We will use the following vector norms: (1.4).
We use the following vector potential, A y2, to introduce the local B z enhancement: begin{array}{*{20}l} A_{y2} x,z)&=-epsilon_{2}B_{0}gamma dexpleft -frac{z^{2}}{dexpleft -frac{z^{2}(-l_{x} < x/d < 0), end{array} (9).
Then the symbol λ min ( S ) ( λ max ( S ) ) will denote the minimal (maximal) eigenvalue of S. We will also use the following vector norms: ∥ x ( t ) ∥ : = ∑ i = 1 n x i 2 ( t ), ∥ x ( t ) ∥ τ, ξ : = ∫ t − τ t e − ξ ( t − s ) ∥ x ( s ) ∥ 2 d s, where x = ( x 1, x 2, …, x n ) T and ξ is a real parameter.
For convenience, we use the following vector notation: (3) v (t ) = (v CC (t ), v CD (t ), v DC (t ), v DD (t ) ) g I = (R, S, T, P ) g II = (R, T, S, P ) Using this notation, we can write the players' expected payoffs in round t as π I (t ) = g I ⋅ v (t ) and π II (t ) = g II ⋅ v (t ).
For this purpose we used the following vectors: AAV5 containing the IKK2dn gene (AAV5.IKK2dn) or AAV2 containing the IκBα-supressor gene (AAV2.IκBα SR).
Subsequently, the vector RLS algorithm [30] can be used to formulate the following vector RLS-based joint CIR, CFO and SFO tracking scheme.
Throughout this paper, we will denote the relative interior of S p by S o p, and we will use the following conventions for vectors in the Euclidean space R n for vectors x : = ( x 1, …, x n ) and y : = ( y 1, …, y n ) : Consider the nonlinear programming problem (VOP): where X = { x ∈ R n : g ( x ) ≧ b, x ≧ 0 }, f : R n → R p, g : R n → R m are continuously differentiable.
We use the following conventions for vectors in (mathbb{R}^{n}): begin{aligned} x= y Leftrightarrow & x_{i}= y_{i},quad forall i= 1, ldots,n; xleqq y Leftrightarrow & x_{i}leq y_{i},quad forall i=1,ldots,n; xleqslant y Leftrightarrow & x_{i}leq y_{i},quad forall i=1,ldots,n, ineq j mbox{ and }x_{j}< y_{i} mbox{ for some } j; x< y Leftrightarrow & x_{i}< y_{i},quad forall i= 1, ldots,n.
In the same way, the following vector is used to denote the closeness between the genes and the phenotypes in the disease phenotype network: Φ g = (Φ gp 1, Φ gp 2, Φ gp 3,…, Φ gp n ).
To denote the association between a phenotype and a gene, the following formulation (2) is defined: (2) Φ g p ′ = ∑ g ′ ∈ G (p ′ ) e − L g g ′ 2. The following vector is used to denote the similarities between the query phenotype and all phenotypes in the disease phenotype network: S p = (S pp 1, S pp 2, S pp 3,…, S pp n ).
The following vector combinations were used for transposition assays: a) pT2B/ puro alone, b) pT2B/ puro + no-SB100X vector, c) pT2B/ puro + CMV-SB100X, d) pT2B/ puro + mNUS-SB100X.
