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非靶向代谢组学方法英语Non-targeted Metabolomics Methods in EnglishIntroductionNon-targeted metabolomics is an innovative approach in the field of metabolomics that aims to identify and quantify as many metabolites as possible in a given biological sample without any prior knowledge or bias towards specific metabolites. This method provides comprehensive insights into the global biochemical changes occurring in a biological system, such as a cell, tissue, or organism. In recent years, non-targeted metabolomics has gained immense popularity due to its ability to unravel intricate metabolic pathways and discover novel biomarkers for various diseases.Sample Collection and PreparationThe first step in non-targeted metabolomics is the collection and preparation of the biological sample. The choice of sample depends on the research question and can range from blood, urine, tissues, or even fecal samples. It is crucial to handle the samples with extreme care to avoid any degradation or contamination of metabolites. Sample preparation involves various techniques such as extraction, filtration, and derivatization, to enhance the stability and visibility of metabolites during subsequent analysis.Mass Spectrometry-Based AnalysisMass spectrometry (MS) is the key analytical technique used in non-targeted metabolomics. It detects and quantifies metabolites based on their mass-to-charge ratio (m/z) and abundance. Liquid chromatography-massspectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) are commonly used platforms for metabolite analysis. LC-MS is suitable for hydrophilic compounds, while GC-MS is preferred for volatile and thermally stable metabolites.Data Acquisition and PreprocessingOnce the samples are analyzed using MS, the raw data obtained needs to be processed and converted into a format suitable for downstream analysis. This step involves data acquisition, which includes peak picking, alignment, and normalization. Peak picking identifies and quantifies metabolite peaks in the acquired spectra, while alignment corrects any potential retention time variations. Normalization ensures that all samples are comparably represented, eliminating any technical biases.Statistical Analysis and IdentificationStatistical analysis is a crucial step in non-targeted metabolomics, as it helps in identifying significant metabolites and detecting patterns within the dataset. Multivariate statistical techniques, such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), are commonly used to visualize and interpret the data. Additionally, metabolite identification is performed by matching the acquired mass spectra with metabolite databases, such as the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), using tools like MassBank, MetFrag, or Metlin.Metabolic Pathway AnalysisOne of the key strengths of non-targeted metabolomics is its ability to unravel complex metabolic pathways. Pathway analysis tools, such as MetaboAnalyst, MetaboMiner, and Ingenuity Pathway Analysis (IPA), are used to identify significantly altered pathways and discover potential biomarkers. These analyses provide crucial insights into the underlying biochemical mechanisms and aid in understanding the disease pathogenesis or physiological responses.Challenges and Future PerspectivesDespite its numerous advantages, non-targeted metabolomics faces several challenges. Metabolite identification remains a major bottleneck due to the limited coverage of metabolite databases and the lack of standardization in data reporting. Additionally, the high complexity and dynamic range of metabolomes make it difficult to detect low-abundance metabolites accurately. Nevertheless, advancements in analytical techniques, bioinformatics, and collaborative efforts are steadily overcoming these challenges and driving the field forward.In conclusion, non-targeted metabolomics plays a vital role in understanding the complex metabolic dynamics within biological systems. Through the use of advanced mass spectrometry techniques, data analysis tools, and metabolite identification strategies, this approach has the potential to uncover novel biomarkers and therapeutic targets for various diseases. With continued advancements, non-targeted metabolomics is poised to revolutionize personalized medicine and contribute significantly to the field of biomedical research.。

