Direct visualization of the dynamics of membrane-anchor proteins in living cells

Dynamic properties of proteins have crucial roles in understanding protein function and molecular mechanism within cells. In this paper, we combined total internal reflection fluorescence microscopy with oblique illumination fluorescence microscopy to observe directly the movement and localization of membrane-anchored green fluorescence proteins in living cells. Total internal reflect illumination allowed the observation of proteins in the cell membrane of living cells since the penetrate depth could be adjusted to about 80 nm, and oblique illumination allowed the observation of proteins both in the cytoplasm and apical membrane, which made this combination a promising tool to investigate the dynamics of proteins through the whole cell. Not only individual protein molecule tracks have been analyzed quantitatively but also cumulative probability distribution function analysis of ensemble trajectories has been done to reveal the mobility of proteins. Finally, single particle tracking has acted as a compensation for single molecule tracking. All the results exhibited green fluorescence protein dynamics within cytoplasm, on the membrane and from cytoplasm to plasma membrane.

Investigation of air-core vortex at hydraulic intakes

Hydrodynamic properties of the surface vortex have been investigated. Based on the Navier-Stokes equations, three sets of the new formulations for the tangential velocity distributions are derived, and verified against the experimental measurements in the literature. It is shown that one modification greatly improves the agreement with the experimental data. Physical model experiments were carried out to study the intake vortex related to the Xiluodu hydropower project. The velocity fields were measured using the Particle Tracking Velocimetry (PTV) technique. The proposed equation for tangential velocity distribution is applied to the Xiluodu project with the solid boundary being considered by the method of images. Good agreement has been observed between the formula prediction and the experimental observation. © 2010 Publishing House for Journal of Hydrodynamics.

Differential Dynamic Microscopy: a High-Throughput Method for Characterizing the Motility of Microorganism

We present a fast, high-throughput method for characterizing the motility of microorganisms in 3D based on standard imaging microscopy. Instead of tracking individual cells, we analyse the spatio-temporal fluctuations of the intensity in the sample from time-lapse images and obtain the intermediate scattering function (ISF) of the system. We demonstrate our method on two different types of microorganisms: bacteria, both smooth swimming (run only) and wild type (run and tumble) Escherichia coli, and the bi-flagellate alga Chlamydomonas reinhardtii. We validate the methodology using computer simulations and particle tracking. From the ISF, we are able to extract (i) for E. coli: the swimming speed distribution, the fraction of motile cells and the diffusivity, and (ii) for C. reinhardtii: the swimming speed distribution, the amplitude and frequency of the oscillatory dynamics. In both cases, the motility parameters are averaged over \approx 10^4 cells and obtained in a few minutes.

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

基于神经网络的机器人视觉伺服控制

视觉伺服可以应用于机器人初始定位自动导引、自动避障、轨线跟踪和运动目标跟踪等控制系统中。传统的视觉伺服系统在运行时包括工作空间定位和动力学逆运算两个过程,需要实时计算视觉雅可比矩阵和机器人逆雅可比矩阵,计算量大,系统结构复杂。本文分析了基于图像的机器人视觉伺服的基本原理,使用BP神经网络来确定达到指定位姿所需要的关节角度,将视觉信息直接融入伺服过程,在保证伺服精度的情况下大大简化了控制算法。文中针对Puma560工业机器人的模型进行了仿真实验,结果验证了该方法的有效性。

基于主动视觉和超声的机器人目标跟踪系统

运动目标跟踪技术是未知环境下移动机器人研究领域的一个重要研究方向。该文提出了一种基于主动视觉和超声信息的移动机器人运动目标跟踪设计方法,利用一台SONY EV-D31彩色摄像机、自主研制的摄像机控制模块、图像采集与处理单元等构建了主动视觉系统。移动机器人采用了基于行为的分布式控制体系结构,利用主动视觉锁定运动目标,通过超声系统感知外部环境信息,能在未知的、动态的、非结构化复杂环境中可靠地跟踪运动目标。实验表明机器人具有较高的鲁棒性,运动目标跟踪系统运行可靠。

移动机器人运动目标跟踪系统设计

利用SONYEV-D31摄像机和自主研发的摄像机控制模块,构建了一套主动视觉子系统,并将该子系统应用于RIRA-II型移动机器人上,实现了移动机器人运动目标自动跟踪功能。RIRA-II移动机器人采用了由一组分布式行为模块和集中命令仲裁器组成的基于行为的分布式控制体系结构。各行为模块基于领域知识通过反应方式产生投票,由仲裁器产生动作指令,机器人完成相应的动作。在设置了障碍、窄通道以及模拟墙体的复杂环境下进行运动目标跟踪实验,实验表明运动目标跟踪系统运行可靠,具有较高的鲁棒性。

