ФЛУОРЕСЦЕНТНАЯ МОЛЕКУЛЯРНАЯ ТОМОГРАФИЯ НА ОСНОВЕ АСИМПТОТИЧЕСКОГО ПРИБЛИЖЕНИЯ ФУНКЦИИ ИСТОЧНИКА: ТЕКУЩЕЕ СОСТОЯНИЕ И ПЕРСПЕКТИВЫ

Sensing ion or ligand concentrations, physico-chemical conditions, and molecular dimerization or conformation change is possible by assays involving fluorescent lifetime imaging. The inherent low throughput of imaging impedes rigorous statistical data analysis on large cell numbers. We address this limitation by developing a fluorescence lifetime-measuring flow cytometer for fast fluorescence lifetime quantification in living or fixed cell populations. The instrument combines a time-correlated single photon counting epifluorescent microscope with microfluidics cell-handling system. The associated computer software performs burst integrated fluorescence lifetime analysis to assign fluorescence lifetime, intensity, and burst duration to each passing cell. The maximum safe throughput of the instrument reaches 3,000 particles per minute. Living cells expressing spectroscopic rulers of varying peptide lengths were distinguishable by Förster resonant energy transfer measured by donor fluorescence lifetime. An epidermal growth factor (EGF)-stimulation assay demonstrated the technique's capacity to selectively quantify EGF receptor phosphorylation in cells, which was impossible by measuring sensitized emission on a standard flow cytometer. Dual-color fluorescence lifetime detection and cell-specific chemical environment sensing were exemplified using di-4-ANEPPDHQ, a lipophilic environmentally sensitive dye that exhibits changes in its fluorescence lifetime as a function of membrane lipid order. To our knowledge, this instrument opens new applications in flow cytometry which were unavailable due to technological limitations of previously reported fluorescent lifetime flow cytometers. The presented technique is sensitive to lifetimes of most popular fluorophores in the 0.5–5 ns range including fluorescent proteins and is capable of detecting multi-exponential fluorescence lifetime decays. This instrument vastly enhances the throughput of experiments involving fluorescence lifetime measurements, thereby providing statistically significant quantitative data for analysis of large cell populations. © 2014 International Society for Advancement of Cytometry

Pulsed Interleaved Excitation Fluctuation Imaging: Method and Application to HIV-1 Assembly

A deep learning method to separate fluorophores based on their fluorescence lifetime.

Fires are complex and it is hard to derive relationships from theory in fire science. Full-scale and small-scale experiments have been used with great success in order to increase the understanding of fire chemistry and fire dynamics. An alternative or complement to these often expensive and resource demanding traditional experiments are numerical experiments. In this paper, numerical experiments are reviewed as a research method and put into the context of traditional compartment fire experiments. Benefits and challenges with numerical experiments compared to traditional compartment fire experiments are presented and discussed in this paper. Numerical experiments are a promising method in fire science research. However, it is currently not considered satisfying to solely use a numerical experiment to study a certain fire phenomena. Different experimental methods should not be regarded as competitive but as complementary, and a combination of traditional and numerical experimental methods are in many cases appropriate in order to analysis a certain fire phenomena.

This paper presents a new method for zonal wavefront reconstruction (WFR) with application to adaptive optics systems. This new method, indicated as Spline based ABerration REconstruction (SABRE), uses bivariate simplex B-spline basis functions to reconstruct the wavefront using local wavefront slope measurements. The SABRE enables WFR on nonrectangular and partly obscured sensor grids and is not subject to the waffle mode. The performance of SABRE is compared to that of the finite difference (FD) method in numerical experiments using data from a simulated Shack-Hartmann lenslet array. The results show that SABRE offers superior reconstruction accuracy and noise rejection capabilities compared to the FD method.

<p style='text-indent:20px;'>In this paper, we study linear transport model by adopting <i>deep learning method</i>, in particular deep neural network (DNN) approach. While the interest of using DNN to study partial differential equations is arising, here we adapt it to study kinetic models, in particular the linear transport model. Moreover, theoretical analysis on the convergence of neural network and its approximated solution towards analytic solution is shown. We demonstrate the accuracy and effectiveness of the proposed DNN method in numerical experiments.</p>

In this paper, new weak second-order stochastic Runge–Kutta (SRK) methods for Ito stochastic differential equations (SDEs) with an m-dimensional Wiener process are introduced. Two new explicit SRK methods with weak order 2.0 are proposed. As the main innovation, the new explicit SRK methods have two advantages. First, only three evaluations of each diffusion coefficient are needed in per step. Second, the number of necessary random variables which have to be simulated is only \(m+2\) for each step. Compared to well-known explicit SRK methods, these good properties can be used to reduce the computational effort. Our methods are compared with other well-known explicit weak second-order SRK methods in numerical experiments. And the numerical results show that the computational efficiency of our methods is better than other methods.