Answers for Exercises —Numerical methods using MatlabChapter 1P10 2. Solution (a) )(x g x = produces an equation 0862=+-x x . Solving it gives the roots 2=x and 4=x .Since 2)2(=g and 4)4(=g , thus, both 2=P and 4=P are fixed points of )(x g . (b) –(d) The iterative rule using )(x g is 22144n n n p p p ---=. The results for part (b)-(d) with starting value 9.10=p and 8.30=p are listed in Table 1.(e) Calculate values of x x g -='4)( at 2=x and 4=x .12)2(>='g , and 10)4(<='g .Since )(x g ' is continuous, there exists a number 0>δ such that1)(<'x g for all ]4,4[δ+δ-∈x .There also exists a number 0>λ such that1)(>'x g for all ]2,2[λ+λ-∈x .Therefore, 4=p is an attractive fixed point. The sequence generated by22144n n n p p p ---=with starting value 8.30=p converges to 4=p . 2=p is a repelling fixed point. The sequence generated by 22144n n n p p p ---=with starting value 9.10=p does not converge to 2=p .P11 4. Find the fixed point for )(x g : )(x g x = gives 2±=p . Find the derivative: 12)(+='x x g .Evaluate )2(-'g and )2(g ': 3)2(-=-'g , 5)2(='g .Both 2-=p and 2=p gives 1)(>'p g . There is no reason to find the solution(s)using the fixed-point iteration.P11 6. Proof ))(()()(010112p p g p g p g p p -ξ'=-=-)()()( 0101p p K p p g -<-ξ'≤P214. False position method: Assume that ],[n n b a contains the root. The equation of the secand line through ))(,(n n a f a and ))(,(n n b f b is )()()()(n nn n n n b x a b a f b f b f y ---=-. Itintersects x -axise at)()())((n n n n n n n a f b f a b b f b c ---= (Eq. 1.36, p18)1981.0)6.1()(,4907.0)4.2()(00-=-==-=f b f f a f ,8301.1)()())((0000000-=---=a f b f a b b f b c ;Since 0095.0)(0-=c f , then ]8301.1,4.2[],[11--=b a . Similarly, we have1.84093- 1=c , ]1.84093- ,4.2[],[22-=b a 1.84139- 2=c , ]1.84139- ,4.2[],[33-=b a -1.841403=c10. Bisection method: Assume that ],[n n b a contains the root. Then 2nn n b a c +=. (a) 1587.1)4(,4;1425.0)3(,300==-==f b f a , then 5.30=c .Since 03746.0)5.3()(0>==f c f , then ]5.3,3[],[11=b a .Similarly, we can obtain ,,,321c c c . The results are listed in Table 3.The values of tan(x) at midpoints are going to zero while the sequence converges(b) Since 0)3tan(<=, there exist a root in )3,1(..0-=, 055741425.1tan(>)1The results using Bisection method are listed in Table 4.Although the sequence converges, the values of tan (x) at midpoints are not going to zero.P36 2. 3)(2--=x x x f has two zeros 2131±=x . (3028.2,3028.121≈-≈x x ) The first derivative of 3)(2--=x x x f is 12)(-='x x f .The Newton-Raphson iterative function is 123)()()(2-+='-=x x x f x f x x g . The Newton-Raphson formula is 12321-+=+n nn p p p , ,2,1,0=n . The results are listed in Table 5 with starting value p 0=1.6 and p 0=0.0 respectively.Obviously, the sequence generated by the starting value p 0=0.0 does not converge.11. Use Newton-raphson method to solve 0)(3=-=A x x f .The derivative of )(x f is 23)(x x f ='.3232)()()(223x Ax x A x x f x f x x g +=+='-=.Newton-Raphoson formula is 32211--+=n n n p Ap p , ,2,1=n .Since 3A p = is a zero of A x x f -=3)( and 10332)(33<=⎥⎦⎤⎢⎣⎡-='=Ap x A p g ,The sequence generated by the recursive formula 32211--+=n n n p Ap p will converge to3A p = for any starting value ],[330δδ+-∈A A p , where 0>δ.·Answers for Exercises —Numerical methods using MatlabChapter 2P44 2. Solution The 4th equation yields 24=x .Substituting 24=x to the 3rd equation gives 53=x .Substituting both 24=x and 53=x to the 2nd equation produces 32-=x . 21=x is obtained by sustituting all 32-=x , 53=x and 24=x to the 1st equation. The value of the determinant of the coefficient matrix is 115573115=⨯⨯⨯=D .4. Proof (a) Calculating the product of the two given upper-triangular matrices gives⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡++++=⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=33333323232222223313231213112212121111113323221312113323221312110000b a b a b a b a b a b a b a b a b a b a b b b b b b a a a a a a B A . It is also an upper-triangular matrix.(b) Let N N ij a A ⨯=)( and N N ij b B ⨯=)( where 0=ij a and 0=ij b when j i >.Let N N ij c B A C ⨯==)(. According to the definition of product of the two matrices, we have ∑==Nk kjik ij b ac 1for all N j i ,,2,1, =.0=ij c when j i > because 0=ij a and 0=ij b when j i >.That means that the product of the two upper-triangular matrices is also upper triangular.5. Solution From the first equation we have 31=x .Substituting 31=x to the second equation gives 22=x .13=x is obtained from the third equation and 14-=x is attained from the last equation.The value of the determinant of the coefficient is 243)1(42)det(-=⨯-⨯⨯=A7. Proof The formula of the back substitution for an N N ⨯upper-triangular system is N NN a b x =and kkNk j jkj k k a x a b x ∑+=-=1 for 1,,2,1 --=N N k .The process requiresN N=+++111 divisions, 22)1()1(212NN N N N -=-=-+++ multiplications, and2)1(212NN N -=-+++ additions or subtractions.P53 1. Solution Using elementary transformations for the augmented matrix gives330012630464275101263046425232103514642],[3231213121⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡--−−→−⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡---−−−→−⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡--=++-+-r r r r r r B AThat means that ⎪⎩⎪⎨⎧=++=++-=-+523 1035 4642321321321x x x x x x x x x is equivalent to⎪⎩⎪⎨⎧==+-=-+33 1263 4642332321x x x x x x The set of solutions is .3,2,1123-===x x x11. Solution Using the algorithm of Gaussian Elimination gives12420010324050110700211242001032409013270021],[212⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡----−−−→−⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡--=+-r r B A ⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡------−−→−+1242001032005011070021324r r ⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡------−−→−+21000103200501107002143r r The set of solutions of the system is obtained by the back substitutions,3,2,2234==-=x x x and .11=x(Chasing method for solving tridiagonal linear systems)14. (a) (i) Solution Applying Gaussian elimination with partial pivoting to the augment matrix results in⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡---−−→−⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡---=↔1100320001.0101001.01003001.010030001.010*******],[31r r B A ⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡--−−→−⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡--−−→−↔+-+-00043.03333.43019933.996667.630001.0100319933.996667.63000043.03333.430001.01003 3231213231r r r r r r ⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡---−−−−→−+-6806.00625.680019933.996667.630001.01003326667.633333.43r rThe set of solutions is,101.0524,0100.0-623⨯==x x and .105.2400 -61⨯=x15. Solution The N N ⨯Hilbert matrix is defined byN N ij H H ⨯=)( where 11-+=j i H ij for N j i ≤≤,1.(a) The inverse of the 44⨯ Hilbert matrix is⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡--------=-280042001680140420064802700240168027001200120140240120161H The exact solution is T X )140,240,120,16(--=.(b) The solution is T X )0881.185,0628.310,6053.149,7308.18(--=.>>1 H is ill-conditioned. A miss is as good as a mile. (失之毫厘，谬以千里)P62 5 (a) Solving B LY = gives TY )2,12,6,8(-=. From Y UX = we have TX )2,1,1,3(-=. The product of A and X is TAX )4,10,4,8(--=.That means B AX =(b) Similarly to the part (a), we haveTY )1,12,6,28(=, TX )1,2,1,3(=, and B AX T==)4,23,13,28(.6. ⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡---=175.113011*********L , ⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡-----=5.70001040085304011UP72 7. (a) Jacobi Iterative formula is ()⎪⎪⎩⎪⎪⎨⎧-+=+-=++-=+++)()()1()()()1()()()1(226141358k k k k k k k k k y x z z x y z y x for ,2,1,0=kResults for ),,()()()(k k k k z y x P =’, ,3,2,1=k are listed in Table 2.1 with starting value )0,0,0(0=P .The numerical results show that Jacobi iteration does not converge.(b) Gauss-Seidel Iterative formula is()⎪⎪⎩⎪⎪⎨⎧-+=+-=++-=++++++)1()1()1()()1()1()()()1(226141358k k k k k k k k k y x z z x y z y x for ,2,1,0=kResults ),,()()()(k k k k z y x P =’, ,3,2,1=k are listed in Table 2.2 with starting value )0,0,0(0=P ’Reasons:Conside the eigenvalues of iterative matricesSplit the coefficient matrix ⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡----=612114151A into three matrices⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡-⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡---⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡-=--=000100150012004000600010001U L D A .The iterative matrix of Jacobi iteration is⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡--=⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡----⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡=+=-061311041500121041506100010001)(1U L D T JThe spectral raduis of J T is 16800.5)(>=ρJ T . )1176.0,4546405880(i . .-±=λ’ So Jacobi method doesnot converge.Similarly, the iterative matrix of Gauss-Seidel iteration is⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡--=-=-65503200150)(1U L D T G .The spectral radius of G T is 2532.19)(=ρG T >1. )0866.0,2532.19,0(-=λ’ So Gauss-Seidel method does not converge.8. (a) Jacobi Iterative formula is()⎪⎩⎪⎨⎧-+=-+=+-=+++6/225/)8(4/)13()()()1()()()1()()()1(k k k k k k k k k y x z z x y z y x for ,2,1,0=k ),,()()()(k k k k z y x P =’ for 10,,2,1 =k are listed in Table 2.3 with starting value )0,0,0(0=P .Jacobi iteration converges to the solution (3, 2, 1)’(b) Gauss-Seidel iterative formula is()⎪⎪⎩⎪⎪⎨⎧+---=+---=+-=++++++)1()1()1()()1()1()()()1(22614/)8(4/)13(k k k k k k k k k y x z z x y z y x for ,2,1,0=k ),,()()()(k k k k z y x P =’ for 10,,2,1 =k are listed in Table 2.4 with starting value )0,0,0(0=PGauss-Seidel iteration converges to the solution (3, 2, 1)’Answers for Exercises —Numerical methods using MatlabChapter 3P99 1. Solution (a) The nth order derivative of )sin()(x x f = is )2sin()()(π+=n x x f n .Therefore, !5!3)(535x x x x P +-=, !7!5!3)(7537x x x x x P -+-= and !9!7!5!3)(97539x x x x x x P +-+-=.(b) Estimating the remainder term gives71091075574.2!101!10)5sin()(-⨯≤≤π+=x c x E for 1≤x .(c) Substituting 4π=x to )2sin()()(π+=n x x f n gives ,22)4()4(,22)4()4()3(-=π=π''=π'=πf f f f and 22)4()4()5()4(-=π=πf f .By using Taylor polynomial we have!5)4(22!4)4(22!3)4(22!2)4(22)4(2222)(54325π-+π-+π--π--π-+=x x x x x x P P108 1. (a) Using th e Horner ’s method to find )4(P givesSo )4(P =1.18.(b) From part (a) we have 12.002.002.0)(2-+-=x x x Q . )4()4(Q P =' can be also obtained byusing Horner ’s method.So )4(P '=-0.36 Another method:Hence, P(4)=-0.36.(c) Find )4(I and )1(I firstly.Then=-=⎰)1()4()(41I I dx x P 4.3029.(d) Use Horner ’s method to evaluate P (5.5)Hence, P (5.5)=0.2575.(d) Let 012233)(a x a x a x a x P +++=. There are 4 coefficients needed to found.Substituting four known point ),(i i y x , i =1, 2, 3, 4, into )(x P gives four linear equations with unknowni a , i =1, 2, 3, 4.54.10123=+++a a a a 5.12480123=+++a a a a 42.139270123=+++a a a a 66.05251250123=+++a a a aThe coefficients can be found by solving this linear system: .66.1,2.0,1.0,02.00123=-==-=a a a aP120 1. The values of f (x ) at the given points are listed in Table 3.1:(a) Find the Lagrange coefficient polynomials and 010)(0,1x x x L -=---=.1101)(1,1+=++=x x x LThe interpolating polynomial is x x L f x L f x P =+-=)()0()()1()(1,10,11. (b) ),(21)11()1()(20,2x x x x x L -=----=,110)1)(1()(21,2x x x x L -=--+=),(212)1()(22,2x x x x x L +=+=x x L f x L f x L f x P =++-=)()1()()0()()1()(2,21,20,22. (c) ),2)(1(61)21)(11()2)(1()(0,3---=-------=x x x x x x x L),2)(1)(1(21)20)(10)(10()2)(1)(1()(1,3--+=--+--+=x x x x x x x L),2)(1(21)21(1)11()2()1()(2,3-+-=-+-+=x x x x x x x L),1)(1(61)12(2)12()1()1()(3,3-+=-+-+=x x x x x x x L33,32,31,30,33)()2()()1()()0()()1()(x x L f x L f x L f x L f x P =+++-=(d) ,2212)(0,1x x x L -=--=,1121)(0,1-=--=x x x L 67)()2()()1()(1,10,11-=+=x x L f x L f x P . (e) ),23(21)20)(10()2)(1()(20,2+-=----=x x x x x L ),2()21(1)2()(21,2x x x x x L --=--=),(21)12(2)1()(20,2x x x x x L -=--=.23)()2()()1()()0()(22,21,20,22x x x L f x L f x L f x P -=++=7. (a) Note that each Lagrange polynomial )(,2x L k is of degree at most 2 and )(x g is a combination of)(,2x L k . Hence )(x g is also a polynomial of degree at most 2.(b) For each k x , 2,1,0=k , the Lagrange coefficient polynomial 1)(,2=k k x L , and 0)(,2=k j x L for k j ≠, 2,1,0=j . Therefore, 01)()()()(2,21,20,2=-++=k k k k x L x L x L x g .(c) )(x g is a polynomial of degree 2≤n and has n ≥ 3 zeroes. According to the fundamental theorem of algebra, 0)(=x g for all x .9. Let )()()(x P x f x E N N -=. )(x E N is a polynomial of degree N ≤.)(x f is degree with )(x P N at N +1 points N x x x ,,,10 implies that )(x E N has N +1 zeroes. Therefore, 0)(=x E N for all x , that is, )()(x P x f N = for all x .P131 6. (a) Find the divided-difference table:(b) Find the Newton polynomials with order 1, 2, 3 and 4.)0.1(80.16.3)(1--=x x P , )0.2)(0.1(6.0)0.1(80.160.3)(2--+--=x x x x P ,)0.3)(0.2)(0.1(15.0)0.2)(0.1(6.0)0.1(80.16.3)(3------+--=x x x x x x x P , )0.4)(0.3)(0.2)(0.1(03.0 )0.3)(0.2)(0.1(15.0)0.2)(0.1(6.0)0.1(80.16.3)(4----+------+--=x x x x x x x x x x x P .