechanisms of regulation of gene expression by fatty acids. Lipids. 2004;39(11):1077-1083\n'},{id:"B141",body:'\nCho K-J et al. Alpha-lipoic acid inhibits adipocyte differentiation by regulating pro-adipogenic transcription factors via mitogen-activated protein kinase pathways. The Journal of Biological Chemistry. 2003;278(37):34823-34833\n'},{id:"B142",body:'\nGoto T et al. 10-oxo-12(Z)-octadecenoic acid, a linoleic acid metabolite produced by gut lactic acid bacteria, potently activates PPARγ and stimulates adipogenesis. Biochemical and Biophysical Research Communications. 2015;459(4):597-603\n'},{id:"B143",body:'\nWong SH et al. The adaptive effects of dietary fish and safflower oil on lipid and lipoprotein metabolism in perfused rat liver. Biochimica et Biophysica Acta. 1984;792(2):103-109\n'},{id:"B144",body:'\nRen B et al. Polyunsaturated fatty acid suppression of hepatic fatty acid synthase and S14 gene expression does not require peroxisome proliferator-activated receptor alpha. The Journal of Biological Chemistry. 1997;272(43):26827-26832\n'},{id:"B145",body:'\nRustan AC, Christiansen EN, Drevon CA. Serum lipids, hepatic glycerolipid metabolism and peroxisomal fatty acid oxidation in rats fed omega-3 and omega-6 fatty acids. The Biochemical Journal. 1992;283(Pt 2):333-339\n'},{id:"B146",body:'\nTakeuchi H et al. Comparative effects of dietary fat types on hepatic enzyme activities related to the synthesis and oxidation of fatty acid and to lipogenesis in rats. Bioscience, Biotechnology, and Biochemistry. 2001;65(8):1748-1754\n'},{id:"B147",body:'\nMadsen L, Petersen RK, Kristiansen K. Regulation of adipocyte differentiation and function by polyunsaturated fatty acids. Biochimica et Biophysica Acta. 2005;1740(2):266-286\n'},{id:"B148",body:'\nJove M et al. Human omental and subcutaneous adipose tissue exhibit specific lipidomic signatures. The FASEB Journal. 2014;28(3):1071-1081\n'},{id:"B149",body:'\nHodson L, Skeaff CM, Fielding BA. Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake. Progress in Lipid Research. 2008;47(5):348-380\n'},{id:"B150",body:'\nDenys A, Hichami A, Khan NA. Eicosapentaenoic acid and docosahexaenoic acid modulate MAP kinase enzyme activity in human T-cells. Molecular and Cellular Biochemistry. 2002;232(1-2):143-148\n'},{id:"B151",body:'\nFan X et al. Arachidonic acid and related methyl ester mediate protein kinase C activation in intact platelets through the arachidonate metabolism pathways. Biochemical and Biophysical Research Communications. 1990;169(3):933-940\n'},{id:"B152",body:'\nJiang YH et al. Dietary fat and fiber differentially alter intracellular second messengers during tumor development in rat colon. Carcinogenesis. 1996;17(6):1227-1233\n'},{id:"B153",body:'\nKawaguchi T et al. Mechanism for fatty acid “sparing” effect on glucose-induced transcription: Regulation of carbohydrate-responsive element-binding protein by AMP-activated protein kinase. The Journal of Biological Chemistry. 2002;277(6):3829-3835\n'},{id:"B154",body:'\nMurata M et al. Dual action of eicosapentaenoic acid in hepatoma cells: Up-regulation of metabolic action of insulin and inhibition of cell proliferation. The Journal of Biological Chemistry. 2001;276(33):31422-31428\n'},{id:"B155",body:'\nMadani S et al. Diacylglycerols containing omega 3 and omega 6 fatty acids bind to RasGRP and modulate MAP kinase activation. The Journal of Biological Chemistry. 2004;279(2):1176-1183\n'},{id:"B156",body:'\nNikolopoulou E et al. Arachidonic acid-dependent gene regulation during preadipocyte differentiation controls adipocyte potential. Journal of Lipid Research. 2014;55(12):2479-2490\n'},{id:"B157",body:'\nMoreno-Santos I et al. The antagonist effect of arachidonic acid on GLUT4 gene expression by nuclear receptor type II regulation. International Journal of Molecular Sciences. 2019;20(4):963\n'},{id:"B158",body:'\nCatalioto RM et al. Autocrine control of adipose cell differentiation by prostacyclin and PGF2 alpha. Biochimica et Biophysica Acta. 1991;1091(3):364-369\n'},{id:"B159",body:'\nGaillard D et al. Requirement and role of arachidonic acid in the differentiation of pre-adipose cells. The Biochemical Journal. 1989;257(2):389-397\n'},{id:"B160",body:'\nNegrel R, Gaillard D, Ailhaud G. Prostacyclin as a potent effector of adipose-cell differentiation. The Biochemical Journal. 