基于多微处理器的智能超声探测系统

设计了一种基于多微处理器的智能超声探测系统。该系统采用由上位工作模式控制单元和下位智能超声传感器阵列组成的分布式结构。每个超声传感器均由独立的微处理器控制,并完成实时数据处理、抗干扰处理、故障报警以及数据通信等功能。上位控制单元根据移动机器人的运动状态采用不同的控制策略,使下位传感器阵列分组并行工作,提高了探测系统的实时性;下位传感器采用"阈值比较法"、"改进型递推均值滤波"算法和模糊信息处理技术,提高了探测系统的准确性、可靠性。将该探测系统安装于RIRA-II移动机器人上,进行了基于主动视觉和超声信息的运动目标跟踪实验,实验表明超声探测系统运行可靠、稳定。

基于栅格地图的多化学羽流源定位

本文研究了移动机器人在湍流环境中定位多个化学羽流源的问题。利用粒子随机行走方法建立动态羽流模型。在此环境中机器人进行梳状搜索并采集羽流数据,使用一种基于后验概率独立假设的改进贝叶斯算法融合这些传感器数据建立一张羽流源位置的概率地图,地图中具有高概率值的栅格指出了羽流源可能的位置。仿真结果说明了该方法的有效性,通过与标准贝叶斯算法的比较说明了该方法优点。

Three-phase computations of the continuous casting process

In this paper, the continuous casting process for steel slab production is modelled using a mult-physics approach. For this purpose, a Finite Volume (FV) numerical model was constructed in 3D, with the following characteristics: Time dependent, turbulent fluid flow and heat transfer in the molten steel and flux regions, solidification of the skin layer, under prescribed heat loss boundary conditions, particle tracking simulation of argon bubbles injected with the metal into the mould, full coupling between bubbles and liquid through buoyancy and interfacial forces using a novel gas accumulation technique, and a full transient simulation of flux-metal interface behaviour under the influence of gravity and fluid inertial forces and bubble plume buoyancy. The unstructure mesh FV code PHYSICA developed at Greenwich was used for carry out the simulations with physical process data and properties supplied by IRSID SA.

Dynamic model for metal cleanness evaluation by melting in a cold crucible

Melting of metallic samples in a cold crucible causes inclusions to concentrate on the surface owing to the action of the electromagnetic force in the skin layer. This process is dynamic, involving the melting stage, then quasi-stationary particle separation, and finally the solidification in the cold crucible. The proposed modeling technique is based on the pseudospectral solution method for coupled turbulent fluid flow, thermal and electromagnetic fields within the time varying fluid volume contained by the free surface, and partially the solid crucible wall. The model uses two methods for particle tracking: (1) a direct Lagrangian particle path computation and (2) a drifting concentration model. Lagrangian tracking is implemented for arbitrary unsteady flow. A specific numerical time integration scheme is implemented using implicit advancement that permits relatively large time-steps in the Lagrangian model. The drifting concentration model is based on a local equilibrium drift velocity assumption. Both methods are compared and demonstrated to give qualitatively similar results for stationary flow situations. The particular results presented are obtained for iron alloys. Small size particles of the order of 1 μm are shown to be less prone to separation by electromagnetic field action. In contrast, larger particles, 10 to 100 μm, are easily “trapped” by the electromagnetic field and stay on the sample surface at predetermined locations depending on their size and properties. The model allows optimization for melting power, geometry, and solidification rate.

Understanding macroalgal dispersal in a complex hydrodynamic environment: a combined population genetic and physical modelling approach

Gene flow in macroalgal populations can be strongly influenced by spore or gamete dispersal. This, in turn, is influenced by a convolution of the effects of current flow and specific plant reproductive strategies. Although several studies have demonstrated genetic variability in macroalgal populations over a wide range of spatial scales, the associated current data have generally been poorly resolved spatially and temporally. In this study, we used a combination of population genetic analyses and high-resolution hydrodynamic modelling to investigate potential connectivity between populations of the kelp Laminaria digitata in the Strangford Narrows, a narrow channel characterized by strong currents linking the large semi-enclosed sea lough, Strangford Lough, to the Irish Sea. Levels of genetic structuring based on six microsatellite markers were very low, indicating high levels of gene flow and a pattern of isolation-by-distance, where populations are more likely to exchange migrants with geographically proximal populations, but with occasional long-distance dispersal. This was confirmed by the particle tracking model, which showed that, while the majority of spores settle near the release site, there is potential for dispersal over several kilometres. This combined population genetic and modelling approach suggests that the complex hydrodynamic environment at the entrance to Strangford Lough can facilitate dispersal on a scale exceeding that proposed for L. digitata in particular, and the majority of macroalgae in general. The study demonstrates the potential of integrated physical–biological approaches for the prediction of ecological changes resulting from factors such as anthropogenically induced coastal zone changes.