(c)–(d) The results are listed in Table 3.2P143 6. x x x T 32)(323-=, ]1,1[-∈x .The derivative of )(3x T is 323)(223-⋅='x x T . 0)(3='x T yields 21±=x . Evaluating )(3x T at 21±=x and 1±=x gives 1)1(3-=-T , 1)21(3=-T , 1)21(3-=T and 1)1(3=T .Therefore, 1))(max(3=x T , 1))(min(3-=x T .10. When 2=N , the Chebyshev nodes are ,23)6/5cos(0-=π=x ,01=x and 23)6/cos(2=π=x .Calculating the Lagrange coefficient polynomials based on 210,,x x x can produce the following results:,323)232(23)23()(20,2x x x x x L +-=⨯-⨯--=,341)23(23)23()23()(21,2x x x x L -=-⨯-+=.32323232)23()(22,2x x x x x L +=⨯⨯+=The proof is finished.Answers for Exercises —Numerical methods using MatlabChapter 4P157 1(a). Solution The sums for obtaining Normal equations are listed in Table 4.1The normal equations are ,710=A 135=B . Then ,7.0=A 6.2=B .The least-squares line is 6.27.0+=x y .2449.0)((51)(215122=⎪⎪⎭⎫ ⎝⎛-=∑=k k k x f y f EP158 4. Proof Suppose the linear-squares line is B Ax y += where A and B satisfiesthe Normal equations ∑∑===+N k k Nk ky xAB N 11and ∑∑∑====+Nk k k N k k N k k y x x A x B 1121.y y N x A B N N B x N A B x A N k k Nk k N k k ==⎪⎪⎭⎫ ⎝⎛+=+⎪⎪⎭⎫ ⎝⎛=+∑∑∑===111111 meas thatthe point ),(y x lies on the linear-squares line B Ax y +=.5. First eliminating B on the Normal equations∑∑===+Nk k Nk k y x A B N 11and ∑∑∑====+Nk k k Nk k Nk k y x x A x B 1121gives⎪⎪⎭⎫ ⎝⎛-=∑∑∑===Nk k N k k N k k k y x y x N D A 1111 where 2112⎪⎪⎭⎫ ⎝⎛-=∑∑==N k k N k k x x N D . Substituting A into the first equation gets⎪⎪⎭⎫⎝⎛⎪⎪⎭⎫ ⎝⎛+-=∑∑∑∑∑=====Nk k Nk k N k k k N k k N k k y x N y x x y N D D B 12111111. Note that ∑∑∑∑∑∑∑∑========⎪⎪⎭⎫ ⎝⎛-=⎪⎪⎭⎫ ⎝⎛⎪⎪⎭⎫ ⎝⎛-=N k k N k k N k k N k k N k k N k k N k k Nk k y x N y x y x x N N y N D 12111212112111. Simplifying B gives⎪⎪⎭⎫ ⎝⎛-=∑∑∑∑====Nk k k N k k Nk k N k k y x x y x D B 111121.8(b). The sums needed in the Normal equations are listed in Table 4.26177.142==∑∑k kk x y x A )2(=M5606.063==∑∑kkkxy x B )3(=MHence, 26177.1x y = and 35606.0x y =.0.3594 )(51)(21512222=⎪⎪⎭⎫ ⎝⎛-=∑=k k k Ax y Ax E , 1.1649 )(51)(21512332=⎪⎪⎭⎫ ⎝⎛-=∑=k k k Bx y Bx E .26175.1x y =fits the given data better.P171 2(c). The sums for normal equations are listed in Table 4.3.Using the formula∑∑∑∑∑∑∑========++=++52545352515135125151512515k kk k k k k k k k kk k k ky x x A x B x C y x A xB Cproduces the system with unkowns A , B , and CSolving the obove system gives .6.0,1.0,5.2-=-==C B A The fitting curve is .6.01.05.22--=x x yP172 4. (a) Translate points in x-y plane into X-Y plane using y Y x X ln ,==. The results arelisted in Table 4.4.The Normal equationsgive the system .793410,110,22105=+-==+A C B A C ∑∑∑∑∑======+=+515125151515k kk k k k k k kk k Y X X A X B Y X A B .8648.0155,2196.455-=+=+A B A BThen -0.50844=A , 1.3524=B . Thus 866731.3524.e e C B ===.The fitting curve is xe .y 50844.086673-=, and 1190.0)((51)(215122=⎪⎪⎭⎫ ⎝⎛-=∑=k k k x f y f E .(b) Translate points in x-y plane into X-Y plane using yY x X 1,==. The results are listed in Table 4.5.The Normal equationsgive the system Then 2432.0=A , 30280.0=B .The fitting curve is 30280.02432.01+=x y and 5548.4)((51)(215122=⎪⎪⎭⎫ ⎝⎛-=∑=k k k x f y f E .(c) It is easy to see that the exponential function is better comparing with errors in part (a) and part (b)..1620.5155,7300.255=+=+A B A B ∑∑∑∑∑======+=+515125151515k kk k k k k k kk k Y X X A X B Y X A BP188 1. (a) Derivativing )(x S gives 232132)(x a x a a x S ++='. Substituting the conditions intothe derivative pruduces the system of equations . 012428420321 32132103213210⎪⎪⎩⎪⎪⎨⎧=++=+++=++=+++a a a a a a a a a a a a a a (b) Solving the linear system of equations in (a) gives 29,,12,63210-==-==a a a a . The cubic polynomial is 3229126)(x x x x S -+-=.Figure: Graph of the cubic polynomial4. Step 1 Find the quantities: 3,1210===h h h , 21/)20(/)(0010-=-=-=h y y d13/)03(/)(1121=-=-=h y y d , 6667.03/)31(/)(2232-=-=-=h y y d18)(6011=-=d d u , 10)(6122-=-=d d uStep 2 Use ⎪⎪⎩⎪⎪⎨⎧-'-=⎪⎭⎫ ⎝⎛++'--=+⎪⎭⎫⎝⎛+))((3232))((32232322211100121110d x S u m h h m h x S d u m h m h h to obtain the linear system⎩⎨⎧-=+=+0001.155.1032135.72121m m m m .The solutions are 5161.2,8065.321-==m m . Step 3 Compute 0m and 3m using clamaped boundary.4.90322))((310000-=-'-=m x S d h m , 2.92482))((322323=--'=md x S h m Step 4 Find the spline coefficients16)2(,210001,000,0-=+-===m m h d s y s , 1.45166,-2.45162013,002,0=-===h m m s m s ;-1.54856)2(,021111,110,1=+-===m m h d s y s , -0.35136,1.903321123,112,1=-===h m m s m s ;0.38716)2(,332221,220,2=+-===m m h d s y s , 0.30236,-1.258122233,222,2=-===h m m s m s ;Step 5 The cubic spline is320)3(4516.1)3(4516.2)3(2)()(+++-+-==x x x x S x S for 23-≤≤-x ,321)2(3513.0)2(903.1)2(5484.1)()(+-+++-==x x x x S x S for 12≤≤-x , and322)1(3023.0)1(2581.1)1(3871.03)()(-+---+==x x x x S x S for 41≤≤x .5. Calculate the quantities: 3,1210===h h h , 20-=d ,11=d , 6667.02-=d ,181=u , 102-=u . ( Same values as Ex. 4)Substituting }{j h , }{j d and }{j u into ()()⎩⎨⎧=++=++22211112111022u m h h m h u m h m h h gives ⎩⎨⎧-=+=+1012318382121m m m mSolve the linear equation to obtain .5402.1,8276.221-==m m In addition, .030==m m Use formula (4. 65) to find the spline coefficients:4713.26)2(,210001,000,0-=+-===m m h d s y s , 4713.06,020013,002,0=-===h m m s m s ;-1.05756)2(,021111,110,1=+-===m m h d s y s , -0.24276,4138.121123,112,1=-===h m m s m s ;8735.06)2(,332221,220,2=+-===m m h d s y s , 0856.06,7701.0-22233,222,2=-===h m m s m s .Therefore, 30)3(4713.0)3(4713.22)(+++-=x x x S , for 23-≤≤-x ;321)2(2427.0)2(4138.1)2(0575.1)(+-+++-=x x x x S , for 12≤≤-x322)1(0856.0)1(7701.0)1(8735.03)(-+---+=x x x x S for 41≤≤x .Answers for Exercises —Numerical methods using MatlabChapter 5P209 1(b). Solution LetThe result of using the trapezoidal rule with h =1 isUsing Simpson’s rule with h=1/2, we haveFor Simpson’s 3/8 rule with h=1/3, we obtainThe result of using the Boole’s rule with h=1/4 is4. Proof Integrate )(1x P over ],[10x x .110102012101)(2)(2)(xx x x x x x x h f x x h f dx x P -+--=⎰=)(210f f h +. The Quadrature formula )(2)(1010f f hdx x f x x +≈⎰is called the trapezoidal rule.6. Solution The Simpson ’s rule is)4(3)(21010f f f hdx x f x x ++≈⎰. It will suffice to apply Simpson ’s rule over the interval [0, 2] with the test functions32,,,1)(x x x x f = and 4,x . For the first four functions, since)1141(31212+⨯+==⎰dx , )2140(31220+⨯+==⎰xdx , )4140(3138202+⨯+==⎰dx x , )8140(314203+⨯+==⎰dx x , the Simpson ’s rule is exact. But for 4)(x x f =,)16140(3153224+⨯+≠=⎰dx x . .Therefore, the degree of precision of Simpson ’s rule is n =3.T he Simpson’s rule and the Simpson ’s 3/8 rule have the same degree of precision n =3.).4cos(1)(x e x f x -+=..f f f f h dx x f 3797691))1()0((21)(2)(1010=+=+≈⎰.9583190))1()5.0(4)0((61)4(3)(21010. f f f f f f h dx x f =++=++≈⎰.9869270 ))1()3/2(3)3/1(3)0(( 8/1 )33(83)(321010.f f f f f f f f hdx x f =+++=+++≈⎰.008761 ))1(7)4/3(32)2/1(12)4/1(32)0(7( 90/1 )73212327(452)(432101.f f f f f f f f f f hdx x f =++++=++++≈⎰P220 3(a) Solution When 3)(x x f =for 10≤≤x , ⎰+π=123912dx x x area .The values of 2391)(x x x g +=at 11 sample points (M =10) are listed in the Table 5.1:(i) Using the composite Trapezoidal rule ∑-=++=110)()()((2),(M k k M x g h x g x g hh g T , the computation is)9156.11084.16098.03719.01563.00710.00280.00081.00010.0(101)1623.30(201)101,(++++++++++=g T=)2160.4(101)1623.3(201+=0.1576+0.4216=0.5792.(ii) Using the composite Simposon ’s rule ∑∑-=--=+++=11121120)(34)(32)()((3),(M k k M k k M x g h x g h x g x g h h g S , the computation is)9156.16098.01563.00280.00010.0(304)1084.13719.00710.00081.0(302 )1623.30(301)101,(++++++++++=g S=)7106.2(304)5054.1(302 )1623.3(301++=0.5672.7. (a) Because the formula)2()1()0()(2102g w g w g w dt t g ++=⎰is exact for the three functions 1)(=t g ,x t g =)(, and 2)(x t g =, we obtain three equations with unkowns 0w , 1w , and 2w :2210=++w w w , 2221=+w w ,38421=+w w . Solving this linear system gives 310=w , 341=w and 312=w .Thus, ())2()1(4)0(31)(20g g g dt t g ++=⎰(b) Let ht x x +=0 and denote ,01h x x +=.202h x x +=Then the change of variable ht x x +=0 translates ],[20x x into [0, 2] and converts the integral expresion dx x f )( into dt ht x hf )(0+. Hence,()())()(4)(3)2()1(4)0(3)()()(21022002x f x f x f h g g g hdt t g h dt ht x f h dx x f x x ++=++==+=⎰⎰⎰. The formula ())()(4)(3)(21020x f x f x f hdx x f x x ++=⎰ is known as the Simpson ’s rule over ],[20x x .8(a).9(a).P234 1(a) Let 212sin )(x xx f +=. The Romberg table with three rows for ⎰+3212sin dx x xis given as follows:Where04191.0)02794.0(23)106sin 0(23))3()0((23)0()0,0(-=-=+=+==f f T R , 04418.0)5.113sin (5.1204191.0)5.1(5.12)0()1()0,1(2=++-=+==f T T R ,3800.0)25.215.4sin 75.015.1sin (75.0204418.0))25.2()75.0((75.02)1()2()0,2(22=++++=++==f f T T R , 07288.03)04191.0(04418.043)0,0()0,1(4)1()1,1(=--⨯=-==R R S R ,].6/,6/[],[ and cos )(Let ππ-==b a x x f have we , and cos )( ,sin )( Since Mab h x x f x x f -=-=''-='.10513/123/ )(12),(922-⨯<⨯⎪⎭⎫ ⎝⎛ππ≤''--=M c f h a b h f E T .1039.2/)( and )4375( 9.4374 So,4-⨯≈-==>M a b h M M ].6/,6/[],[ andcos )(Let ππ-==b a x x f ,cos )( ,sin )( , cos )( ,sin )( Since )4(x x f x x f x x f x x f =='''-=''-='.105123/1803/ )(180),(92)4(4-⨯<⨯⎪⎭⎫ ⎝⎛ππ≤--=M c f h a b h f E S havewe ,2 and M a b h -=.1027.92/)( and )565( 8.564 So,4-⨯≈-==>M a b h M M4919.0304418.03800.043)0,1()0,2(4)2()1,2(=-⨯=-==R R S R ,5198.0307288.04919.01615)1,1()1,2(16)2()2,2(=-⨯=-==R R B R ,2. Proof If L J T J =∞→)(lim , thenL LL J T J T J S J J =-=--=∞→∞→343)1()(4lim)(lim andL LL J S J S J B J J =-=--=∞→∞→151615)1()(16lim )(lim .9. (a) Let 78)(x x f =. 0)()8(=x f implies 4=K . Thus 256)4,4(=R .(b) Let 1011)(x x f =.0)()11(=x f implies 5=K . Thus 2048)5,5(=R .10. (a) Do variable translation t x =. Thendt t dt t t dx x tx ⎰⎰⎰=⋅===121122.That means the two integrals dx x ⎰1anddt t ⎰122have the same numerical value.(b) Let 22)(t t f = and x x g =)(.Use dt t R ⎰≈122)1,1( means that the truncation error is )()4(n f k ξ approximately.Note that 0)()3(=t f . It means )1,1(212R dt t =⎰.But for x x g =)(, 0)()(=x g n is not true for all ]1,0[∈x and any integer 0>n .Thus the Romberg sequence is faster for dt t ⎰122 than fordx x ⎰1even though they have the samenumerical value.P242 1 (a) Applying the change of variable 22ab x a b t ++-=to dt t ⎰256 givesdx x dt t x t ⎰⎰-+=+⋅==115125)1(66.Thus the two integrals are dt t ⎰256 anddx x ⎰-+⋅115)1(6equivalent.(b)315315311525)1(6)1(6)()1(66=-=-+++=≈+⋅=⎰⎰x x x x f G dx x dt t =0.0809 +58.5857=58.6667If using )(3f G to approximate the integral, The result is535055353115205)1(695)1(698)1(695)()1(66==-=-+++++=≈+⋅=⎰⎰x x x x x x f G dx x dt t64105.5965956.0000 98 0.0035 95=⨯+⨯+⨯=6. Analysis: The fact that the degree of precision of N -point Gauss-Legendra integration is 2N -1 impliesthat the error term can be represented in the form )()()2(c kf f E N N =.(a) Since dt t dx x tx ⎰⎰-+=+==117127)1(88, and ()0)1()8(7=+t implies 82=K . Thus =256)(4=f G .(b),)1(111111101210dt t dx x tx ⎰⎰-+=+==and ()0)1()11(10=+t implies 122=K .Thusdx x⎰21011=2048)(6=f G .7. The n th Legendre polynomial is defined by The first five polynomials areThe roots of them are same as ones in Table 5.8.11. The conditions that the relation is exact for the functions means the three equations:326.0 6.0 0)6.0( )6.0(2 3132111321=+=+-=++w w w w w w w Sloving the system gives 98 ,95 231===w w w . ))6.0((95)0(98))6.0((95)(212111f f f dx x f ++-≈⎰- is called three-point Gauss-Legendre rule.Answers for Exercises —Numerical methods using MatlabChapter 6P249 1. (a) Proof Differentiate 22)(2+-+=-t t Ce t y t .22)(-+-='-t Ce t y tSubstitute )(t y and )(t y ' into the right-hand side of the equation y t y -='2.side left )(22)22(side right 222='=-+-=+-+-=-=--t y t Ce t t Ce t y t t t(b) Solution Let y t y t f -=2),(. Then 1),(-=y t f y for any ),(y t .So, the Lipschitz constant is 1=L .()[],2,11!21)(1)(20=-⋅==n x dx d n x P x P nnn n n ()()()3303581)(3521)(1321)()(1)(244332210+-=-=-===x x x P x x P x x P x x P x P 2,,1)(x x x f =12 . Integrate both side of )(t f y =' over [a , b ]: ⎰⎰='=-babadt t f dt y a y b y )()()(.Then,)()()(a y b y dt t f ba-=⎰, where )(t y is the solution of the I. V . P)(t f y =', for b t a ≤≤ with 0)(=a y . That means that the definite integral⎰badt t f )( can becomputed using the two values )(a y and )(b y of the solution )(t y of the given I. V . P.. 14. Solution Separate the two variables of the equation 211t y +=' into the form dt tdy 211+=. Integrate dt tdy 211+=and yeild the general solution C t y +=arctan . The initial-value condition 0)0(=y means that 0=C . The solution for the I. V . P. is t y arctan =.P257 3. (a)-(c) The formula using Euler ’s method to solve the I. V . P. ty y -=', 1)0(=y canbe represented in the form k k k y ht y )1(1-=+. When 2.0=h and 1.0=h , the results are listed in Table 6.1.(d) The F. G . E. does decrease half approximatelly as expacted when h is halved.6. When 02.0=a , 00004.0=b and 10=h , the Euler ’s formula for 2bP aP P -=' is in the form210004.02.1k k k P P P -=+. With 1.760=P , the missing entries can be filled in the table.。

