Locked nucleic acids biosensor for detection of BCR/ABL fusion gene using benzoate binuclear copper (II) complex as hybridization indicator

Benzoate binuclear copper (II) complex, [Cu₂(C₇H₅O₂)₄(C₂H₆O)₂] (abbreviated as CuR₂) was prepared and its interaction with double-stranded salmon sperm DNA (dsDNA) in pH 7.4 phosphate buffer solution was studied by electrochemical experiments at the Au electrode (AuE). It was revealed that CuR₂ presented an excellent electrochemical activity on AuE and could bind with dsDNA by intercalation mode. The CuR₂ was further utilized as a new indicator in the fabrication of an electrochemical DNA biosensor for detection of BCR/ABL fusion gene. The biosensor based on nanogold (NG) modified AuE was developed by using thiolated-hairpin locked nucleic acids (LNA) as the capture probe for hybridization with BCR/ABL fusion gene. The results indicated this new method has excellent specificity for single-base mismatch and complementary after hybridization. The constructed electrochemical DNA biosensor achieved a detection limit of 1.0 × 10⁻¹⁰ M for complementary target DNA with a good stability.     

Self-intersection elimination in metamorphosis of two-dimensional curves

H :[0, 1]× ³→ ³, where H(t, r) for t=0 and t=1 are two given planar curves C ₁(r) and C ₂(r). The first t parameter defines the time of fixing the intermediate metamorphosis curve. The locus of H(t, r) coincides with the ruled surface between C ₁(r) and C ₂(r), but each isoparametric curve of H(t, r) is self-intersection free. The second algorithm suits morphing operations of planar curves. First, it constructs the best correspondence of the relative parameterizations of the initial and final curves. Then it eliminates the remaining self-intersections and flips back the domains that self-intersect.     

Computational research of the dynamics of bubble cavities and supercavity in a pipe filled with degassed liquid under hydroblows

Further substantiation of the reliability of the method for calculation of an unsteady flow of liquid in a pipeline with coordinate and time variant velocity of sound, developed by the author and previously published in the journal “Matematicheskoye modelirovanie,” is presented. The dependences for variations with x and t in total volume (W _Σ) of bubble cavities, the volume of the supercavity (W _φ2), steam quality (α), the velocity of sound (a), true pressure (P′), and the mass flow rate (G) of a liquid or steam-and-liquid mixture during the course of two cycles, calculated according to the proposed method, for the characteristic cross sections of a simple pipeline filled with cavitating degassed liquid (near the feeding reservoir (x = 0), in the middle of the pipe (x = 0.5L), and near the completely closed throttle (x = L), where x is a longitudinal coordinate and L is the length of the pipe) and for the characteristic times (t) of the development of cavitation processes (for the maximum length of the region of cavitation (t _km ), for the maximum total volume of bubble cavities (t _W ), and for the complete closure of all cavities (t _c )), are presented. An analysis of these dependences is given, which reveals the dynamics of cavitation processes in a pipe filled with degassed cavitating liquid under hydroblows.     

A magnetic bead-based DNA extraction and purification microfluidic device

This article introduces a novel magnetic bead-based DNA extraction and purification device using active magnetic mixing approach. Mixing and separation steps are performed using functionalised superparamagnetic beads suspended in cell lysis buffer in a circular chamber that is sandwiched between two external magnetic coils. Non-uniform nature of magnetic field causes temporal and spatial distribution of beads within the chamber. This process efficiently mixes the lysis buffer and whole blood in order to extract DNA from target cells. Functionalized surface of the magnetic beads then attract the exposed DNA molecules. Finally, DNA-attached magnetic beads are attracted to the bottom of the chamber by activating the bottom magnetic coil. DNA molecules are extracted from magnetic beads by washing and re-suspension processes. In this study, a circular PMMA microchamber, 25 μL in volume, 500 μm in depth and 8 mm in diameter was fabricated to purify DNA from spiked bacterial cell cultures into the whole blood sample using Promega Magazorb DNA extraction kit. The lysis efficiency was evaluated using a panel of Gram-positive (Bacillus subtilis) and Gram-negative (Escherichia coli) bacterial cells cultures into the blood sample to achieve approximately 100,000 copy levels inside the chip. Manufacturer’s standard extraction protocol was modified to a more simplified process suitable for chip-based extraction. The lysis step was performed using 5 min incubation at 56 °C followed by 5 min incubation at room temperature for binding process. Temperature rise was generated and maintained by the same external magnetic coils used for active mixing. The yield/purity and recovery levels of the extracted DNA were evaluated using quantitative UV spectrophotometer and real-time PCR assay, respectively. Real-time PCR results indicated efficient chip-based bacterial DNA extraction using modified extraction protocol comparable to the standard bench-top extraction process.     

