Anomalous diffusion : from life to machines

Diffusion refers to numerous phenomena, by which particles and bodies of all kinds move throughout any kind of material, has emerged as one of the most prominent subjects in the study of complex systems. Motivated by the recent developments in experimental techniques, the field had an important burst in theoretical research, particularly in the study of the motion of particles in biological environments. Just with the information retrieved from the trajectories of particles we are now able to characterize many properties of the system with astonishing accuracy. For instance, when Einstein introduced the diffusion theory back in 1905, he used the motion of microscopic particles to calculate the size of the atoms of the liquid these were suspended. Initially, most of the experimental evidence showed that such systems follow Brownian-like dynamics, i.e. the homogeneous interaction between the particles and the environment led to its stochastic, but uncorrelated motion. However, we know now that such a simple explanation lacks crucial phenomena that have been shown to arise in a plethora of physical systems. The divergence from Brownian dynamics led to the theory of anomalous diffusion, in which the particles are affected in a way or another by their interactions with the environment such that their diffusion changes drastically. For instance features such as ergodicity, Gaussianity, or ageing are now crucial for in the understanding of diffusion processes, well beyond Brownian motion. In theoretical terms, anomalous diffusion has a well-developed framework, able to explain most of the current experimental observations. However, it has been usually focused in describing the systems in terms of its macroscopic behaviour. This means that the processes are described by means of general models, able to predict the average or collective features. Even though such an approach leads to a correct description of the system and hints on the actual underlying phenomena, it lacks the understanding of the particular microscopic interactions leading to anomalous diffusion. The work presented in this Thesis has two main goals. First, we will explore how one may use microscopical (or phenomenological) models to understand anomalous diffusion. By microscopical model we refer to a model in which we will set exactly how the interactions between the various components of a system are. Then, we will explore how these interactions may be tuned in order to recover and control anomalous diffusion and how its features depend on the properties of the system. We will explore crucial topics arising in recent experimental observations, such as weak-ergodicity breaking or liquid-liquid phase separation. Second, we will survey the topic of trajectory characterization. Even if our theories are extremely well developed, without an accurate tool for studying the trajectories observed in experiments, we will be unable to correctly make any faithful prediction. In particular, we will introduce one of the first machine learning techniques that can be used for such purpose, even in systems where previous techniques failed largely.La difusión es el fenómeno por el cual partículas de todas formas y tamaños se mueven a través del entorno que les rodea. Su estudio se ha convertido en una potente herramienta para entender el comportamiento de sistemas complejos. Gracias al reciente desarrollo de diferentes técnicas experimentales, este fenómeno ha generado un enorme interés tanto desde el punto de vista experimental como del teórico, y en especial,en el estudio del movimiento de partículas microscópicas en entornos biológicos. Mediante el análisis de las trayectorias de estas partículas, no solo somos capaces de caracterizar sus propiedades, sino también las de su entorno. El propio Albert Einstein, autor junto con Marian Smoluchowski de la teoría de la difusión, demostró que era posible calcular el radio de los átomos de un líquido simplemente mediante el análisis del movimiento de una partícula suspendida en este. Esta teoría, que dio origen a lo que hoy conocemos como movimiento Browniano, consideraba que la interacción homogénea de una partícula con su entorno provocaba el movimiento aleatorio de esta última. Aunque el movimiento Browniano haya sido utilizado para describir una enorme cantidad de experimentos, hoy sabemos que existen sistemas particulares que se desvían de sus predicciones. Esta divergencia ha dado pie al desarrollo de la teoría de la difusión anómala, en la que, debido a las propiedades de las partículas y sus entornos, la difusión difiere drásticamente de las predicciones de la teoría Browniana. Algunos fenómenos como la ergodicidad, Gausianidad o el envejecimiento de difusión, particulares de la difusión anómala, son hoy en día cruciales para entender el movimiento de partículas en sistemas complejos. En términos teóricos, la difusión anómala tiene unas bases firmes, con las cuáles se explica gran parte de las observaciones experimentales más recientes. Esta teoría, sin embargo, suele centrarse en la descripción de la difusión desde un punto de vista macroscópico. Esto quiere decir: analizar un sistema mediante modelos generales, capaces de predecir propiedades colectivas o globales. Aunque las teorías macroscópicas consiguen describir correctamente la mayoría de los procesos de difusión, no tienen la capacidad de discernir qué tipo de interacciones dan lugar a la difusión anómala. El trabajo presentado en esta tesis tiene dos objetivos principales. El primero es explorar el uso de modelos microscópicos (o fenomenológicos) para entender la difusión anómala. Un modelo microscópico, en contraposición al macroscópico, describe el sistema a partir de sus propiedades específicas. En este caso, a partir del tipo de interacciones que existen entre las partículas y su entorno. El objetivo es por lo tanto entender cuáles de estas interacciones producen difusión anómala. Además, caracterizaremos los parámetros macroscópicos de la difusión, como el exponente anómalo, y mostraremos como depende de las propiedades del sistema. En el camino, exploraremos cómo fenómenos como la rotura débil de la ergodicidad (weak-ergodicity breaking) o la separación de fase aparecen en sistemas con interacciones complejas. El segundo objetivo consiste en el desarrollo de técnicas para la caracterización de trayectorias provenientes de procesos de difusión. Aunque nuestro entendimiento teórico llegue a niveles insospechados en los próximos años, sin un análisis correcto y preciso de las trayectorias experimentales, jamás podremos construir un puente entre teoría y experimentos. Por tanto, el desarrollo de técnicas con las que analizar con la mayor precisión posible dichas trayectorias es un problema igual de importante que el desarrollo teórico de la difusión. En este trabajo, estudiaremos cómo las técnicas de aprendizaje automático (Machine Learning) pueden ser utilizadas para caracterizar dichas trayectorias, llegando a niveles de precisión y análisis muy por encim