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Dyn Games Appl(2011)1:74–114DOI10.1007/s13235-010-0005-0Some Recent Aspects of Differential Game TheoryR.Buckdahn·P.Cardaliaguet·M.QuincampoixPublished online:5October2010©Springer-Verlag2010Abstract This survey paper presents some new advances in theoretical aspects of dif-ferential game theory.We particular focus on three topics:differential games with state constraints;backward stochastic differential equations approach to stochastic differential games;differential games with incomplete information.We also address some recent devel-opment in nonzero-sum differential games(analysis of systems of Hamilton–Jacobi equa-tions by conservation laws methods;differential games with a large number of players,i.e., mean-field games)and long-time average of zero-sum differential games.Keywords Differential game·Viscosity solution·System of Hamilton–Jacobi equations·Mean-field games·State-constraints·Backward stochastic differential equations·Incomplete information1IntroductionThis survey paper presents some recent results in differential game theory.In order to keep the presentation at a reasonable size,we have chosen to describe in full details three topics with which we are particularly familiar,and to give a brief summary of some other research directions.Although this choice does not claim to represent all the recent literature on the R.Buckdahn·M.QuincampoixUniversitéde Brest,Laboratoire de Mathématiques,UMR6205,6Av.Le Gorgeu,BP809,29285Brest, FranceR.Buckdahne-mail:Rainer.Buckdahn@univ-brest.frM.Quincampoixe-mail:Marc.Quincampoix@univ-brest.frP.Cardaliaguet( )Ceremade,UniversitéParis-Dauphine,Place du Maréchal de Lattre de Tassigny,75775Paris Cedex16, Francee-mail:cardaliaguet@ceremade.dauphine.frmore theoretic aspects of differential game theory,we are pretty much confident that it cov-ers a large part of what has recently been written on the subject.It is clear however that the respective part dedicated to each topic is just proportional to our own interest in it,and not to its importance in the literature.The three main topics we have chosen to present in detail are:–Differential games with state constraints,–Backward stochastic differential equation approach to differential games,–Differential games with incomplete information.Before this,we also present more briefly two domains which have been the object of very active research in recent years:–nonzero-sum differential games,–long-time average of differential games.Thefirst section of this survey is dedicated to nonzero-sum differential games.Although zero-sum differential games have attracted a lot of attention in the80–90’s(in particular, thanks to the introduction of viscosity solutions for Hamilton–Jacobi equations),the ad-vances on nonzero-sum differential games have been scarcer,and mainly restricted to linear-quadratic games or stochastic differential games with a nondegenerate diffusion.The main reason for this is that there was very little understanding of the system of Hamilton–Jacobi equations naturally attached to these games.In the recent years the analysis of this sys-tem has been the object of several papers by Bressan and his co-authors.At the same time, nonzero-sum differential games with a very large number of players have been investigated in the terminology of mean-field games by Lasry and Lions.In the second section we briefly sum up some advances in the analysis of the large time behavior of zero-sum differential games.Such problems have been the aim of intense re-search activities in the framework of repeated game theory;it has however only been re-cently investigated for differential games.In the third part of this survey(thefirst one to be the object of a longer development) we investigate the problem of state constraints for differential games,and in particular,for pursuit-evasion games.Even if such class of games has been studied since Isaacs’pioneer-ing work[80],the existence of a value was not known up to recently for these games in a rather general framework.This is mostly due to the lack of regularity of the Hamiltonian and of the value function,which prevents the usual viscosity solution approach to work(Evans and Souganidis[63]):Indeed some controllability conditions on the phase space have to be added in order to prove the existence of the value(Bardi,Koike and Soravia[18]).Following Cardaliaguet,Quincampoix and Saint Pierre[50]and Bettiol,Cardaliaguet and Quincam-poix[26]we explain that,even without controllability conditions,the game has a value and that this value can be characterized as the smallest supersolution of some Hamilton–Jacobi equation with discontinuous Hamiltonian.Next we turn to zero-sum stochastic differential games.Since the pioneering work by Fleming and Souginidis[65]it has been known that such games have a value,at least in a framework of games of the type“nonanticipating strategies against controls”.Unfortunately this notion of strategies is not completely satisfactory,since it presupposes that the players have a full knowledge of their opponent’s control in all states of the world:It would be more natural to assume that the players use strategies which give an answer to the control effectively played by their opponent.On the other hand it seems also natural to consider nonlinear cost functionals and to allow the controls of the players to depend on events of the past which happened before the beginning of the game.The last two points have beeninvestigated in a series of papers by Buckdahn and Li[35,36,39],and an approach more direct than that in[65]has been developed.Thefirst point,together with the two others,will be the object of the fourth part of the survey.In the last part we study differential games with incomplete information.In such games, one of the parameters of the game is chosen at random according to some probability mea-sure and the result is told to one of the players and not to the other.Then the game is played as usual,players observing each other’s control.The main difference with the usual case is that at least one of the players does not know which payoff he is actually optimizing.All the difficulty of this game is to understand what kind of information the informed player has interest in to disclose in order to optimize his payoff,taking thus the risk that his opponent learns his missing information.Such games are the natural extension to differential games of the Aumann–Maschler theory for repeated games[11].Their analysis has been developed in a series of papers by Cardaliaguet[41,43–45]and Cardaliaguet and Rainer[51,52].Throughout these notes we assume the reader to be familiar with the basic results of dif-ferential game theory.Many references can be quoted on this subject:A general introduction for the formal relation between differential games and Hamilton–Jacobi equations(or sys-tem)can be found in the monograph Baçar and Olsder[13].We also refer the reader to the classical monographs by Isaacs[80],Friedman[67]and Krasovskii and Subbotin[83]for early presentations of differential game theory.The recent literature on differential games strongly relies on the notion of viscosity solution:Classical monographs on this subject are Bardi and Capuzzo Dolcetta[17],Barles[19],Fleming and Soner[64],Lions[93]and the survey paper by Crandall,Ishii and Lions[56].In particular[17]contains a good introduc-tion to the viscosity solution aspects of deterministic zero-sum differential games:the proof of the existence and the characterization of a value for a large class of differential games can be found there.Section6is mostly based on the notion of backward stochastic differential equation(BSDE):We refer to El Karoui and Mazliak[60],Ma and Yong[96]and Yong and Zhou[116]for a general presentation.The reader is in particular referred to the work by S.Peng on BSDE methods in stochastic control[101].Let usfinally note that,even if this survey tries to cover a large part of the recent literature on the more theoretical aspects of differential games,we have been obliged to omit some topics:linear-quadratic differential games are not covered by this survey despite their usefulness in applications;however,these games have been already the object of several survey ck of place also prevented us from describing advances in the domain of Dynkin games.2Nonzero-sum Differential GamesIn the recent years,the more striking advances in the analysis of nonzero-sum differential games have been directed in two directions:analysis by P.D.E.methods of Nash feedback equilibria for deterministic differential games;differential games with a very large number of small players(mean-field games).These topics appear as the natural extensions of older results:existence of Nash equilibria in memory strategies and of Nash equilibria in feedback strategies for stochastic differential games,which have also been revisited.2.1Nash Equilibria in Memory StrategiesSince the work of Kononenko[82](see also Kleimenov[81],Tolwinski,Haurie and Leit-mann[114],Gaitsgory and Nitzan[68],Coulomb and Gaitsgory[55]),it has been knownthat deterministic nonzero-sum differential games admit Nash equilibrium payoffs in mem-ory strategies:This result is actually the counterpart of the so-called Folk Theorem in re-peated game theory[100].Recall that a memory(or a nonanticipating)strategy for a player is a strategy where this player takes into account the past controls played by the other play-ers.In contrast a feedback strategy is a strategy which only takes into account the present position of the system.Following[82]Nash equilibrium payoffs in memory strategies are characterized as follows:A payoff is a Nash equilibrium payoff if and only if it is reach-able(i.e.,the players can obtain it by playing some control)and individually rational(the expected payoff for a player lies above its min-max level at any point of the resulting trajec-tory).This result has been recently generalized to stochastic differential games by Buckdahn, Cardaliaguet and Rainer[38](see also Rainer[105])and to games in which players can play random strategies by Souquière[111].2.2Nash Equilibria in Feedback FormAlthough the existence and characterization result of Nash equilibrium payoffs in mem-ory strategies is quite general,it has several major drawbacks.Firstly,there are,in general, infinitely many such Nash equilibria,but there exists—at least up to now—no completely satisfactory way to select one.Secondly,such equilibria are usually based on threatening strategies which are often non credible.Thirdly,the corresponding strategies are,in general, not“time-consistent”and in particular cannot be computed by any kind of“backward in-duction”.For this reason it is desirable tofind more robust notions of Nash equilibria.The best concept at hand is the notion of subgame perfect Nash equilibria.Since the works of Case[54]and Friedman[67],it is known that subgame perfect Nash equilibria are(at least heuristically)given by feedback strategies and that their corresponding payoffs should be the solution of a system of Hamilton–Jacobi equations.Up to now these ideas have been successfully applied to linear-quadratic differential games(Case[54],Starr and Ho[113], ...)and to stochastic differential games with non degenerate viscosity term:In thefirst case,one seeks solutions which are quadratic with respect to the state variable;this leads to the resolution of Riccati equations.In the latter case,the regularizing effect of the non-degenerate diffusion allows us to usefixed point arguments to get either Nash equilibrium payoffs or Nash equilibrium feedbacks.Several approaches have been developed:Borkar and Ghosh[27]consider infinite horizon problems and use the smoothness of the invari-ant measure associated to the S.D.E;Bensoussan and Frehse[21,22]and Mannucci[97] build“regular”Nash equilibrium payoffs satisfying a system of Hamilton–Jacobi equations thanks to elliptic or parabolic P.D.E techniques;Nash equilibrium feedbacks can also be built by backward stochastic differential equations methods like in Hamadène,Lepeltier and Peng[75],Hamadène[74],Lepeltier,Wu and Yu[92].2.3Ill-posedness of the System of HJ EquationsIn a series of articles,Bressan and his co-authors(Bressan and Chen[33,34],Bressan and Priuli[32],Bressan[30,31])have analyzed with the help of P.D.E methods the system of Hamilton–Jacobi equations arising in the construction of feedback Nash equilibria for deter-ministic nonzero-sum games.In state-space dimension1and for thefinite horizon problem, this system takes the form∂V i+H i(x,D V1,...,D V n)=0in R×(0,T),i=1,...,n,coupled with a terminal condition at time T(here n is the number of players and H i is the Hamiltonian of player i,V i(t,x)is the payoff obtained by player i for the initial condition (t,x)).Setting p i=(V i)x and deriving the above system with respect to x one obtains the system of conservation laws:∂t p i+H i(x,p1,...,p n)x=0in R×(0,T).This system turns out to be,in general,ill-posed.Typically,in the case of two players(n= 2),the system is ill-posed if the terminal payoff of the players have an opposite monotonicity. If,on the contrary,these payoffs have the same monotony and are close to some linear payoff (which is a kind of cooperative case),then the above system has a unique solution,and one can build Nash equilibria in feedback form from the solution of the P.D.E[33].Still in space dimension1,the case of infinite horizon seems more promising:The sys-tem of P.D.E then reduces to an ordinary differential equation.The existence of suitable solutions for this equation then leads to Nash equilibria.Such a construction is carried out in Bressan and Priuli[32],Bressan[30,31]through several classes of examples and by various methods.In a similar spirit,the papers Cardaliaguet and Plaskacz[47],Cardaliaguet[42]study a very simple class of nonzero-sum differential games in dimension1and with a terminal payoff:In this case it is possible to select a unique Nash equilibrium payoff in feedback form by just imposing that it is Pareto whenever there is a unique Pareto one.However,this equilibrium payoff turns out to be highly unstable with respect to the terminal data.Some other examples of nonlinear-quadratic differential games are also analyzed in Olsder[99] and in Ramasubramanian[106].2.4Mean-field GamesSince the system of P.D.Es arising in nonzero-sum differential games is,in general,ill-posed,it is natural to investigate situations where the problem simplifies.It turns out that this is the case for differential games with a very large number of identical players.This problem has been recently developed in a series of papers by Lasry and Lions[87–90,94] under the terminology of mean-field games(see also Huang,Caines and Malhame[76–79] for a related approach).The main achievement of Lasry and Lions is the identification of the limit when the number of players tends to infinity.The typical resulting model takes the form⎧⎪⎨⎪⎩(i)−∂t u−Δu+H(x,m,Du)=0in R d×(0,T),(ii)∂t m−Δm−divD p H(x,m,Du)m=0in R d×(0,T),(iii)m(0)=m0,u(x,T)=Gx,m(T).(1)In the above system,thefirst equation has to be understood backward in time while the second one is forward in time.Thefirst equation(a Hamilton–Jacobi one)is associated with an optimal control problem and its solution can be regarded as the value function for a typical small player(in particular the Hamiltonian H=H(x,m,p)is convex with respect to the last variable).As for the second equation,it describes the evolution of the density m(t)of the population.More precisely,let usfirst consider the behavior of a typical player.He controls through his control(αs)the stochastic differential equationdX t=αt dt+√2B t(where(B t)is a standard Brownian motion)and he aims at minimizing the quantityET12LX s,m(s),αsds+GX T,m(T),where L is the Fenchel conjugate of H with respect to the p variable.Note that in this cost the evolving measure m(s)enters as a parameter.The value function of our average player is then given by(1-(i)).His optimal control is—at least heuristically—given in feedback form byα∗(x,t)=−D p H(x,m,Du).Now,if all agents argue in this way,their repartition will move with a velocity which is due,on the one hand,to the diffusion,and,one the other hand,to the drift term−D p H(x,m,Du).This leads to the Kolmogorov equation(1-(ii)).The mean-field game theory developed so far has been focused on two main issues:firstly,investigate equations of the form(1)and give an interpretation(in economics,for instance)of such systems.Secondly,analyze differential games with afinite but large num-ber of players and interpret(1)as their limiting behavior as the number of players goes to infinity.Up to now thefirst issue is well understood and well documented.The original works by Lasry and Lions give a certain number of conditions under which(1)has a solution,discuss its uniqueness and its stability.Several papers also study the numerical approximation of this solution:see Achdou and Capuzzo Dolcetta[1],Achdou,Camilli and Capuzzo Dolcetta[2], Gomes,Mohr and Souza[71],Lachapelle,Salomon and Turinici[85].The mean-field games theory has been used in the analysis of wireless communication systems in Huang,Caines and Malhamé[76],or Yin,Mehta,Meyn and Shanbhag[115].It seems also particularly adapted to modeling problems in economics:see Guéant[72,73],Lachapelle[84],Lasry, Lions,Guéant[91],and the references therein.As for the second part of the program,the limiting behavior of differential games when the number of players tend to infinity has been understood for ergodic differential games[88].The general case remains mostly open.3Long-time Average of Differential GamesAnother way to reduce the complexity of differential games is to look at their long-time be-havior.Among the numerous applications of this topic let us quote homogenization,singular perturbations and dimension reduction of multiscale systems.In order to explain the basic ideas,let us consider a two-player stochastic zero-sum dif-ferential game with dynamics given bydX t,ζ;u,vs =bX t,ζ;u,vs,u s,v sds+σX t,ζ;u,v,u s,v sdB s,s∈[t,+∞),X t=ζ,where B is a d-dimensional standard Brownian motion on a given probability space (Ω,F,P),b:R N×U×V→R N andσ:R N×U×V→R N×d,U and V being some metric compact sets.We assume that thefirst player,playing with u,aims at minimizing a running payoff :R N×U×V→R(while the second players,playing with v,maximizes). Then it is known that,under some Isaacs’assumption,the game has a value V T which is the viscosity solution of a second order Hamilton–Jacobi equation of the form−∂t V T(t,x)+Hx,D V T(t,x),D2V T(t,x)=0in[0,T]×R N,V T(T,x)=0in R N.A natural question is the behavior of V T as T→+∞.Actually,since V T is typically of linear growth,the natural quantity to consider is the long-time average,i.e.,lim T→+∞V T/T.Interesting phenomena can be observed under some compactness assumption on the un-derlying state-space.Let us assume,for instance,that the maps b(·,u,v),σ(·,u,v)and (·,u,v)are periodic in all space variables:this actually means that the game takes place in the torus R N/Z N.In this framework,the long-time average is well understood in two cases:either the dif-fusion is strongly nondegenerate:∃ν>0,(σσ∗)(x,u,v)≥νI N∀x,u,v,(where the inequality is understood in the sense of quadratic matrices);orσ≡0and H= H(x,ξ)is coercive:lim|ξ|→+∞H(x,ξ)=+∞uniformly with respect to x.(2) In both cases the quantity V T(x,0)/T uniformly converges to the unique constant¯c forwhich the problem¯c+Hx,Dχ(x),D2χ(x)=0in R Nhas a continuous,periodic solutionχ.In particular,the limit is independent of the initial condition.Such kind of results has been proved by Lions,Papanicoulaou and Varadhan[95] forfirst order equations(i.e.,deterministic differential games).For second order equations, the result has been obtained by Alvarez and Bardi in[3],where the authors combine funda-mental contributions of Evans[61,62]and of Arisawa and Lions[7](see also Alvarez and Bardi[4,5],Bettiol[24],Ghosh and Rao[70]).For deterministic differential games(i.e.,σ≡0),the coercivity condition(2)is not very natural:Indeed,it means that one of the players is much more powerful than the other one. However,very little is known without such a condition.Existing results rely on a specific structure of the game:see for instance Bardi[16],Cardaliaguet[46].The difficulty comes from the fact that,in these cases,the limit may depend upon the initial condition(see also Arisawa and Lions[7],Quincampoix and Renault[104]for related issues in a control set-ting).The existence of a limit for large time differential games is certainly one of the main challenges in differential games theory.4Existence of a Value for Zero-sum Differential Games with State Constraints Differential games with state constraints have been considered since the early theory of differential games:we refer to[23,28,66,69,80]for the computation of the solution for several examples of pursuit.We present here recent trends for obtaining the existence of a value for a rather general class of differential games with constraints.This question had been unsolved during a rather long period due to problems we discuss now.The main conceptual difficulty for considering such zero-sum games lies in the fact that players have to achieve their own goal and to satisfy the state constraint.Indeed,it is not clear to decide which players has to be penalized if the state constraint is violated.For this reason,we only consider a specific class of decoupled games where each player controls independently a part of the dynamics.A second mathematical difficulty comes from the fact that players have to use admissible controls i.e.,controls ensuring the trajectory to fulfilthe state constraint.A byproduct of this problem is the fact that starting from two close initial points it is not obvious tofind two close constrained trajectories.This also affects the regularity of value functions associated with admissible controls:The value functions are,in general,not Lipschitz continuous anymore and,consequently,classical viscosity solutions methods for Hamilton–Jacobi equations may fail.4.1Statement of the ProblemWe consider a differential game where thefirst player playing with u,controls afirst systemy (t)=gy(t),u(t),u(t)∈U,y(t0)=y0∈K U,(3) while the second player,playing with v,controls a second systemz (t)=hz(t),v(t),v(t)∈V,z(t0)=z0∈K V.(4)For every time t,thefirst player has to ensure the state constraint y(t)∈K U while the second player has to respect the state constraint z(t)∈K V for any t∈[t0,T].We denote by x(t)= x[t0,x0;u(·),v(·)](t)=(y[t0,y0;u(·)](t),z[t0,z0;v(·)](t))the solution of the systems(3) and(4)associated with an initial data(t0,x0):=(t0,y0,z0)and with a couple of controls (u(·),v(·)).In the following lines we summarize all the assumptions concerning with the vectorfields of the dynamics:⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩(i)U and V are compact subsets of somefinitedimensional spaces(ii)f:R n×U×V→R n is continuous andLipschitz continuous(with Lipschitz constant M)with respect to x∈R n(iii)uf(x,u,v)andvf(x,u,v)are convex for any x(iv)K U={y∈R l,φU(y)≤0}withφU∈C2(R l;R),∇φU(y)=0ifφU(y)=0(v)K V={z∈R m,φV(z)≤0}withφV∈C2(R m;R),∇φV(z)=0ifφV(z)=0(vi)∀y∈∂K U,∃u∈U such that ∇φU(y),g(y,u) <0(vii)∀z∈∂K V,∃v∈V such that ∇φV(z),h(z,v) <0(5)We need to introduce the notion of admissible controls:∀y0∈K U,∀z0∈K V and∀t0∈[0,T]we defineU(t0,y0):=u(·):[t0,+∞)→U measurable|y[t0,y0;u(·)](t)∈K U∀t≥t0V(t0,z0):=v(·):[t0,+∞)→V measurable|z[t0,z0;v(·)](t)∈K V∀t≥t0.Under assumptions(5),the Viability Theorem(see[9,10])ensures that for all x0= (y0,z0)∈K U×K VU(t0,y0)=∅and V(t0,z0)=∅.Throughout the paper we omit t0in the notations U(t0,y0)and U(t0,y0)whenever t0=0.We now describe two quantitative differential games.Let us start with a game with an integral cost:Bolza Type Differential Game Given a running cost L:[0,T]×R N×U×V→R and afinal costΨ:R N→R,we define the payoff associated to an initial position(t0,x0)= (t0,y0,z0)and to a pair of controls(u,v)∈U(t0,y0)×V(t0,z0)byJt0,x0;u(·),v(·)=Tt0Lt,x(t),u(·),v(·)dt+Ψx(T),(6)where x(t)=x[t0,x0;u(·),v(·)](t)=(y[t0,y0;u(·)](t),z[t0,z0;v(·)](t))denotes the solu-tion of the systems(3)and(4).Thefirst player wants to maximize the functional J,while the second player’s goal is to minimize J.Definition1A mapα:V(t0,z0)→U(t0,y0)is a nonanticipating strategy(for thefirst player and for the point(t0,x0):=(t0,y0,z0)∈R+×K U×K V)if,for anyτ>0,for all controls v1(·)and v2(·)belonging to V(t0,z0),which coincide a.e.on[t0,t0+τ],α(v1(·)) andα(v2(·))coincide almost everywhere on[t0,t0+τ].Nonanticipating strategiesβfor the second player are symmetrically defined.For any point x0∈K U×K V and∀t0∈[0,T]we denote by A(t0,x0)and by B(t0,x0)the sets of the nonanticipating strategies for thefirst and the second player respectively.We are now ready to define the value functions of the game.The lower value V−is defined by:V−(t0,x0):=infβ∈B(t0,x0)supu(·)∈U(t0,y0)Jt0,x0;u(·),βu(·),(7)where J is defined by(6).On the other hand we define the upper value function as follows:V+(t0,x0):=limε→0+supα∈A(t0,x0)infv(·)∈V(t0,z0)Jεt0,x0;αv(·),v(·)(8)withJεt0,x0;u(·),v(·):=Tt0Lt,x(t),u(t),v(t)dt+Ψεx(T),where x(t)=x[t0,x0;u(·),v(·)](t)andΨεis the lower semicontinuous function defined byΨε(x):=infρ∈R|∃y∈R n with(y,ρ)−x,Ψ(x)=ε.The asymmetry between the definition of the value functions is due to the fact that one assumes that the terminal payoffΨis lower semicontinuous.WhenΨis continuous,one can check that V+can equivalently be defined in a more natural way asV+(t0,x0):=supα∈A(t0,x0)infv(·)∈V(t0,z0)Jt0,x0;αv(·),v(·).We now describe the second differential game which is a pursuit game with closed target C⊂K U×K V.Pursuit Type Differential Game The hitting time of C for a trajectory x(·):=(y(·),z(·)) is:θCx(·):=inft≥0|x(t)∈C.If x(t)/∈C for every t≥0,then we setθC(x(·)):=+∞.In the pursuit game,thefirst player wants to maximizeθC while the second player wants to minimize it.The value functions aredefined as follows:The lower optimal hitting-time function is the mapϑ−C :K U×K V→R+∪{+∞}defined,for any x0:=(y0,z0),byϑ−C (x0):=infβ(·)∈B(x0)supu(·)∈U(y0)θCxx0,u(·),βu(·).The upper optimal hitting-time function is the mapϑ+C :K U×K V→R+∪{+∞}de-fined,for any x0:=(y0,z0),byϑ+ C (x0):=limε→0+supα(·)∈A(x0)infv(·)∈V(z0)θC+εBxx0,αv(·),v(·).By convention,we setϑ−C (x)=ϑ+C(x)=0on C.Remarks–Note that here again the definition of the upper and lower value functions are not sym-metric:this is related to the fact that the target assumed to be closed,so that the game is intrinsically asymmetric.–The typical pursuit game is the case when the target coincides with the diagonal:C= {(y,z),|y=z}.We refer the reader to[6,29]for various types of pursuit games.The formalism of the present survey is adapted from[50].4.2Main ResultThe main difficulty for the analysis of state-constraint problems lies in the fact that two trajectories of a control system starting from two—close—different initial conditions could be estimated by classical arguments on the continuity of theflow of the differential equation. For constrained systems,it is easy to imagine cases where the constrained trajectories starting from two close initial conditions are rather far from each other.So,an important problem in order to get suitable estimates on constrained trajectories,is to obtain a kind of Filippov Theorem with ly a result which allows one to approach—in a suitable sense—a given trajectory of the dynamics by a constrained trajectory.Note that similar results exist in the literature.However,we need here to construct a constrained trajectory in a nonanticipating way[26](cf.also[25]),which is not the case in the previous constructions.Proposition1Assume that conditions(5)are satisfied.For any R>0there exist C0= C0(R)>0such that for any initial time t0∈[0,T],for any y0,y1∈K U with|y0|,|y1|≤R,。