Lagrangian transport of particles in Ria de Aveiro lagoon, Portugal

A two-dimensional vertically integrated hydrodynamic model coupled to a particle tracking model is applied to study the dispersion processes and residence time in Ria de Aveiro lagoon (Portugal). The only dispersion process that is considered in this study is the advection, according to the main characteristics of the local hydrodynamic. The particle tracking model computes the particles position at each time step, using a fourth-order Runge-Kutta integration method. The dispersion of passive particles released along the lagoon and in critical areas are studied in this work. The residence time is also determined for the entire lagoon. The results show that the mixture between particles coming from different channels of the lagoon is negligible in a time scale higher than 2 tidal cycles. The residence time for the lagoon central area is about 2 days, revealing a strong marine influence in this area. At the upper reaches of the channels were found values higher than 2 weeks.

TNF-α influences the lateral dynamics of TNF receptor I in living cells

In mammalian cells, inflammation is mainly mediated by the binding of tumor necrosis factor alpha to tumor necrosis factor receptor 1. In this study, we investigated lateral dynamics of TNF-R1 before and after ligand binding using high-density single-particle tracking in combination with photoactivated localization microscopy. Our single-molecule data indicates the presence of tumor necrosis factor receptor 1 with different mobilities in the plasma membrane, suggesting different molecular organizations. Cholesterol depletion led to a decrease of slow receptor species and a strong increase in the average diffusion coefficient. Moreover, as a consequence of tumor necrosis factor-alpha treatment, the mean diffusion coefficient moderately increased while its distribution narrowed. Based on our observation, we propose a refined mechanism on the structural arrangement and activation of tumor necrosis factor receptor 1 in the plasma membrane.

Caracterização de microorganismos aquáticos por processamento digital de imagens e redes neurais artificiais

A identificação e o monitoramento de microorganismos aquáticos, como bactérias e microalgas, tem sido uma tarefa árdua e morosa. Técnicas convencionais, com uso de microscópios e corantes, são complexas, exigindo um grande esforço por parte dos técnicos e pesquisadores. Uma das maiores dificuldades nos processos convencionais de identificação via microscopia é o elevado número de diferentes espécies e variantes existentes nos ambientes aquáticos, muitas com semelhança de forma e textura. O presente trabalho tem por objetivo o desenvolvimento de uma metodologia para a caracterização e classificação de microorganismos aquáticos (bactérias e microalgas), bem como a determinação de características cinemáticas, através do estudo da mobilidade de microalgas que possuem estruturas que permitem a natação (flagelos). Para caracterização e reconhecimento de padrões as metodologias empregadas foram: o processamento digital de imagens e redes neurais artificiais (RNA). Para a determinação da mobilidade dos microorganismos foram empregadas técnicas de velocimetria por processamento de imagens de partículas em movimento (Particle Tracking Velocimetry - PTV). O trabalho está dividido em duas partes: 1) caracterização e contagem de microalgas e bactérias aquáticas em amostras e 2) medição da velocidade de movimentação das microalgas em lâminas de microscópio. A primeira parte envolve a aquisição e processamento digital de imagens de microalgas, a partir de um microscópio ótico, sua caracterização e determinação da densidade de cada espécie contida em amostras. Por meio de um microscópio epifluorescente, foi possível, ainda, acompanhar o crescimento de bactérias aquáticas e efetuar a sua medição por operadores morfológicos. A segunda parte constitui-se na medição da velocidade de movimentação de microalgas, cujo parâmetro pode ser utilizado como um indicador para se avaliar o efeito de substâncias tóxicas ou fatores de estresse sobre as microalgas. O trabalho em desenvolvimento contribuirá para o projeto "Produção do Camarão Marinho Penaeus Paulensis no Sul do Brasil: Cultivo em estruturas Alternativas" em andamento na Estação Marinha de Aquacultura - EMA e para pesquisas no Laboratório de Ecologia do Fitoplâncton e de Microorganismos Marinhos do Departamento de Oceanografia da FURG. O trabalho propõe a utilização dos níveis de intensidade da imagem em padrão RGB e oito grandezas geométricas como características para reconhecimento de padrões das microalgas O conjunto proposto de características das microalgas, do ponto de vista de grandezas geométricas e da cor (nível de intensidade da imagem e transformadas Fourier e Radon), levou à geração de indicadores que permitiram o reconhecimento de padrões. As redes neurais artificiais desenvolvidas com topologia de rede multinível totalmente conectada, supervisionada, e com algoritmo de retropropagação, atingiram as metas de erro máximo estipuladas entre os neurônios de saída desejados e os obtidos, permitindo a caracterização das microalgas.