Mapping Between 2-D Meshes of the Same Size

1IntroductionInrece11tyears,n1a11yparallelalgoritlImshavebeendesignedtosolvedifferentproblemso1lvario1ls11etworktopologics.Bi11arytrees,meshesandhypercubesarethethreeimportal1tl1etworktop()logieswllicllhaterpcoivedintensivestlldy.WiththeadvanceofVLSI,manyllewl1etworkssuchasstargrapl1[1]havebeenorwiIlbeintroduced.Inor相似文献   

System Reduction and Solution Algorithms for Singular Linear Difference Systems under Rational Expectations

A first-order linear difference system under rational expectations is, AEy_t+1|I_t=By_t+C(F)Ex_t|I_t , where y_t is a vector of endogenous variables;x_t is a vector ofexogenous variables; Ey_t+1|I_t is the expectation ofy_t+1 givendate t information; and C(F)Ex_t|I_t =C₀x_t+C₁Ex_t+1|I_t+dot;s +C_nEx_t+n|I_t. If the model issolvable, then y_t can be decomposed into two sets of variables:dynamicvariables d_t that evolve according toEd_t+1|I_t = Wd_t + ¶si_d(F)Ex_t|I_t and other variables thatobey the dynamicidentities f_t =–Kd_t–¶si_f(F)Ex_t|I_t. We developan algorithm for carrying out this decomposition and for constructing theimplied dynamic system. We also provide algorithms for (i) computing perfectforesight solutions and Markov decision rules; and (ii) solutions to relatedmodels that involve informational subperiods.     

Linear programming with uncertain objective function: Minimax solution for relative loss

Given the uncertain linear cost functionz=c′x, with parameter vectorc belonging to a bounded convex polyhedronC and a set of linear constrainst with a bounded solution setX, a functionF (x) is defined as representing the maximum relative loss with respectc∈C for a givenx and the problem is set of findingx∈X minimizingF (x). The work is based on the theory of multiparametric linear programming and on the theory of mathematical programming with fractional objective function. The problem is conceived as a preliminary study of linear programming with stochastic objective function. Work partially supported by GNAFA (CNR).     

Feedback stabilization of a linear control system in Hilbert space with ana priori bounded control

This paper derives a feedback controlf(t), ‖f(t)‖_E≦r,r>0, which forces the infinite-dimensional control systemdu/dt=Au+Bf, u(0)=u _o ≠H to have the asymptotic behavioru(t)→0 ast→∞ inH. HereA is the infinitesimal generator of aC _o semigroup of contractionse ^At on a real Hilbert spaceH andB is a bounded linear operator mapping a Hilbert space of controlsE intoH. An application to the boundary feedback control of a vibrating beam is provided in detail and an application to the stabilization of the NASA Spacecraft Control Laboratory is sketched. This research was sponsored in part by the Air Force Office of Scientific Research, Air Force Systems Command, USAF Contract/Grants AFOSR 81-0172 and AFOSR 87-0315.     

Max-Stretch Reduction for Tree Spanners

A tree t-spanner T of a graph G is a spanning tree of G whose max-stretch is t, i.e., the distance between any two vertices in T is at most t times their distance in G. If G has a tree t-spanner but not a tree (t−1)-spanner, then G is said to have max-stretch of t. In this paper, we study the Max-Stretch Reduction Problem: for an unweighted graph G=(V,E), find a set of edges not in E originally whose insertion into G can decrease the max-stretch of G. Our results are as follows: (i) For a ring graph, we give a linear-time algorithm which inserts k edges improving the max-stretch optimally. (ii) For a grid graph, we give a nearly optimal max-stretch reduction algorithm which preserves the structure of the grid. (iii) In the general case, we show that it is -hard to decide, for a given graph G and its spanning tree of max-stretch t, whether or not one-edge insertion can decrease the max-stretch to t−1. (iv) Finally, we show that the max-stretch of an arbitrary graph on n vertices can be reduced to s′≥2 by inserting O(n/s′) edges, which can be determined in linear time, and observe that this number of edges is optimal up to a constant.     