5第18卷 第11期 2016 年 11 月辽宁中医药大学学报JOURNAL OF LIAONING UNIVERSITY OF TCMVol. 18 No. 11 Nov .，2016腰椎间盘突出症属中医“痹症”或“腰腿痛”范畴，是临床常见病、多发病，古代文献早有论述，《素问·刺腰痛论》中记载道：“肉里之脉令人腰痛，不可以咳，咳则经缩急。

”又云：“衡络之脉令人腰痛，不可以俯仰，仰则恐仆，得之举重伤腰。

”《灵枢·经脉》云：“脊痛，腰似折，牌不可以曲……是主筋所生病者，项、背、腰、尻、胴、脯、脚皆痛，小祉不用。

”其病因病机主要为肝肾亏虚，风寒湿邪痹阻脉络，以致血瘀气滞、经络运行不畅，呈“本虚标实”或“虚实夹杂”等特点，标实主要责于寒湿痹阻。

中医外治法在治疗本病的临床应用较为广泛，其作用机制主要包括药物和热疗两个方面，通过皮肤渗透性吸收，发挥消肿止痛、活血化瘀、祛风通络、温经散寒、补益肝肾、强筋壮骨等功效。

现代医学研究表明，其可有效改善微循环、促进炎症物质的吸收等。

本研究旨在通过对中医熨法在治疗腰椎间盘突出症应用的报道进行系统评价，为中医熨法的临床应用提供有力证据。

1 资料与方法1.1 资料来源通过计算机检索维普、CBM、CNKI、万方、中医熨法治疗腰椎间盘突出症Meta 分析杨鸫祥1，王禹2，姚啸生1，孙广江1，戚晓楠1，刘杨2（1.辽宁中医药大学附属医院，辽宁 沈阳 110032；2.辽宁中医药大学，辽宁 沈阳 110847）摘 要：目的：评价中医熨法治疗腰椎间盘突出症的临床疗效。

方法：通过计算机检索的方式，对维普、CBM、CNKI、万方、Pubmed 等数据库有关中医熨法治疗腰椎间盘突出症的相关RCT 文献进行质量评价，两人独立提取研究资料，应用RevMan 5.3软件进行资料分析，并应用漏斗图表示发表性偏倚。