1989;257(2):399-405\n'},{id:"B161",body:'\nForman BM et al. 15-Deoxy-delta 12, 14-prostaglandin J2 is a ligand for the adipocyte determination factor PPAR gamma. Cell. 1995;83(5):803-812\n'},{id:"B162",body:'\nKliewer SA et al. A prostaglandin J2 metabolite binds peroxisome proliferator-activated receptor gamma and promotes adipocyte differentiation. Cell. 1995;83(5):813-819\n'},{id:"B163",body:'\nSerrero G, Lepak NM. Prostaglandin F2alpha receptor (FP receptor) agonists are potent adipose differentiation inhibitors for primary culture of adipocyte precursors in defined medium. Biochemical and Biophysical Research Communications. 1997;233(1):200-202\n'},{id:"B164",body:'\nSerrero G, Lepak NM, Goodrich SP. Paracrine regulation of adipose differentiation by arachidonate metabolites: Prostaglandin F2 alpha inhibits early and late markers of differentiation in the adipogenic cell line 1246. Endocrinology. 1992;131(6):2545-2551\n'},{id:"B165",body:'\nSerrero G, Lepak NM, Goodrich SP. Prostaglandin F2 alpha inhibits the differentiation of adipocyte precursors in primary culture. Biochemical and Biophysical Research Communications. 1992;183(2):438-442\n'},{id:"B166",body:'\nCasimir DA, Miller CW, Ntambi JM. Preadipocyte differentiation blocked by prostaglandin stimulation of prostanoid FP2 receptor in murine 3T3-L1 cells. Differentiation. 1996;60(4):203-210\n'},{id:"B167",body:'\nKamon J et al. Prostaglandin F(2)alpha enhances glucose consumption through neither adipocyte differentiation nor GLUT1 expression in 3T3-L1 cells. Cellular Signalling. 2001;13(2):105-109\n'},{id:"B168",body:'\nMiller CW, Casimir DA, Ntambi JM. The mechanism of inhibition of 3T3-L1 preadipocyte differentiation by prostaglandin F2alpha. Endocrinology. 1996;137(12):5641-5650\n'},{id:"B169",body:'\nReginato MJ et al. Prostaglandins promote and block adipogenesis through opposing effects on peroxisome proliferator-activated receptor gamma. The Journal of Biological Chemistry. 1998;273(4):1855-1858\n'},{id:"B170",body:'\nVassaux G et al. Differential response of preadipocytes and adipocytes to prostacyclin and prostaglandin E2: Physiological implications. Endocrinology. 1992;131(5):2393-2398\n'},{id:"B171",body:'\nSugimoto Y et al. Microarray evaluation of EP4 receptor-mediated prostaglandin E2 suppression of 3T3-L1 adipocyte differentiation. Biochemical and Biophysical Research Communications. 2004;322(3):911-917\n'},{id:"B172",body:'\nTsuboi H et al. Prostanoid EP4 receptor is involved in suppression of 3T3-L1 adipocyte differentiation. Biochemical and Biophysical Research Communications. 2004;322(3):1066-1072\n'},{id:"B173",body:'\nLi R et al. CYP2J2 attenuates metabolic dysfunction in diabetic mice by reducing hepatic inflammation via the PPARγ. American Journal of Physiology. Endocrinology and Metabolism. 2015;308(4):E270-E282\n'},{id:"B174",body:'\nSuzuki M, Tamura T, Shimomura Y. Less body fat accumulation in rats fed a safflower oil diet than in rats fed a beef tallow diet. The Journal of Nutrition. 1990;120(11):1291-1296\n'},{id:"B175",body:'\nWang H, Storlien LH, Huang X-F. Effects of dietary fat types on body fatness, leptin, and ARC leptin receptor, NPY, and AgRP mRNA expression. American Journal of Physiology-Endocrinology and Metabolism. 2002;282(6):E1352-E1359\n'},{id:"B176",body:'\nMinami A et al. Effect of eicosapentaenoic acid ethyl ester v. oleic acid-rich safflower oil on insulin resistance in type 2 diabetic model rats with hypertriacylglycerolaemia. The British Journal of Nutrition. 2002;87(2):157-162\n'},{id:"B177",body:'\nCha SH et al. Chronic docosahexaenoic acid intake enhances expression of the gene for uncoupling protein 3 and affects pleiotropic mRNA levels in skeletal muscle of aged C57BL/6NJcl mice. The Journal of Nutrition. 2001;131(10):2636-2642\n'},{id:"B178",body:'\nTakahashi Y, Ide T. Dietary n-3 fatty acids affect mRNA level of brown adipose tissue uncoupling protein 1, and white adipose tissue leptin and glucose transporter 4 in the rat. The British Journal of Nutrition. 2000;84(2):175-184\n'},{id:"B179",body:'\nOkuno M et al. Perilla oil prevents the excessive growth of visceral adipose tissue in rats by down-regulating adipocyte differentiation. The Journal of Nutrition. 1997;127(9):1752-1757\n'},{id:"B180",body:'\nJang IS et al. Role of dietary fat type in the development of adiposity from dietary obesity-susceptible Sprague-Dawley rats. The British Journal of Nutrition. 