Image Compression and Reconstruction Using pi<Subscript><Emphasis Type="Italic">t</Emphasis></Subscript>-Sigma Neural Networks

A high-order feedforward neural architecture, called pi _t -sigma (π _t σ) neural network, is proposed for lossy digital image compression and reconstruction problems. The π _t σ network architecture is composed of an input layer, a single hidden layer, and an output layer. The hidden layer is composed of classical additive neurons, whereas the output layer is composed of translated multiplicative neurons (π _t -neurons). A two-stage learning algorithm is proposed to adjust the parameters of the π _t σ network: first, a genetic algorithm (GA) is used to avoid premature convergence to poor local minima; in the second stage, a conjugate gradient method is used to fine-tune the solution found by GA. Experiments using the Standard Image Database and infrared satellite images show that the proposed π _t σ network performs better than classical multilayer perceptron, improving the reconstruction precision (measured by the mean squared error) in about 56%, on average.     

Sugli zeri delle funzioni di bessel

In this paper we give some inequalities for the zerosc _ν,n of the cilinder functionJ _ν (x)cosα−Y _ν (x)sindα, 0≤α<π, for the zerosc′_ν,n ofC′_ν(x) and we make an application to negative zerosa _n ,b _n of Airy functions Ai(x), Bi(x) of first and second kind respectively. We also make a comparison between inequalities related to the first zeroj _ν,1 of the Bessel functionJ _ν (x) of first kind with ν>0 and other known inequalities. Some results are new, other not, but in this case they are derived in a simpler way than usual ones.

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Lavoro eseguito nell'ambito del Gruppo Nazionale per l'Informatica Matematica del C.N.R.     

Predicting RNA Secondary Structure Using Profile Stochastic Context-Free Grammars and Phylogenic Analysis

Stochastic context-free grammars （SCFGs） have been applied to predicting RNA secondary structure. The prediction of RNA secondary structure can be facilitated by incorporating with comparative sequence analysis. However, most of existing SCFG-based methods lack explicit phylogenic analysis of homologous RNA sequences, which is probably the reason why these methods are not ideal in practical application. Hence, we present a new SCFG-based method by integrating phylogenic analysis with the newly defined profile SCFG. The method can be summarized as： 1） we define a new profile SCFG, M, to depict consensus secondary structure of multiple RNA sequence alignment; 2） we introduce two distinct hidden Markov models, λ and λ＇, to perform phylogenic analysis of homologous RNA sequences. Here, λ＇ is for non-structural regions of the sequence and λ＇ is for structural regions of the sequence; 3） we merge λ and λ＇ into M to devise a combined model for prediction of RNA secondary structure. We tested our method on data sets constructed from the Rfam database. The sensitivity and specificity of our method are more accurate than those of the predictions by Pfold.     

Optimal time byzantine agreement for t <n/8 with linear-messages

Summary.  This paper presents a Byzantine Agreement protocol with n=8t+1, optimal number of rounds (namely min{ f+2, t+1} where f is number of actual faults), and messages of linear size (namely m≦n+O(log n), where m stands for message size). All previous protocols that stop in optimal time and tolerate t=O(n) faults require messages of size at least O(n ²). The new protocol uses a novel technique called Reconstructed Traversal which is based on the Reconstruction Principle and on the Coordinated Traversal protocol. Received: August 1992/Accepted: January 1995l     

Online variance minimization

We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C ^t and try to select a parameter vector w ^t−1 such that the total variance over a sequence of trials ?_t=1^T (w^t-1)^T C^tw^t-1\sum_{t=1}^{T} (\boldsymbol {w}^{t-1})^{\top} \boldsymbol {C}^{t}\boldsymbol {w}^{t-1} is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in ℝ ⁿ are considered—the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix ?_t=1^T C^t\sum_{t=1}^{T} \boldsymbol {C}^{t}. For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu ^⊤ and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.     

<Emphasis Type="Italic">t</Emphasis>-Private and <Emphasis Type="Italic">t</Emphasis>-Secure Auctions

In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically secure protocols for electronic auctions have been presented in the last decade. Our work extends these protocols in several ways. On the basis of garbled circuits, i.e., encrypted circuits, we present protocols for sealed-bid auctions that fulfill the following requirements： 1） protocols are information-theoretically t-private for honest but curious parties; 2） the number of bits that can be learned by malicious adversaries is bounded by the output length of the auction; 3） the computational requirements for participating parties are very low： only random bit choices and bitwise computation of the XOR-function are necessary. Note that one can distinguish between the protocol that generates a garbled circuit for an auction and the protocol to evaluate the auction. In this paper we address both problems. We will present a t-private protocol for the construction of a garbled circuit that reaches the lower bound of 2t ＋ 1 parties, and Finally, we address the problem of bid changes in an auction. a more randomness efficient protocol for （t ＋ 1）^2 parties     