结果：严格按照纳入标准、排除标准对所检索文献进行筛选，共纳入15篇文献，纳入病例2120例。

澳式手法结合龙氏正骨手法治疗椎动脉型颈椎病疗效观察【摘要】目的：探讨澳式手法结合龙氏正骨手法治疗椎动脉型颈椎病治疗效果。

方法：椎动脉型颈椎病90例按随机数字表法随机分为治疗组和对照组各45例，治疗组采用澳式手法结合龙氏正骨手法，对照组采用中频电疗加牵引，比较两组患者的疗效。

结果：治疗组患者总有效率为91.1%，对照组71.1%，两组间差异有显著性意义（p<0.05）。

结论：澳式手法结合龙氏正骨手法治疗椎动脉型颈椎病治疗效果优于中频加牵引。

【关键词】椎动脉型颈椎病；maitland手法；龙氏正骨；牵引；中频电疗【中图分类号】r681 【文献标识码】a 【文章编号】1004-7484（2013）06-109-02椎动脉型颈椎病（cervical spondylosis of vertebral artery type，csa）是由于多种因素致使椎动脉遭受刺激或压迫，以致血管迂曲、狭窄造成椎动脉供血不足为主要症状的综合症[1]。

是颈椎病中常见的一种类型，临床表现以头晕、头痛、颈项痛为主。

此病病程长、病情重，对患者的生活、工作影响较大。

2010年6月-2011年6月，我科采用采用澳式手法结合龙氏正骨手法加牵引加颈椎牵引治疗椎动脉型颈椎病，取得了较好的效果，现报道如下：1 资料与方法1.1 一般资料全部病例为2010年6月-2011年6月我院住院和门诊治疗的椎动脉型颈椎病患者共90例，诊断参照《中医病症诊断疗效标准》[2]。

排除颈椎病的其他分型；眼源性、耳源性眩晕；颈动脉ⅰ段（进入颈、横突孔以前的椎动脉段）受压所引起的基底动脉供血不全；合并有心、肝、肾、造血系统等原发性疾病及其他严重性疾病，精神病患者。

采用随机表法将90例患者随机分为对照组与观察组，每组45例。

治疗组，男20例，女25例；年龄35-68岁，平均42岁；病程10天～2年。

对照组，男18例，女27例；年龄37-65岁，平均44岁；病程8天～3年。

Virtual Books:Integrating Hypertext and Virtual RealityMaster’s Thesis of Jouke C. VerlindenGraduation Committee:prof. dr. H.G. Solir. C.A.P.G. van der Mastdr. Jay David Bolter (GVU Center, Georgia Tech)dr. James D. Foley (GVU Center, Georgia Tech)ir. B.R. SodoyerDelft University of Technology, Faculty of Technical Matematics and Informatics, HCI group.August 1993.Abstract“Think of computers as a medium, not as a tool” - Alan Kay in “The Art of User Inter-face Design”, 1989.Virtual Reality technology gives us new ways to represent information, based on spatial dis-play and multisensory interactivity. At present both commercial products and scientific re-search in VR create and explore relatively simple environments. These environments are often purely perceptual: that is, the user is placed a in world of color and shape that represents or re-sembles the “real” world. Objects (tables, doors, walls) in these environments have no deeper semantic significance.The Virtual Books project is an exploration of introducing semantics into three-dimensional space, by inclusions and manipulation of information, based on traditional writing technolo-gies (e.g. printed books) and the emerging electronic books (hypertexts, hypermedia etc.) Printed books often combine pictures and text. Hypermedia integrates texts with graphics, an-imation, video, and audio. Our goal is to extend these existing techniques of integration so that we can deploy text or other information in three dimensions and allow for effective interaction between the writer/reader/user and the text. We believe that this approach will provide solu-tions to prominent problems in the fields of hypertext and Virtual Reality. Four prototypes were developed to illustrate our ideas: The Georgia Tech Catalog, the Textured Book, the V oice Annotation System, and the World Processor. Silicon Graphics workstations with both immersive and non-immersive Virtual Reality technology were employed. To implement the prototypes, two software libraries were made (the bird and the SVE library); they facilitate easy creation and reuse of virtual environments. This project was done at the Graphics, Visual-ization, and Usability (GVU) Center, Georgia Tech, Atlanta, U.S.A. My advisor was dr. Jay David Bolter, professor in the School of Literature, Communications, and Culture.PrefaceAlmost 10 months of work are lying behind me. They seemed to have last a lifetime, that will come to an sudden end within a few days. Moreover, the project is the final step towards ob-taining my Master’s Degree in Computer Science -- a “project” that lasted 5 years! That means I can only say:The project has died, long live the project!Together with another Dutch exchange student, Anton Spaans, I have lived in Atlanta (Geor-gia, USA) for about eight months. We were both temporary members of the Graphics, Visual-ization, and Usability Center at the Georgia Institute of Technology. Daily (and nightly) we worked with advanced computer systems, faculty, and graduate students. These inspired me to do what I did and to pursue a further career in R&D. During my stay, I was also involved in various other activities, including the Apple Design Contest, the spatial audio research, and 3D algorithm visualization. And, of course, the band and the movie committee.It was a fascinating stay that taught me a lot. Not just about science or user interfaces: in those eight months I was a member of american society. A society in which the artificial has become natural -- a society that sells “I Can’t Believe it’s not Butter”™ and where the slogan “Just Add Water..” seems to be ubiquitous.AcknowledgmentsThe Dutch often say American friendships are superficial. Not in my experience: the people I met in Atlanta, Chapel Hill, Palo Alto, and so many other places turned out to be good friends. It is impossible to thank them all, even if I had ten months time to do so. I thank all people who made may stay as it was, and those who supported me during this unforgettable time. Es-pecially:Jay Bolter, my advisor at the GVU Center. His enthusiastic and open-minded approach made the project what it became. He treated me as a companion, not as a student. Yet he taught me so much...Jim Foley, for giving me the opportunity to come to his extraordinary lab and for putting me on the right track by introducing me to Jay.Joan Morton was an angel. She helped us whenever it was needed and did so many other things for the exchange students. Larry Hodges, who tolerated my work on “his” machines and introduced me to many other computer graphics researchers.Charles van der Mast, my advisor at the Delft University of Technology, who made this possi-ble. Without knowing him, I probably would not have ended up working abroad. Furthermore, he patiently awaited my results and provided me with suitable criticism.Daryl Lawton for bringing us to the fattest and fanciest dinner places. Mimi Recker, who ad-vised me during the usability tests. David Burgess and Beth Mynatt for distracting me from my actual project and involving me in their remarkable work.And of course all the GVU “Rats”, including: Jack Freeman, Jasjit Singh, Wayne Woolton, James O’Brien, Joe Wehrli, Heather Pritchett, Tom Meyer, Augusto op den Bosch, Anton Spaans, Mary-Ann Frogge, Jerome Salomon, Todd Griffith, Thomas Kuehme, Krishna Barat and all the others..The participants of the tests: Robert Hamilton (who I met again a month ago in Amsterdam), Gary Harrison, David Hamilton, and the eight students of Stuart Moultrop’s technical writing class.Dan Russell, who was my indespicable host at Xerox PARC. And of course the graduate stu-dents at UNC; especially Russell Taylor, who gave me the opportunity to have a look in the kitchen of the world famous Virtual Reality lab and introduced me to his friends Stephan, Rich, and John.The other members of the band: Tim, Ted and Mike. It was great to start a musical conversa-tion with you, guys!Dimitri, once a student and now a married engineer, who helped me tremendously during the last (and critical) days.My family and friends in holland, who didn’t forget me (even when I forgot them..) And fi-nally, I thank the one who supported me and had to deal with my stress during this long period that didn’t seem to end: Simone.Table of ContentsAbstract (1)Preface (2)Acknowledgments (3)1.Introduction (7)1.1Project8 1.2Environment9 1.2.1GVU Center 9 1.2.2Jay David Bolter 101.3Report112.Problem Analysis (12)2.1Background 1: Hypertext13 2.1.1Short (Hi)story of Media 13 2.1.2Hypertext 17 2.1.3Problems 21 2.2Background 2: Virtual Reality23 2.2.1Introduction 23 2.2.2Survey 24 2.2.3Problems with current Virtual Reality systems. 26 2.3Virtual Books: Integrating Hypertext and Virtual Reality28 2.3.1Proposal 28 2.3.2Related Research 282.3.3Requirements and Constraints 303.Functional Design (31)3.1Spatial Authoring concepts32 3.1.1Concepts of Hypertext environments 32 3.1.2Virtual Reality concepts 33 3.1.3Virtual Books Concepts 34 3.2Functionality36 3.3Presentation issues37 3.3.1Representation of hypertextual structure. 37 3.3.2Navigation and the representation of links. 37 3.3.3Representation of information. 38 3.3.4Virtual Reality issues. 38 3.4prototypes40 3.4.1Catalog 40 3.4.2Textured Book 42 3.4.3Voice Annotation System. 433.4.4World Processor 454.Technical Design (51)4.1Platform524.1.1Hardware 52 4.1.2Software Support 53 4.2Prototypes59 4.2.1The Catalog 59 4.2.2The Textured Book 59 4.2.3Speech Annotation System 594.2.4World Processor 595.Implementation and Evaluation (61)5.1The Catalog62 5.2Textured Book64 5.3Voice Annotation System67 5.4World Processor696.Conclusions and Future Research (71)6.1Conclusions and Results72 6.2 Future Research74Bibliography (77)Appendix A: PapersAppendix B: Prototypes ListAppendix C: Manuals of the Software LibrariesAppendix D: User’s Manual of the World ProcessorAppendix E: Voice Annotation testsAppendix F: A short report about my trip to Xerox and UNC1. IntroductionThe Master’s program of informatics at the Delft University of Technology requires a research project of six to nine months, with a thesis as result. Fortunately, through the contacts of Charles van der Mast (Delft University of Technology) with James Foley (Georgia Institute of Technology) I had the opportunity to work with prof. Jay David Bolter at the Graphics, Visual-ization and Usability Center in Atlanta, U.S.A., during a period of eight months. We explored our mutual interests in virtual reality, hypertext, writing and media. This Master’s thesis is considered to be the final, but certainly not the only result of our cooperation: 4 faculty reports and several videoclips were made as well.In the first months, october and november, we tried to formulate the Virtual Books Project as clear as possible. At the same time, I developed and implemented general GVU demonstra-tions for the Virtual Reality equipment. This equipment was recently purchased and just un-packed. In december, prof. Bolter went to Milan to give a keynote speech at ECHT’92, called “Virtual Reality and Hypertext”. A month later, I had the unexpected opportunity to visit three interesting research laboratories: the Virtual Reality lab at the university of North Carolina, Chapell Hill, the Xerox Palo Alto Research Center (PARC) and the world famous M.I.T. Me-dia Lab in Boston.By that time, I finished working on the lower-level software support (the bird- and the SVE-li-brary) and began to develop two complex Virtual Book-prototypes: the Voice Annotation sys-tem and the World Processor. Exploratory user tests were conducted during march and april. Both prototypes seemed interesting enough to start writing two separate papers on them, one has recently been accepted to the European Simulation Symposium (ESS ‘93), to be held on october 25-28, 1993 at the Delft University of Technology in the Netherlands.During the last month in Atlanta (may ‘93), I expanded the SVE- library and updated its docu-mentation in cooperation with Drew Kessler. One of the additions enables relatively easy tex-ture mapping, which was used in the last prototype called the Textured Book. After my return to Holland in june, I proceeded with writing this thesis and finishing the ESS ‘93 paper.1.1ProjectInitially, the project did not have distinct objectives. Jay Bolter and I introduced the term “Vir-tual Book”, which represented our interest in the exploration of Virtual Reality as a medium -a medium that could be used to communicate and structurize information in new ways. We fo-cused on some of the shortcomings of today’s upcoming electronic media: Hypertext and Vir-tual Reality.Hypertext and hypermedia are considered to be the new avenues in textual and reflective com-munication. These so-called “electronic books” have great perspectives. Their potential is in-creasing every day due to growing infrastructures and computing power. At the same time, these communication channels threaten the efficient and effective use of information. These disadvantages are often summarized as “information overload”. I will unravel this problem in several parts including 1) the getting lost in information space problem 2) the cognitive task switching problem. It will be argued that such problems are related to the limitations of the ap-plied metaphors and interaction techniques, that did not change significantly since the late six-ties.On the other hand, the sensory illusion of television, movies and computer games seem to be upgraded by the ultimate form of visual and engaging media:Virtual Reality. Virtual Reality is considered to be the most interactive medium of the future. The techniques involved generate three-dimensional environments that maximize the naturalness of the user interface - by three dimensional direct manipulation and perceptual immersion. Although the quality of the im-ages and devices has improved since its introduction in 1968, its theoretical potential did not change. The user is placed in a world of color and shape that represents or resembles the real world. Objects (tables, doors, walls) in these environments have no deeper semantic signifi-cance. This makes Virtual Reality a poor medium for symbolic communication.This project explores the integration of the traditional electronic books and virtual reality. Printed books often combine pictures and text. Hypermedia integrates texts with graphics, an-imation video and audio. Our goal is to extend these existing techniques of integration so that we can deploy information in three dimensions and allow for effective interaction between the writer/reader/user and the information. We think this synergetic approach will solve some of the most prominent problems in both fields, e.g. the “getting lost in hyperspace” problem. The project can be divided into three steps:1)Framing and testing ideas and testing in mockups or modest prototypes. These mockupsmay be on paper or in the computer. Of course, this phase includes a search of the rele-vant literature as well as attempts to get familiar with the available Virtual Reality hard-and software.2)Developing more elaborate prototypes that highlight specific aspects for creating andreading virtual books. This includes:a) developing a software layer that allows fast creation and modification of virtual book-prototypes.b) developing a prototype that illustrates how problems associated with current elec-tronic books can be solved or diminished.c) developing a prototype that illustrates how to add facilities for verbal communicationinto existing virtual reality applications.3)Based upon the second phase, I will: a) conduct some usability tests with groups of di-verse disciplines. b) identify strengths and weaknesses of the environments. c) draw con-clusions about the feasability and usefulness of such a virtual book and discussdirections for future research.1.2Environment1.2.1GVU CenterThe Graphics, Visualization and Usability Center is one of the most active and outstanding re-search institutes on Human-Computer Interfaces (HCI) in the world. The center houses a wide variety of faculty, who try to explore new frontiers of HCI. Members and graduate students or-igin from the College of Architecture, School for Civil Engeneering, College of Computing, School of Industrial and Systems Engineering, Office for Information Technology, School of Literature, Communication and Culture, School of Mathematics, Multimedia Technology Lab, and School of Psychology. James D. Foley, the well known computer graphics scientist, is the Center’s director. His careful management and open mindedness are the crucial driving forces to the quality and diversity of the Center’s research. His vision of the GVU is formulated as follows:“Making computers accesible and usable by every person represents the next and per-haps final great frontier int he computer/information revolution which has swept the world during the last half of this century.... The Center’s vision is of a world in which individuals are empowered in their everyday pursuits by the use of computers; of a world in which computers are used as easily and effectively as are automobiles, ste-reos, and telephones”(GVU 1992, p. 1)The Center’s research covers: realistic imagery, computer- supported collaborative work, al-gorithm visualization, medical imaging, image understanding, scientific data visualization, animation, user interface software, usability, virtual environments, image quality, user inter-faces for blind people,and expert systems in graphics and user interfaces. These projects are lead by several well know scientists, including John Stasko, Al Badre, Jessica Hodgins, Scott Hudson, Piyawadee “Noi” Sukaviriya and Christine Mitchell. Apart from the regular objective to publish and present high-quality scientific work, faculty and graduate students put a lot of effort into the creation of convincing demonstrations of their findings. MIT media lab’s “demo or die”-rule (brand 1987) seems to apply to the GVU as well: guided tours and demonstrations are frequently given to many visitors (including funders and scientists).Most graduate students do their research in the Graphics, Visualization, and Usability lab, which offers many high-end workstations and audio/video facilities. Furthermore, the lab also includes a conference room (with HCI library), a professional animation production area, and an isolated room for usability tests. A special “usability manager” takes care of the software, hardware and people of the lab (currently Suzan Liebeskind). However, the lab does not only provide technical support. The presence of so much “brains” in concentrated doses adds a so-cial dimension to the lab’s activities, a valuable -informal- communications channel that was certainly beneficial for my projects. Discussions, trouble-shooting sessions, and expert consul-tancies are held daily (and nightly!) every now and then. More formal meetings include the weekly brown bag meetings and the distinguished lecturer’s series (held each quarter). The completely renovated lab was officially opened 7 days after Anton Spaans and I arrived. This “convocation” day included several talks of celebrities in HCI research (e.g. Stuart Card and Andy van Dam) and, of course, many demonstrations of the GVU research in the lab.The GVU Center and its Lab can not easily be compared with the user interface research group at Delft. Apart from its interdisciplinary character and the wide variety of high-perfor-mance (graphics) workstations at the Center, there is another important difference between the the GVU lab and the HCI group at Delft: the GVU lab has a broad focus of research and does not fear to go beyond applied research. Companies like Siemens, SUN, DEC and Silicon Graphics fund projects that are focused on “technology Push”. This kind of research gets little attention in Delft, where research is primarily limited to applied problems, with its focus on validity and methods.As a part of the graphics research, professor Larry F. Hodges directs the virtual environments research group. At the beginning of the summer quarter in 1992 dr. Hodges ordered Virtual Reality equipment (see chapter 4.1 for a technical description). When I arrived in october ‘92 about 5 members were just unpacking the parts and trying to connect the systems together. From that moment on the research rapidly evolved to new, sophisticated uses and applications of virtual environments, including developing navigation interface techniques and metaphors, assessing display parameters for manipulation in virtual environments, making scientific visu-alization applications, and developing therapy for phobias (especially fear of heights). The group has weekly meetings to discuss strategies and the progress of the projects.1.2.2Jay David BolterMy advisor, Dr. Jay David Bolter is a professor in the School of Literature, Communication and Culture. He teaches technical writing, classical languages and the use of multimedia appli-cations. His research is directed toward communication, hypertext and new multimodal inter-faces for writing. He has written two books on the cultural and social significance of the computer: “Turing’s Man: Western Culture in the Computer Age” (1984) and “Writing Space: The Computer, Hypertext and the History of Writing” (1991). His books show that he is a gifted writer who has an understanding of both humanities and computer science. Apart from his writing, he co-designed and implemented a very interesting hypertext system called Sto-ryspace. In my experience, this application is one of the few hypertexts system that augments the writing task instead of disorienting the user with an overload of functionality. The usability is high, as can be noticed by the number of people that buy and employ it (it is comercially available for the Apple Macintosh).1.3ReportAlthough the project consisted of many small seemingly unrelated parts, one main thesis was pursued. This report will present the results of the Virtual Books project in a top-down fashion in 6 chapters. After describing the two backgrounds (hypertext and Virtual Reality), a more detailed discussion of Virtual Books is held in chapter 2 (Problem Analysis).Chapter 3 (Functional Design) elaborates on the the design of Virtual Books. It includes a dis-cussion of the general concepts, functions, and user interface issues. These evolve into the functional design of four prototypes:1) the Georgia Tech Catalog2) the Textured Book3) the V oice Annotator4) the World ProcessorTheir technical aspects are described in chapter 4 (Technical Design). This chapter also pre-sents a short overview of the computer hard/software that was used and the development of the Simple Virtual Environment(SVE) library.The implementation and evaluation of the prototypes appear in chapter 5 (Implementation and Evaluation). A short videoclip will accompany this report to illustrate the user interface and usability.The last chapter, chapter 6 (Conclusions and Future Research), includes the conclusions of this project and presents possibilities for future Virtual Books research.Several papers were written during this project, including: “The World Processor: an Interface for Textual Display and Manipulation in Virtual Reality”, “Virtual Annotation: Verbal Com-munication in Virtual Reality”, and “A First Experience with Spatial Audio in a Virtual Envi-ronment”. These articles can be found in Appendix A.Appendix B gives a short list of the prototypes; where they are located and how they are startedA description of the libraries that were developed during the project are presented in appendix C.Appendix D is the user’s manual that was used during the usability tests of the World Proces-sor.After the V oice Annotator was tested, the participants were interviewed. The questionaire and its answers are presented in the appendix E.Finally, appendix F is an informal report of my trip to Chapel Hill and Palo Alto.2. Problem AnalysisA more detailed analysis of the backgrounds is needed in order to design Virtual Books. The first sections of this chapter survey the two fields of interest: hypertext and Virtual Reality. Then I will propose to combine these two in the Virtual Books project. By integrating hyper-text facilities in virtual reality applications or adding virtual reality interfaces to hypertext sys-tems, some prominent problems of these fields can be solved.2.1Background 1: HypertextWe are living in the information age: our society produces, consumes and transforms data. The importance of information increases every day, and yet at the same time the amount of data seems to grow without bounds. Some researchers hold that one issue of a today’s newspaper contains more information than a medeival human would have encountered in his or her entire life.Computer science and informatics have introduced a paradigm to model the enormous com-plexity of generating and processing information. This paradigm considers all entities and ac-tivities that are involved during information processing as information systems - human beings as well as computers, faxes, phones etc.I will present a different view on information systems. It is a more literary view, focused on the history of writing and communication as it can be found in Jay Bolter’s book “Writing Space”. Instead of seeing computerized information systems as tools to process information, they are thought to be the decessors of earlier media (papyrus, codex, printed book). The ori-gins of information and its purposes (i.e. communication) have to be considered. In this vision, one of the most distinctive concepts computers contributed to media technology was hyper-text. This will be discussed in the second part of this section. However, current hypertext sys-tems do not always prove to be beneficial. The last section will identify the most prominent problems of these applications.2.1.1Short (Hi)story of MediaIn this section I want to introduce some aspects of media that seem to be relevant to this project. None of the ideas mentioned below are new, most of them originate from Jay Bolter’s book “Writing Space” (Bolter 1991). Reading this work results in a paradigm shift; I don’t percieve computers as tools (information processors with widgets) any more, but as media (substrates for communication).In his book, Jay Bolter initially focuses on the history of writing. One of the most important milestones in history was the introduction of the printing press by Gutenberg in the seven-tienth century. It ended an era in which the written page was only shared by an elite (monks and royal community). The rest of society was not able to read or write and relied on the estab-lisment to pass on information1. Society’s balance was disrupted by the printing press. Sud-denly everybody could purchase a book and get information at first hand; established authorities losed their exclusive rights to share (and create) information. In his book, Jay Bolter tells about the social and cultural impact of new technology. More specifically, he dis-cusses how the shift from traditional to electronic media will change writing and reading. Be-fore presenting electronic media in more detail, a short and a rather simplified excursion into cognitive science will be held to explain the word “medium”. Simplistically speaking, our thoughts are chunked into small entities, optimistically called “ideas” or “concepts” (this ap-proach to thinking is discussed further in the next section on hypertext). To communicate with others, we cluster our thoughts into “information”, and transfer those to a specific mediumthe ancient greek strongly preferred speech to written communication; the last was considered to weaken intellectual skills and memory(e.g. air for speaking, paper for writing/reading). The transfer from brain to medium involves a representation scheme.Figure 1: medium, representation scheme and thoughtsThis representation scheme is a structure or template to shape thoughts into symbols, that are in fact elements that can be embedded in the medium. In other words, the abstract scheme is a method to make our thoughts publicly available while the medium serves as a substrate for symbols. Spoken and written language are the most popular schemes in our everyday life. Other existing media (e.g. television, fax) do afford other (but not necessarily disjunct) repre-sentation schemes. Theories on media, schemes, and communication are rapidly evolving. As for now, we will focus on the history and potential of computer-based media. In the post-war period the introduction of computer technology slowly changed society. At first, the expensive power of computers was only exploited for mathematical purposes. A handful of visionaries accomplished the thought that computers were general-purpose machines; due to its quick ac-cess storage memory, the ability to create huge communication networks and its capacity to manipulate symbols, the computer has an unequalled power to act as a new medium. Influ-enced by the great media-guru Marshall McLuhan, computer scientist Alan Kay1 points out the computer’s unique properties in the context of media: he considers the computer as a meta-medium: a container that can hold information of any form, representation schemes and me-dia. Kay was familiar with the possibilities of digital media, in which arbitrary information is converted into digital symbols before storing it into the computer memory. At present, audio, video, pictures, and text exist side by side in popular multimedia systems.The number of facilities to exchange information by computer are rapidly increasing. In the seventies it started with electronic mail and Bulletin Board Systems. Today, a wide variety of communication channels can be used including:• the Online Book Initiative- a database that can be reached on the internet2. It includes elec-tronic versions of literature, children’s books, fairy tales and poems.• the USENET news system - a distributed Bulletin Board-alike system that includes hundreds of discussion groups. The subjects vary from antroposophy to computer science and from rock groups to biology, all these groups receive for about 20-100 postings a day. USENET news is often employed as an informal communication channel among scientists to discuss ongoing research and opinions.•Gopher- a distributed database with campus information, electronic versions of technical re-graphical interfaces. He also introduced the imaginary personal desktop computer called dynabook.2. the internet is a worldwide cluster of networks that connects universities, researchinstitutes and several industries。