2003;89(3):429-438\n'},{id:"B181",body:'\nNakatani T et al. A low fish oil inhibits SREBP-1 proteolytic cascade, while a high-fish-oil feeding decreases SREBP-1 mRNA in mice liver: Relationship to anti-obesity. Journal of Lipid Research. 2003;44(2):369-379\n'},{id:"B182",body:'\nUkropec J et al. The hypotriglyceridemic effect of dietary n-3 FA is associated with increased beta-oxidation and reduced leptin expression. Lipids. 2003;38(10):1023-1029\n'},{id:"B183",body:'\nPellizzon M et al. Effects of dietary fatty acids and exercise on body-weight regulation and metabolism in rats. Obesity Research. 2002;10(9):947-955\n'},{id:"B184",body:'\nLiberato MV et al. Medium chain fatty acids are selective peroxisome proliferator activated receptor (PPAR) γ activators and pan-PPAR partial agonists. PLoS One. 2012;7(5):e36297\n'},{id:"B185",body:'\nSenarath S et al. Comparison of the effects of long-chain monounsaturated fatty acid positional isomers on lipid metabolism in 3T3-L1 cells. Journal of Oleo Science. 2019\n'},{id:"B186",body:'\nZhao G et al. Anti-inflammatory effects of polyunsaturated fatty acids in THP-1 cells. Biochemical and Biophysical Research Communications. 2005;336(3):909-917\n'},{id:"B187",body:'\nAmri EZ, Ailhaud G, Grimaldi PA. Fatty acids as signal transducing molecules: Involvement in the differentiation of preadipose to adipose cells. Journal of Lipid Research. 1994;35(5):930-937\n'},{id:"B188",body:'\nDavies JD et al. Adipocytic differentiation and liver x receptor pathways regulate the accumulation of triacylglycerols in human vascular smooth muscle cells. The Journal of Biological Chemistry. 2005;280(5):3911-3919\n'},{id:"B189",body:'\nDing S, Mersmann HJ. Fatty acids modulate porcine adipocyte differentiation and transcripts for transcription factors and adipocyte-characteristic proteins*. The Journal of Nutritional Biochemistry. 2001;12(2):101-108\n'},{id:"B190",body:'\nMcNeel RL, Mersmann HJ. Effects of isomers of conjugated linoleic acid on porcine adipocyte growth and differentiation. The Journal of Nutritional Biochemistry. 2003;14(5):266-274\n'},{id:"B191",body:'\nWolins NE et al. S3-12, Adipophilin, and TIP47 package lipid in adipocytes. The Journal of Biological Chemistry. 2005;280(19):19146-19155\n'},{id:"B192",body:'\nMagliano DJ et al. Persistent organic pollutants and diabetes: A review of the epidemiological evidence. Diabetes and Metabolism. 2014;40(1):1-14\n'},{id:"B193",body:'\nFarkhondeh T, Samarghandian S, Azimi-Nezhad M. The role of arsenic in obesity and diabetes. Journal of Cellular Physiology. 2019 Aug;234(8):12516-12529\n'},{id:"B194",body:'\nBartlett DE et al. Uremic toxins activates Na/K-ATPase oxidant amplification loop causing phenotypic changes in adipocytes in In vitro models. International Journal of Molecular Sciences. 2018;19(9):2685\n'},{id:"B195",body:'\nMilton FA et al. Dibutyltin compounds effects on PPARγ/RXRα activity, adipogenesis, and inflammation in mammalians cells. Frontiers in Pharmacology. 2017;8:507\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Haya Al-Sulaiti",address:null,affiliation:'
Department of Drug Design, University of Groningen, Netherlands
'},{corresp:null,contributorFullName:"Alexander S. Dömling",address:null,affiliation:'
Department of Drug Design, University of Groningen, Netherlands
'},{corresp:"yes",contributorFullName:"Mohamed A. Elrayess",address:"maelrayess@hotmail.com",affiliation:'
Biomedical Research Center (BRC), Qatar University, Qatar
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Fungal biomass was quantified by estimating the ergosterol content of the mycelium, and by a simple material balance the corresponding residual substrate was obtained. Fungal growth and substrate consumption rates showed different behavior for these monocultures (μ = 0.03 and 0.11 h−1; rs = − 0.04 and − 0.0006 gsubstrate/h, respectively). In this case, xylanases production was directly linked to the growth, while laccases were produced during both growth and maintenance phases. Besides xylanases (42% of total Aspergillus enzyme), high titers of cellulases (15%), amylases (34%), and invertases (9%), as well as lignin and manganese peroxidases (10 and 24% of the total Trametes enzyme), were produced on the corresponding monocultures. When both fungi were used in a coculture mode, xylanases and laccases production decreased (around 85 and 70%), and the proportion of the hydrolases and oxidases changed. 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