The main aim of this paper is to accelerate the image decomposition model based on (BV, H ?1). It is solved with a particularly effective primal-dual gradient descent algorithm. The algorithm works on the primal-dual formulation and exploits the information of the primal and dual variables simultaneously. It converges significantly faster than some popular existing methods in numerical experiments. This approach is to some extent related to projection type methods for solving variational inequalities.

In order to explain features of a point-to-point human arm movement, several models based on a minimization principle have been proposed. For example, there are the minimum hand jerk model, the minimum angle jerk model, the minimum commanded torque change model, the minimum-variance model and so on. For some models listed above; the minimum hand jerk model, the minimum angle jerk model and the minimum commanded torque change model, these established methods to obtain an optimal trajectory which satisfies corresponding criterion have already been reported. However it is difficult to obtain the optimal solutions of the minimum-variance model and of other some models. In this study, we propose a novel method to calculate optimal trajectories without depending on the employed criterion by using genetic algorithm. The proposed method has following two procedures; first, the objective trajectory is described by the sum of polynomial equations which satisfy the boundary conditions strictly, and second, the coefficients of the polynomial expressions are determined by using genetic algorithm. In this report, we have confirmed the effectiveness of our method in numerical experiments. First, in the minimum commanded torque change model, we show that the produced trajectory is in beautiful agreement with the optimal solution. Next, in the minimum-variance model, the obtained solution by using proposed method has better evaluated values than one of the quasi-optimal solution that is obtained by the conventional Simplex method

The possibility of solving the inverse scattering problem with allowance for the effect of multiple scattering between the scatterers by the least-squares method that minimizes the solution’s residual is considered. Since the problem is ill-posed, Tikhonov’s regularization is efficient with the entropy of the estimate of the measured field taken as the stabilizing functional, which is the essence of the maximum entropy method. The method is implemented in its multifrequency version. Results of the application of the maximum entropy method in numerical experiments are provided with images reconstructed in different frequency ranges in the presence of white noise.

Fluorescent molecular thermometers showing temperature-dependent fluorescence lifetimes enable thermal mapping of small spaces such as a microchannel and a living cell. We report the temperature-dependent fluorescence lifetimes of poly(NIPAM-co-DBD-AA), which is a random copolymer of N-isopropylacrylamide (NIPAM) and an environment-sensitive fluorescent monomer (DBD-AA) containing a 4-sulfamoyl-7-aminobenzofurazan structure. The average fluorescence lifetime of poly(NIPAM-co-DBD-AA) in aqueous solution increased from 4.22 to 14.1 ns with increasing temperature from 30 to 35 degrees C. This drastic change in fluorescence lifetime (27% increase per 1 degrees C) is the sharpest ever reported. Concentration independency, one of the advantages of fluorescence lifetime measurements, was seen in average fluorescence lifetime (13.7 +/- 0.18 ns) of poly(NIPAM-co-DBD-AA) at 33 degrees C over a wide concentration range (0.005-1 w/v%). With increasing temperature, polyNIPAM units in poly(NIPAM-co-DBD-AA) change their structure from an extended form to a globular form, providing apolar and aprotic environments to the fluorescent DBD-AA units. Consequently, the environment-sensitive DBD-AA units translate the local environmental changes into the extension of the fluorescence lifetime. This role of the DBD-AA units was revealed by a study of solvent effects on fluorescence lifetime of a model environment-sensitive fluorophore.

This paper presents a monotonicity-based spatiotemporal conductivity imaging method for continuous regional lung monitoring using electrical impedance tomography (EIT). The EIT data (i.e. the boundary current–voltage data) can be decomposed into pulmonary, cardiac and other parts using their different periodic natures. The time-differential current–voltage operator corresponding to the lung ventilation can be viewed as either semi-positive or semi-negative definite owing to monotonic conductivity changes within the lung regions. We used these monotonicity constraints to improve the quality of lung EIT imaging. We tested the proposed methods in numerical simulations, phantom experiments and human experiments.

A third order conservative reconstruction, in the context of finite volume schemes for hyperbolic conservation laws, is constructed based on logarithmic functions. This logarithmic method reconstructs without the use of a limiter, any preprocessing of input data, special treatments for local extrema, or shock solutions. Also the method is local in the sense that data from only the nearest neighbors are required. We test the new reconstruction method in several numerical experiments, including nonlinear systems in one and two space dimensions.

We present a solution method for solving electromagnetic problems in three dimensions in parameter regimes where the quasi-static approximation applies and the permeability is constant. Firstly, by using a potential formulation with a Coulomb gauge, we circumvent the ill-posed problem in regions of vanishing conductivity, obtaining an elliptic, weakly coupled system of differential equations. The system thus derived is strongly elliptic, which leads to reliable discretizations. Secondly, we derive a robust finite-volume discretization. Thirdly, we solve the resulting large, sparse algebraic systems using preconditioned Krylov-space methods. A particularly efficient algorithm results from the combination of BICGSTAB and a block preconditioner using an incomplete LU-decomposition of the dominant system blocks only. We demonstrate the efficacy of our method in several numerical experiments.

Levy-Driven Libor Model and its Numerical Approximation