Physiol Rev90:859–904,2010;doi:10.1152/physrev.00045.2009.Gut Microbiota in Health and DiseaseINNA SEKIROV,SHANNON L.RUSSELL,L.CAETANO M.ANTUNES,AND B.BRETT FINLAYMichael Smith Laboratories,Department of Microbiology and Immunology,and Department of Biochemistry and Molecular Biology,The University of British Columbia,Vancouver,British Columbia,CanadaI.Preface860II.Overview of the Mammalian Gut Microbiota860A.Humans as microbial depots860B.Who are they?860C.Where are they?861D.Where do they come from?861E.How are they selected?862III.Microbiota in Health:Combine and Conquer862A.Immunomodulation863B.Protection866C.Structure and function of the GIT867D.Outside of the GIT868E.Nutrition and metabolism868F.Concluding remarks870IV.Microbiota in Disease:Mechanisms of Fine Balance870A.Imbalance leads to chaos870B.Microbial intruders of the GIT871C.Disorders of the GIT872D.Disorders of the GIT accessory organs876plex multifactorial disorders and diseases of remote organ systems877F.Bacterial translocation and disease880G.Concluding remarks881V.Signaling in the Mammalian Gut881A.Signaling between the microbiota and the host881B.Signaling between the microbiota and pathogens884C.Signaling between members of the microbiota884D.Signaling between the host and pathogens885VI.Models to Study Microbiota885A.Germ-free animals885B.Mono-associated and bi-associated animals887C.Poly-associated animals887D.Human flora-associated animals888VII.Techniques to Study Microbiota Diversity889A.Culture-based analysis889B.Culture-independent techniques889C.Sequencing methods889D.“Fingerprinting”Methods892E.DNA microarrays893F.FISH and qPCR893G.The“meta”family of function-focused analyses893VIII.Future Perspectives:Have We Got the Guts for It?895 Sekirov I,Russell SL,Antunes LCM,Finlay BB.Gut Microbiota in Health and Disease.Physiol Rev90:859–904, 2010;doi:10.1152/physrev.00045.2009.—Gut microbiota is an assortment of microorganisms inhabiting the length andwidth of the mammalian gastrointestinal tract.The composition of this microbial community is host specific, evolving throughout an individual’s lifetime and susceptible to both exogenous and endogenous modifications. Recent renewed interest in the structure and function of this“organ”has illuminated its central position in healthand disease.The microbiota is intimately involved in numerous aspects of normal host physiology,from nutritionalstatus to behavior and stress response.Additionally,they can be a central or a contributing cause of many diseases,affecting both near and far organ systems.The overall balance in the composition of the gut microbial community,as well as the presence or absence of key species capable of effecting specific responses,is important in ensuring homeostasis or lack thereof at the intestinal mucosa and beyond.The mechanisms through which microbiota exerts its beneficial or detrimental influences remain largely undefined,but include elaboration of signaling molecules and recognition of bacterial epitopes by both intestinal epithelial and mucosal immune cells.The advances in modeling and analysis of gut microbiota will further our knowledge of their role in health and disease,allowing customization of existing and future therapeutic and prophylactic modalities.I.PREFACEHippocrates has been quoted as saying “death sits in the bowels”and “bad digestion is the root of all evil”in 400B.C.(105),showing that the importance of the intes-tines in human health has been long recognized.In the past several decades,most research on the impact of bacteria in the intestinal environment has focused on gastrointestinal pathogens and the way they cause dis-ease.However,there has recently been a considerable increase in the study of the effect that commensal mi-crobes exert on the mammalian gut (Fig.1).In this re-view,we revisit the current knowledge of the role played by the gastrointestinal microbiota in human health and disease.We describe the state-of-the-art techniques used to study the gastrointestinal microbiota and also present challenging questions to be addressed in the future of microbiota research.II.OVERVIEW OF THE MAMMALIANGUT MICROBIOTA A.Humans as Microbial DepotsVirtually all multicellular organisms live in close as-sociation with surrounding microbes,and humans are noexception.The human body is inhabited by a vast number of bacteria,archaea,viruses,and unicellular eukaryotes.The collection of microorganisms that live in peaceful coexistence with their hosts has been referred to as the microbiota,microflora,or normal flora (154,207,210).The composition and roles of the bacteria that are part of this community have been intensely studied in the past few years.However,the roles of viruses,archaea,and unicellular eukaryotes that inhabit the mammalian body are less well known.It is estimated that the human mi-crobiota contains as many as 1014bacterial cells,a num-ber that is 10times greater than the number of human cells present in our bodies (162,264,334).The microbiota colonizes virtually every surface of the human body that is exposed to the external environment.Microbes flour-ish on our skin and in the genitourinary,gastrointesti-nal,and respiratory tracts (43,126,210,323).By far the most heavily colonized organ is the gastrointestinal tract (GIT);the colon alone is estimated to contain over 70%of all the microbes in the human body (162,334).The human gut has an estimated surface area of a tennis court (200m 2)(85)and,as such a large organ,represents a major surface for microbial colonization.Additionally,the GIT is rich in molecules that can be used as nutrients by microbes,making it a preferred site for colonization.B.Who Are They?The majority of the gut microbiota is composed of strict anaerobes,which dominate the facultative anaer-obes and aerobes by two to three orders of magnitude (96,104,263).Although there have been over 50bacterial phyla described to date (268),the human gut microbiota is dominated by only 2of them:the Bacteroidetes and the Firmicutes,whereas Proteobacteria,Verrucomicrobia,Actinobacteria,Fusobacteria,and Cyanobacteria are present in minor proportions (64)(Fig.2,A and B ).Esti-mates of the number of bacterial species present in the human gut vary widely between different studies,but it has been generally accepted that it contains ϳ500to 1,000species (341).Nevertheless,a recent analysis involving multiple subjects has suggested that the collective human gut microbiota is composed of over 35,000bacterial spe-cies(76).FIG .1.Number of publications related to the intestinal microbiotain the last two decades,per year.Data were obtained by searching Pubmed (/pubmed/)with the following terms:intestinal microbiota,gut microbiota,intestinal flora,gut flora,intestinal microflora,and gut microflora.860SEKIROV ET AL.C.Where Are They?The intestinal microbiota is not homogeneous.The number of bacterial cells present in the mammalian gut shows a continuum that goes from 101to 103bacteria per gram of contents in the stomach and duodenum,progress-ing to 104to 107bacteria per gram in the jejunum and ileum and culminating in 1011to 1012cells per gram in the colon (220)(Fig.2A ).Additionally,the microbial compo-sition varies between these sites.Frank et al.(76)have reported that different bacterial groups are enriched at different sites when comparing biopsy samples of the small intestine and colon from healthy individuals.Sam-ples from the small intestine were enriched for the Bacilli class of the Firmicutes and Actinobacteria.On the other hand,Bacteroidetes and the Lachnospiraceae family of the Firmicutes were more prevalent in colonic samples (76).In addition to the longitudinal heterogeneity dis-played by the intestinal microbiota,there is also a great deal of latitudinal variation in the microbiota composition (Fig.2B ).The intestinal epithelium is separated from the lumen by a thick and physicochemically complex mucus layer.The microbiota present in the intestinal lumen dif-fers significantly from the microbiota attached and em-bedded in this mucus layer as well as the microbiota present in the immediate proximity of the epithelium.Swidsinski et al.(303)have found that many bacterialspecies present in the intestinal lumen did not access the mucus layer and epithelial crypts.For instance,Bacte-roides ,Bifidobacterium ,Streptococcus ,members of En-terobacteriacea,Enterococcus ,Clostridium ,Lactobacil-lus,and Ruminococcus were all found in feces,whereas only Clostridium ,Lactobacillus,and Enterococcus were detected in the mucus layer and epithelial crypts of the small intestine (303).D.Where Do They Come From?Colonization of the human gut with microbes begins immediately at birth (Fig.2C ).Upon passage through the birth canal,infants are exposed to a complex microbial population (245).Evidence that the immediate contact with microbes during birth can affect the development of the intestinal microbiota comes from the fact that the intestinal microbiota of infants and the vaginal microbiota of their mothers show similarities (187).Additionally,infants delivered through cesarean section have different microbial compositions compared with vaginally deliv-ered infants (128).After the initial establishment of the intestinal microbiota and during the first year of life,the microbial composition of the mammalian intestine is rel-atively simple and varies widely between different indi-viduals and also with time (179,187).However,after 1yr of age,the intestinal microbiota of children startstoFIG .2.Spatial and temporal aspects of intestinal microbiota composition.A :variations in microbial numbers and composition across the lengthof the gastrointestinal tract.B :longitudinal variations in microbial composition in the intestine.C :temporal aspects of microbiota establishment and maintenance and factors influencing microbial composition.GUT MICROBIOTA861resemble that of a young adult and stabilizes(179,187) (Fig.2C).It is presumed that this initial colonization is involved in shaping the composition of the gut microbiota through adulthood.For instance,a few studies have shown that kinship seems to be involved in determining the composition of the gut microbiota.Ley et al.(161) have shown that,in mice,the microbiota of offspring is closely related to that of their mothers.Additionally,it has been shown that the microbiota of adult monozygotic and dizygotic twins were equally similar to that of their sib-lings,suggesting that the colonization by the microbiota from a shared mother was more decisive in determining their adult microbiota than their genetic makeup(350). Although these studies point to the idea that parental inoculation is a major factor in shaping our gut microbial community,there are several confounding factors that prohibit a definite conclusion on this subject.For exam-ple,it is difficult to take into account differences in diet when human studies are performed.On the other hand, mouse studies are performed in highly controlled envi-ronments,where exposure to microbes from sources other than littermates and parents is limited.Therefore, further investigation is needed to decisively establish the role of parental inoculation in determining the composi-tion of the adult gut microbiota.E.How Are They Selected?Besides the mother’s microbiota composition,many other factors have been found to contribute to the micro-bial makeup of the mammalian GIT(Fig.2C).Several studies have shown that host genetics can impact the microbial composition of the gut.For instance,the pro-portions of the major bacterial groups in the murine in-testine are altered in genetically obese mice,compared with their genetically lean siblings(161).Also,mice con-taining a mutation in the major component of the high-density lipoprotein(apolipoprotein a-I)have an altered microbiota(347).Although these studies suggest that host genetics can have an impact on the gut microbiota,it should be noted that such effects are likely to be indirect, working through effects on general host metabolism.Studies on obesity have also revealed that diet can affect gut microbial composition.Consumption of a pro-totypic western diet that induced weight gain significantly altered the microbial composition of the murine gut(311). Further dietary manipulations that limited weight gain were able to reverse the effects of diet-induced obesity on the microbiota.Given the plethora of factors that can affect micro-bial composition in the human gut,it is perhaps surprising that the composition of the human microbiota is fairly stable at the phylum level.The major groups that domi-nate the human intestine are conserved between all indi-viduals,although the proportions of these groups can vary.However,when genera and species composition within the human gut is analyzed,differences occur. Within phyla,the interindividual variation of species com-position is considerably high(64,89).This suggests that although there is a selective pressure for the maintenance of certain microbial groups(phyla)in the microbiota,the functional redundancy within those groups allows for variations in the composition of the microbiota between individuals without compromising the maintenance of proper function.However,this hypothesis remains to be experimentally tested.III.MICROBIOTA IN HEALTH:COMBINE AND CONQUERSeveral lines of evidence point towards a possible coevolution of the host and its indigenous microbiota:it has been shown that transplantation of microbial commu-nities between different host species results in the trans-planted community morphing to resemble the native mi-crobiota of the recipient host(242),and that gut micro-biota species exhibit a high level of adaptation to their habitat and to each other,presenting a case of“microevo-lution”that paralleled the evolution of our species on the large scale(257,342).Moreover,the host has evolved intricate mechanisms that allow local control of the resi-dent microbiota without the induction of concurrent dam-aging systemic immune responses(181).This adaptation is not surprising when considering that different bacterial groups and species have been implicated in various aspects of normal intestinal devel-opment and function of their host(Fig.3).In recent years, we have seen a tremendous increase in gut microbiota-related research,with important advances made towards establishing the identity of specific microbes/microbial groups or microbial molecules contributing to various aspects of host physiology.Concurrently,host factors involved in various aspects of development and matura-tion targeted by the microbiota have been identified.How-ever,a large proportion of research aimed at identifying particular microbiota contributors to host health was done in ex-germ-free(GF)animals mono-or poly-associ-ated with different bacterial species representative of dominant microbiota phyla(e.g.,Bacteroides thetaio-taomicron,Bacteroides fragilis,Lactobacillus spp.)or stimulated with particular microbial components[e.g., lipopolysaccharide(LPS)and polysaccharide A(PSA)]. Thus any discovered contribution of these particular mi-crobial species or molecules to a distinct host structure/ function points to their ability to provide the said contri-bution,but not to the fact that they are the primary microbe/molecule responsible for it in a host associated with a complete microbial community.Additionally,as862SEKIROV ET AL.current culturing techniques limit our ability to isolate strictly anaerobic microbiota members or members with complex nutrient requirements and mutualistic depen-dence on other microbial gut inhabitants (62),the re-search on the contribution of specific gut microbes to various physiological processes is limited to studying a small number of currently isolated and culturable micro-organisms.However,improvements to available culturing techniques (62)and enhanced understanding of microbial metabolism gained from culture-independent studies hold promise to greatly expand this field of research.A.ImmunomodulationThe importance of the gut microbiota in the develop-ment of both the intestinal mucosal and systemic immune systems can be readily appreciated from studies of GF (microbiota lacking)animals.GF animals contain abnor-mal numbers of several immune cell types and immune cell products,as well as have deficits in local and sys-temic lymphoid structures.Spleens and lymph nodes of GF mice are poorly formed.GF mice also have hypoplas-tic Peyer’s patches (PP)(180)and a decreased number of mature isolated lymphoid follicles (27).The number of their IgA-producing plasma cells is reduced,as are the levels of secreted immunoglobulins (both IgA and IgG)(180).They also exhibit irregularities in cytokine levels and profiles (220)and are impaired in the generation of oral tolerance (132).The central role of gut microbiota in the development of mucosal immunity is not surprising,considering that the intestinal mucosa represents the largest surface area in contact with the antigens of the external environment and that the dense carpet of the gut microbiota overlying the mucosa normally accounts for the largest proportion of the antigens presented to the resident immunecellsFIG .3.The complex web of gut microbiota contributions to host physiology.Different gut microbiota components can affect many aspects of normal host development,while the microbiota as a whole often exhibits functional redundancy.In gray are shown members of the microbiota,with their components or products of their metabolism.In white are shown their effects on the host at the cellular or organ level.Black ellipses represent the affected host phenotypes.Only some examples of microbial members/components contributing to any given phenotype are shown.AMP,antimicrobial peptides;DC,dendritic cells;Gm Ϫ,Gram negative;HPA,hypothalamus-pituitary-adrenal;Iap,intestinal alkaline phosphatase;PG,peptidoglycan;PSA,polysaccharide A.GUT MICROBIOTA863and those stimulating the pattern recognition receptors [such as the TLRs and NOD-like receptors(NLRs)]of the intestinal epithelial cells(238).A detailed overview of the intestinal mucosal immunity can be found elsewhere(110, 194).Briefly,it is composed of the gut-associated lym-phoid tissue(GALT),such as the PP and small intestinal lymphoid tissue(SILT)in the small intestine,lymphoid aggregates in the large intestine,and diffusely spread immune cells in the lamina propria of the GIT.These immune cells are in contact with the rest of the immune system via local mesenteric lymph nodes(MLN).In addi-tion to the immune cells,the intestinal epithelium also plays a role in the generation of immune responses through sampling of foreign antigens via TLRs and NLRs (238).The mucosal immune system needs to fulfill two, sometimes seemingly conflicting,functions.It needs to be tolerant of the overlying microbiota to prevent the induc-tion of an excessive and detrimental systemic immune response,yet it needs to be able to control the gut micro-biota to prevent its overgrowth and translocation to sys-temic sites.Gut microbiota is intricately involved in achieving these objectives of the GIT mucosal immune system.1.Mucosal/systemic immunity maturationand developmentA major immune deficiency exhibited by GF animals is the lack of expansion of CD4ϩT-cell populations.This deficiency can be completely reversed by treatment of GF mice with PSA of Bacteroides fragilis(197).Mazmanian et al.(197),in an elegant series of experiments,have shown that either mono-association of GF mice with B. fragilis or oral treatment with its capsular antigen PSA induces proliferation of CD4ϩT cells,as well as restores the development of lymphocytes-containing spleen white pulp.Recognition of PSA by dendritic cells(DCs)with subsequent presentation to immature T lymphocytes in MLNs was required to promote the expansion.GF animals exhibit systemic skewing towards a Th2cytokine profile, a phenotype that was shown to be reversed by PSA treat-ment,in a process requiring signaling through the inter-leukin(IL)-12/Stat4pathway(197).Thus exposure to a single structural component of a common gut microbiota member promotes host immune maturation both locally and systemically,at the molecular,cellular,and organ levels.While B.fragilis PSA appears to have a pan-systemic effect on its host’s immunological development,addi-tional gut microbiota constituents and their components have been shown to have immunomodulatory capacity, highlighting the overlapping,and possibly additive or syn-ergistic,functions of the members of the gut microbial community.For instance,various Lactobacilli spp.have been shown to differentially regulate DCs,with conse-quent influence on the Th1/Th2/Th3cytokine balance at the intestinal mucosa(44),as well as on the activation of natural killer(NK)cells(72).Additionally,peptidoglycan of Gram-negative bacteria induces formation of isolated lymphoid follicles(ILF)via NOD1(an NLR)signaling. Following recognition of microbiota through TLRs,these ILF matured into B-cell clusters(27).A complex microbial community containing a signif-icant proportion of bacteria from the Bacteroidetes phy-lum was shown to be required for the differentiation of inflammatory Th17cells(133).Interestingly,the coloniza-tion of GF mice with altered Schaedlerflora(ASF)was insufficient to promote differentiation of Th17cells,de-spite the fact that ASF includes a number of bacteria from the Bacteroidetes phylum(59).Thisfinding highlights the complexity of interactions between the host and the mi-crobiota and within the microbiota community,indicating that cooperation between microbiota members may be required to promote normal host development.In view of this,thefinding by Atarashi et al.(9),that administration of ATP(which is found in high concentrations in the GIT of SPF,but not GF mice)was sufficient to trigger differ-entiation of Th17cells in GF mice,is all the more intrigu-ing.This raises questions about the metabolic capabilities of different members of the gut microbiota and lends indirect evidence to their metabolic interdependence. 2.Tolerance at the GIT mucosaThe GIT needs to coexist with the dense carpet of bacteria overlying it without an induction of excessive detrimental immune activation both locally and systemi-cally.Prevention of excessive immune response to the myriad of bacteria from the gut microbiota can be achieved either through physical separation of bacteria and host cells,modifications of antigenic moieties of the microbiota to render them less immunogenic,or modula-tion of localized host immune response towards toler-ance.Resident immune cells of the GIT often have a phe-notype distinct from cells of the same lineage found sys-temically.For instance,DCs found in the intestinal mu-cosa preferentially induce differentiation of resident T cells into Th2(134)and Treg(144)subsets,consequently promoting a more tolerogenic state in the GIT.In a series of in vitro experiments,DCs were conditioned towards this tolerogenic phenotype by intestinal epithelial cells(IEC) stimulated with various gut microbiota isolates,such as different Lactobacillus spp.and different Escherichia coli strains(346).The conditioning was dependent on micro-biota-induced secretion of TSLP and transforming growth factor(TGF)-␤by IECs(346).Interestingly,the Gram-posi-tive Lactobacilli were more effective than the Gram-nega-tive E.coli in conditioning the DCs towards a tolerogenic864SEKIROV ET AL.phenotype,likely due to the greater abundance of Lactoba-cilli at the intestinal mucosa,as hypothesized by the authors of the study.Another effective mechanism of preventing colitogenic responses is employed by B.thetaiotaomicron, which prevents activation of the proinflammatory transcrip-tion factor NF␬B by promoting nuclear export of a transcrip-tionally active NF␬B subunit RelA in a PPAR␥-dependent fashion(143).An alternate mechanism of preventing NF␬B activation in response to the gut microbiota is through TLR compartmentalization.Lee et al.(159)have shown that while activation of basolaterally located TLR9promotes NF␬B activation,signaling originating from the apical sur-faces(i.e.,induced by normal gut microbiota)effectively prevents NF␬B activation,promoting tolerance to the resi-dent bacteria.In addition to microbiota-mediated tolerogenic skew-ing of localized immune responses,the host can also decrease the proinflammatory potential of microbiota constituents.The presence of the gut microbiota exposes the host to a vast amount of LPS found on the outer membranes of Gram-negative bacteria.Systemic reac-tions to LPS lead to highly lethal septic shock(19),a very undesirable outcome of host-microbiota interactions.One way to avoid this disastrous scenario is to minimize the toxic potential of LPS,which can be done via dephosphor-ylation of the LPS endotoxin component through the ac-tion of alkaline phosphatases,specifically the intestinal alkaline phosphatase(Iap)(18).Bates et al.(18)have demonstrated that Iap activity in the GIT of zebrafish reduced MyD88-and tumor necrosis factor(TNF)-␣-me-diated recruitment of neutrophils to the intestinal epithe-lium,minimizing the inflammatory response to the gut microbiota and promoting tolerance.Iap activity in ze-brafish GIT was induced via MyD88signaling and was dependent on the presence of microbiota:it could be induced by mono-association with Gram-negative(GmϪ) bacterial isolates(such as Aeromonas and Pseudomonas) or treatment with LPS.Association with Gram-positive (Gmϩ)bacterial isolates(such as Streptococcus and Staphylococcus)failed to promote Iap activity(18),dem-onstrating that at least some host responses to its colo-nizing microbes are group specific.In addition to detoxification of LPS by Iap,IECs also acquire tolerance to endotoxin through downregulation of IRAK-1,which is essential for endotoxin signaling through TLR4(174).This tolerance is acquired at birth, but only in vaginally delivered mice that were exposed to exogenous LPS during passage through the birth canal (174),again highlighting the active role of the microbiota in tolerogenic conditioning of mucosal immune responses at the GIT.Another effective strategy of avoiding excessive im-mune activation at the intestinal mucosa is physical sep-aration of the microbiota from the host mucosal immune system.Recently,Johansson et al.(136)have shown that the mucus layer overlying the colonic mucosa is effec-tively divided into two tiers,with the bottom tier being devoid of bacteria,and the more dynamic top tier being permeated by members of the gut microbiota.3.Control of the gut microbiotaWhile healthy gut microbiota is essential to promote host health and well-being,overgrowth of the bacterial population results in a variety of detrimental conditions, and different strategies are employed by the host to pre-vent this outcome.Plasma cells residing at the intestinal mucosa pro-duce secretory IgA(sIgA)that coats the gut microbiota and allows local control of their numbers(181,310).They are activated by resident DCs that sample the luminal bacteria,but are restricted in their migration to only as far as the local MLNs,so as to avoid induction of a systemic response to the gut microbiota(181).The presence of the gut microbiota is a prerequisite to activate gut DCs to induce maximal levels of IgA production,while treatment of GF mice with LPS augmented IgA production but to lower levels(195).Furthermore,Bacteroides(GmϪbac-teria)were found to be more efficient in induction of sIgA than Lactobacilli(Gmϩbacteria)(343).Interestingly,al-though GmϪbacteria or their structural components were able to stimulate IgA production,the absence of intestinal IgA resulted in overgrowth of SFB,a group of Gmϩbacteria(300),suggesting that induction of sIgA might also be a form of competition between different microbiota members.Two secretory IgA(sIgA)subclasses exist:sIgA1 (produced systemically and at mucosal surfaces)and sIgA2(produced at mucosal surfaces).sIgA2is more resistant to degradation by bacterial proteases than sIgA1 (202),so it is not surprising that it was found to be the main IgA subclass produced in the intestinal lamina pro-pria(107).Production of a proliferation-inducing ligand (APRIL)by IECs activated via TLR-mediated sensing of bacteria and bacterial products was required to induce switching from sIgA1to sIgA2production(107).Both Gmϩand GmϪbacteria,as well as bacterial LPS and flagellin,were similarly effective in inducing APRIL pro-duction(107).Thus exposure of the gut mucosa to its resident microbiota not only promotes IgA secretion,but also ensures that the optimally stable IgA subclass is produced.It is also of interest to note that sIgA fulfills a dual function at the intestinal mucosa:in addition to preventing overgrowth of the gut microbiota,it also min-imizes its interactions with the mucosal immune system, diminishing the host’s reaction to its resident microbes (234).sIgA is not the only host factor preventing the micro-biota from breaching its luminal compartment:antimicro-bial peptides(AMP)produced by the host also work toGUT MICROBIOTA865。