On the structure of binary feedforward inverses with delay 2

Let M‘=S(Mα,f)be a semi-input-memory finite automaton with input alphabet Y and output alphabet X.If X=Y={0,1},then M‘ is a feedforware inverse with delay 2 if and only if there exists a cycle c of state diagram of Mαsuch that f(y0,…,yc,λα（t)0 can be expressed in the form of f ^(1)(y0,…,yc-1,λα（t)) yc for any state t in C and y0,y1,…,yc in Y;or of f^(2)(y0,…,yc-2,λα（t)) yc-c for any state t in Cand y0,y1,…,yc in Y;or for any state t in Cand y0,y1,…yc,in Y,y0,y1…yc satisfies the D[t] condition.The socalled y0,y1…yc satisfying the D[t] condition is that:for some i,j,(i,j)∈{(1,2),(1,3),(2,1),(2,2),(3,1),(3,2)},there exists a (c 2-k)-ary function f^(k),k=1,2,3,such that the Equation(1)and Equation (2)hokl simultaneously for all y‘c-2,…,y‘c 1∈Y. Equation (1);f(y0,…,yc-i,y‘c-i 1,…y‘c,λα（t))=f^(j)(y0,…yc-i,λα（t）） y‘c-i 1 Equation (2):f(y1,…,yc-j 1,y‘c-j 2,…,y‘c 1,λα（t））=f^(j)(y1,…,yc-j 1,λα（t）） y‘c-j s where t=δα（t）and if (i,j)=(1,2)then one and only one of the following conditions C1 and C2 holds for all y‘c-1,y‘c,y‘c 1∈Y.Condition C1:there exists a c-ary function g^(1),such that f(y0,…,yc-2,y‘c-1,y‘c,λα（t））=g^(1),(y0,…,yc-2,λα（t）） y‘c-1( )y‘c;Condition C2:there exists a (c-1)-ary functiong g^(2)such that f(y1,…,yc-2,y‘c-1,y‘c,y‘c 1,λα（t））=g^(2)(y1,…,yc-2,λα（t）） y‘c-1 y‘c,where t=δα(t).     

Estimating the number of good permutations by a modified fast simulation method

A permutation (s₀, s₁,…, s_{N − 1}) of symbols 0, 1,…, N − 1 s called good if the set (t₀, t₁,…, t_{N − 1}) formed according to the rule t_i = i + s_i (mod N), i = 0, 1,…, N − 1, is a permutation too. A modified fast simulation method is proposed to evaluate the number of good permutations for N = 205 with a 5% relative error. Empirical upper and lower bounds for the number of good permutations are also given. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 101–109, July–August 2008.     

Applying fast simulation to find the number of good permutations

A permutation (s₀, s₁,…, s_{N − 1}) of symbols 0, 1,…, N − 1, is called good if the set (t₀, t₁,…, t_{N − 1}) formed according to the rule t_i = i + s_i (mod N), i = 0, 1, … N − 1, is also a permutation. A fast simulation method is proposed. It allows the number of good permutations to be evaluated with high accuracy for large N (in particular, N > 100). Empirical upper and lower bounds for the number of good permutations are presented and verified against numerical data. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 80–89, November–December 2007.     

Combining Markov-Chain Analysis and Drift Analysis

In their seminal article Droste, Jansen, and Wegener (Theor. Comput. Sci. 276:51–82, 2002) consider a basic direct-search heuristic with a global search operator, namely the so-called (1+1) Evolutionary Algorithm ((1+1) EA). They present the first theoretical analysis of the (1+1) EA’s expected runtime for the class of linear functions over the search space {0,1} ⁿ . In a rather long and involved proof they show that, for any linear function, the expected runtime is O(nlog n), i.e., that there are two constants c and n′ such that, for n≥n′, the expected number of iterations until a global optimum is generated is bounded above by c⋅nlog ₂ n. However, neither c nor n′ are specified—they would be pretty large. Here we reconsider this optimization scenario to demonstrate the potential of an analytical method that makes use of the distribution of the evolving candidate solution over the search space {0,1} ⁿ . Actually, an invariance property of this distribution is proved, which is then used to obtain a significantly improved bound on the drift, namely the expected change of a potential function, here the number of bits set correctly. Finally, this better estimate of the drift enables an upper bound on the expected number of iterations of 3.8nlog ₂ n+7.6log ₂ n for n≥2.