resubmitted manuscript received全文共四篇示例，供读者参考第一篇示例：在学术界，大多数研究人员在提交论文时都会遇到审稿意见需要修改后再次提交的情况。

这种情况下，就会出现resubmitted manuscript received这个词汇，即已接收修改后再次提交的稿件。

本文将对resubmitted manuscript received进行详细解释，并探讨其在学术研究中的重要性及影响。

对于编辑部来说，resubmitted manuscript received也是一个重要的环节。

它代表了期刊对作者研究的认可，并表示编辑部对稿件的重视。

在收到resubmitted manuscript后，编辑部将重新安排审稿程序，确保对修改后的稿件进行深入评价。

这也体现了学术期刊对提高稿件质量和保证学术研究严谨性的关注和重视。

resubmitted manuscript received是学术研究中的一个重要环节。

它不仅代表了作者对审稿意见的积极响应，也体现了期刊对研究质量的关注。

通过resubmitted manuscript received这一过程，能够保证学术研究的深入和推进，进而促进学术界的发展和繁荣。

希望各位作者在接到resubmitted manuscript received的消息时，能够认真对待审稿意见，提高稿件质量，为学术研究的进步贡献自己的一份力量。

【以上内容仅供参考】。

第二篇示例：最近，《resubmitted manuscript received》一词在学术界中引起了不小的关注。

这个术语指的是在投稿后修订的论文被重新提交到期刊编辑部，并最终被接受发表。

对于学者们来说，resubmitted manuscript received既代表着一种成功的喜悦，也意味着一段辛苦的努力。

让我们来看看resubmitted manuscript received的过程。

Development of Transport and Manipulation Robotic System for Operation on the Spacecraft SurfaceI.Y. Dalyaev1, E.Y. Smirnova1, I.A. Vasiliev1, A.A. Koshurina2 and M.S. Krasheninnikov21194064, Russia, St. Petersburg, Tihoreckij prospect, 21, CNII RTK2603950, Russia, Nizhny Novgorod, Minina street, 24, NNSTU named after R.E. AlekseevAbstract—This work is devoted to creation of transport and manipulation robotic system (TMS) to work on the surface of the spacecraft. Undoubtedly, the TMS is a powerful tool in solving problems of the study of space, so it is evident that the role of space robotics is continuously increasing due to the need to develop and maintain global communications, navigation and surveillance, permanent manned and visited space stations and bases on the Moon and on the planets of the solar system. As a basic model of a transport subsystem we proposed the symmetrical structure with seven degrees of freedom. The paper describes the structure of the system and provides its kinematics. The article presents the data obtained from the simulation in the MSC.ADAMS package. Thanks to the simulation were obtained predictive characteristics and defined functions of the robotic system. This allowed us to define a technical way to create transport and manipulation robotic system to service the outer surface of the spacecraft. Also this paper deals with the grouped operation of interacting objects (manipulators). These methods of grouped operation are mainly used to control groups of mobile robots, but as specified in [1.2], the general principles of control of the group interaction do not depend on the physical nature (and moreover on the kinematic characteristics) of the interacting objects.Keywords-transport and manipulation systems; robotics; group interaction; grippers; spacecraft; spaceI.I NTRODUCTIONModern space robotics is a promising scientific and technical field, covering a comprehensive design, development, production and operation of robotic tools for space purposes.[3] Such systems are being actively developed and used in international space projects of the US, Canada, Japan, the European Union and Russia. Modern robotic systems differ in size, structure, purpose, methods of work and control.Nowadays there are manipultion systems for space applications that can be used to perform a strictly limited list of technological operations. The limited range of operations is connected with fixed fastening of manipulation systems on the outer surface of the spacecraft (SC), with the complexity of their control during the EVA (extravehicular activity).The experience of existing of the manipulation systems on the ISS shows that their capacity is not sufficient to carry out all necessary maintenance work spectrum. In some cases, the astronauts have to go into space and perform a series of complex operations manually [4].II.M ODELAs a basic model of kinematics of the transport subsystem we propose the use of symmetrical structure with sevendegrees of freedom, shown in Figure 1.FIGURE I.KINEMATIC STRUCTURE OF TRANSPORT ANDMANIPULATION PLATFORMwhere: 1- technological platform; 2 - elbow joint; 3 - connecting links; 4 - roll joints; 5 – pitch joints; 6 - yaw joints;7 - grippersThe chosen structure has a kinetic redundancy because of its symmetry, which allows us to operate with both feet equally. At the same time we have the necessary degrees of freedom for positioning and orientating of the gripping. Therefore, such kinematics allows the transport and manipulation system to move on surfaces of any shape with obstacles. The symmetrical design ensures equal opportunities when operating by the right and by the left foot, and allows us to use standardized algorithms for the motion.As manipulation subsystem we chose the manipulator with a specialized gripper and sex degrees of freedom. Six degrees of freedom is sufficient for accurate positioning of the objects and performance of the necessary functions. To develop approaches to engineering of mechatronic components of the transport and manipulation system we developed its design shape. General view of the multi-functional transport andmanipulation is shown in Figure 2.FIGURE II.MULTIFUNCTIONAL TMSInternational Conference on Advanced Manufacturing and Industrial Application (ICAMIA 2015)where: 1 – grippers holding for mounting on the lifting elements of the ISS; 2 - gripping multifunctional; 3 - joints; 4 - hardware unit; 5 - arm; 6 - a set of television equipment.The modular structure of the TMS allows, if necessary, to make the transformation or reconfiguration of the robot to perform the tasks of different groups [5].III.S IMULATIONTo perform various calculations we constructed three-dimensional solid-state computer model of the TMS. The simulation was performed in the MSC.ADAMS package. The study was carried out by performing model simulations of motion and analysis of simulation results.Simulations have shown the ability of the TMS with the selected kinematics perform the necessary tasks, in particular: – To move on the rail of the ISS RS, considering the irregularity of their arrangement, the curvature of the surface and the presence of the mating surfaces of the ISS RS;– To overcome the gaps in the tracks of the handrails up to 1.5 m;– To carry out inspection operations at the maximum distance to the test object 2.6 m minimum;– To perform manipulation operations with a maximum distance to the test object 1.6 m minimum.The developed TMS can be located on one handrail on different identically oriented handrails and in various differently orientated handrails (e. g, perpendicular). Therefore, an important expectation was to evaluate the elastic deformation of the corners of aluminum handrails of the ISS RS, which will have an impact on the value of the deflectionangle of the TMS (Figure 3).FIGURE III.MOUNTING OF THE TMC ON ONE HANDRAIL (TOP) AND TWO PERPENDICULAR HANDRAILS (BOTTOM)As the gripper of the TMS we propose the use of one of the two variants of the fixing grippers shown in Figure 4. We prefer the second option (right), equipped with a six-component force and torque sensor; this gripper is shown inthe general model of the TMS [6-8].FIGURE IV.VARIANTS OF FIXING GRIPPERIV.C OORDINATED CONTROL OF MANIPULATORS For the stable movement along the rails we need toharmonize the movement the two transport manipulators so that:– They were not released at the same time;– They are not simultaneously restrained;– Do not interfere with each other.To solve these problems we used the principles of control drawn from the ideology of the mobile robots control.This ideology is described in detail in [9, 10]. Let us say here that the authors applied a similar approach to control a group of robots designed to rescue people at oil platforms in case of disaster.V.R ESULTSOn the basis of the calculations the kinematic robot structure was justified and predictive characteristics of TMS were formulated (Table 1).TABLE I. PROJECTED CHARACTERISTICS TMSParameter ForecastThe number of joints, pieces 13Power supply voltage, V 27 Maximum power consumption, W 600Power consumption in storage mode, W 100The capacity of an independent source of power Wh (Al) 2400 (90) + 2400 (90) The duration of battery life, h 8 Dimensions in the folded state, mm 500 х 500 х 1200 Bridging the slopes, m min 1,5Reach the working bodies, m min 2,6 Currently the Central Research and Development Institute of Robotics and Technical Cybernetics carries out the work on testing a prototype model of specialized manipulation system (SMS). After that the flight model of the SMS will be developed, and later it will be tested on the ISS in real space (Figure 5).FIGURE V.THE PROTOTYPE OF A SPECIALIZED MANIPULATIONSYSTEMVI.C ONCLUSIONThe computer studies have shown:– Performance and functionality of the TMS basic kinematic scheme;– Performance of functional modules (joints) of the TMS in outer space, taking into account extreme temperatures;– The possibility of implementing force-torque-based control on the basis of the studied sensors;– Functionality of the TMS modules and the potential opportunity for rapid module replacement and reconfiguration.These given results demonstrate the ability and determine the technical way to create a transport manipulation robotic system to service the outer surface of the spacecraft.The next step in the development of the TMS is the creation of groups of the organized TMS to perform complex operations on the outer surface of the spacecraft in space. Therefore, in the further work we should pay attention to the development of algorithms and principles of group interaction of the TMS for space. The algorithms of group interactions for terrestrial robotic tools can serve as prototypes. The work on them is carried out in the NNSTU named after R. E. Alekseev, with the support of the Ministry of Education and Science within the frames of the program «Research and development on priority directions of scientific-technological complex of Russia for 2014-2020», the unique identifier of the project: RFMEFI57414X0055.R EFERENCES[1]I. A. Kalyaev, A. R. Gaiduk, S. G. Kapustyan. Models and algorithmsfor collective control in groups of robots. M: FIZMATLIT 2009, p. 280. [2] 2. S.G. Kapustyan. Methods of self-organization of distributedinformation and control systems, of intelligent multi-robotic systems // Herald of computer and information technologies. №3 (93), 2012. pp 35-41.[3]Strategy project for development of space activity in Russia up to 2030and beyond.[4]Development and research of a method for compensating the dynamicinteraction in the system "small spacecraft - manipulator" in the problems of the Earth shooting camera guidance / A. A .Carandaev // Proceedings of the XX International Scientific - Technical Conference "Extreme Robotics. Nano -, micro - and macro-robots(ER - 2009) ". - Taganrog: Publishing house TTI SFU, 2009. - P. 98 - 99 [5] A. A. Gradovtsev. Robotic support for future space infrastructurefacilities / A. A. Gradovtsev, A. S. Kondratiev A. N. Timofeev // Extreme Robotics: Proceedings of the International scientific and technical Conference November 23-25, 2011, St. Petersburg., 2011. [6]http://www.lorenz-messtechnik.de/english/files/publications/Management_Systems_Calibra tion _Torque.pdf http://www.lorenz-messtechnik.de/english/company/torque_measurement_technology.php [7]https:///ProductDocuments/Manuals/1800/Operating Service/ Manual.pdf[8]I. A. Vassilyev , S. A. Polovko., E. Y. Smirnova. Organization of groupcontrol of mobile robots for special tasks of robotics // Scientific and technical statements STU, № 1 for 2013[9]I. Vassilyev, A. Koshurina, M. Krasheninnikov, E. Smirnova. The use ofmobile robots groups for rescue missions in extreme climatic conditions // Annals of 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2014。

关节松动术——颈椎MAITLAND’S VERTEBRAL MOBILIZATION FOR CERVICAL SPINESelf Introduction◆◆◆◆◆◆◆◆专业方向：技术简介◆◆定义◆joint mobilization◆关节活动允许范围内完成的一种手法操作技术◆被动运动◆生理运动附属运动◆◆◆◆◆◆基本概念physiological movement◆生理范围内◆◆accessory movement◆自身及其周围组织允许范围内◆◆◆◆◆◆◆生理运动恢复后，如果关节仍有疼痛或僵硬，可能附属运◆先改善◆促进颈椎的附属运动与生理运动◆椎间关节被动附属运动◆椎间关节被动生理运动手法分级◆◆：在任意关节活动范围内大范围运动，但不接触到僵硬及有阻力的◆：大范围运动，但要接触到僵硬或感到阻力的关节位置。

◆：小范围运动，在僵硬或感到阻力的关节活动的末端位置。

Con’t …检查Examination主观检查Subjective Examination客观检查Objective Examination 假设Hypothesis治疗Treatment再检查Re-examination如何选择颈椎关节松动治疗技术1.2.3.4.5.6.1.激惹性 Irritability指病人的症状能被不同激发因素所触发的程度。

决定因1.How much?易程度2.How severe?强度3.How long lasting让症状平复所时间激惹性高低判断◆低激惹性：1.2.3.◆高激惹性：1.2.3.激惹性与手法选择的关系Con’t◆激惹性太高，如果难以忍受关节松动治疗（甚至到1级）的患(Most Painful)◆激惹性较高的患者，手法治疗尽量选择可以减轻或更少疼痛◆激惹性不高的患者，选择更易引出他的症状的技术便于评估2.根据症状的分布Area of symptomcentral or bilateral中央型及对称型Unilateral 单侧型– use bilateral effecting technique 双侧效应的治疗手法use unilateral effecting techniques 单侧效应的治疗手法Central PATractionLongitudinal movement Unilateral PA Rotation Transverse movement Side flexion3.关松方向的选择技术方向Rotation 旋转摆动Away from side of symptom 与症状方向相反Side flexion 侧屈摆动Away from side of symptom 与症状方向相反Unilateral PA 侧方PA On the side of symptom 在症状的同侧Transverse movement 横向松动Do forward side of symptom 朝向症状的方向4.相对重要性1.2.3.5.技术惯用性技术惯用于Traction 神经根症状Rotation 椎间盘源性的症状（To less painful sidefirst）Unilateral PA 椎间孔挤压，小关节退变优先次序单侧症状Unilateral PA=RotationTractionSide flexion=Transversemovement对称症状Central PAbilateral PALongitudinal movementTraction=Rotation6.正常的治疗分级 /进阶◆◆◆功能受限的◆◆◆6.正常的治疗分级 /进阶◆◆级用于激惹性高的疼痛◆级用于激惹性低的疼◆也可更为精细分级+ /◆◆◆muscle spasm 改善关节僵硬 stiffness颈椎关节松动禁忌症及注意事项禁忌症表现恶性肿瘤体重骤减，夜间痛等急性炎症红肿热痛等脊髓受压四肢麻木，步态异常，病理征阳性近期骨折重大创伤后，X-ray重度骨质疏松老年女性，骨密度报告注意事项需注意表现椎动脉，椎基底动脉供血不足需小心，包括颈椎牵引颈椎位置引起头晕；晕厥病史强直性脊柱炎、类风湿性关节炎小心颈椎牵引脊柱活动度受限，手足变形椎体滑脱注意力度下腰段常见，L4-5，X-ray 急性神经根刺激/压迫应采用关节分离，重复次数少，持续时间长严重的疼痛，麻木，无力关节松动的注意事项患者必须充分放松：舒适体位、温度适宜、环境安静、治疗师治疗师必须放松：良好的身体力学，特别是保持脊柱舒展。