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

First published: April 2016This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Cell theory has officially reached 350 years of age as the first use of the word ‘cell’ in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication “Micrographia: or some physiological definitions of minute bodies”. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the sub-cellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more inter-disciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking

Development of an integrated opto-electric biosensor to dynamically examine cytometric proliferation and cytotoxicity

My doctoral research has focused on the development of microscale optical techniques for examining micro/bio fluidics. Preliminary work measured the velocity field in a microchannel, by optical slicing, using Confocal Laser Scanning Microscopy (CLSM). Next, Optical Serial Sectioning Microscopy (OSSM) was applied to examine thermometry by detecting the free Brownian motion of nano-particles suspended in mediums at different temperatures. An extension of this work used objective-based Total Internal Reflection Fluorescence Microscopy (TIRFM) to examine the hindered Brownian motion of nano-particles that were very close to a solid surface (within 1 mm). An optically transparent and electrically conductive Indium Tin Oxide (ITO) biosensor and an integrated dynamic live cell imaging system were developed to dynamically examine changes in cell coverage area, cell morphology, cell-substrate adhesion, and cell-cell interaction. To our knowledge this is the first sensor capable of conducting simultaneous optical and electrical measurements. This system consists of an incubator, which keeps cells viable by providing the necessary environmental conditions (37 °C temperature and 5 % CO2), and multiple microscopy techniques, including multispectrum Interference Reflection Microscopy (MS-IRM), TIRFM, Epi-fluorescence Microscopy, Phase Contrast Microscopy (PCM), and Differential Interference Contrast Microscopy (DICM). Along with investigations of cytometric proliferation including cellular barrier functions, in vitro cytotoxicity experiments were also conducted to examine the effect of a drug (cytochalasin D, a toxic agent) on cellular motility and cellular morphology. These cytotoxicity results give us a fundamental understanding of the cellular processes induced by the drug, which will be invaluable in the search for methods of preventing metastases. In this research, MS-IRM is used to examine the focal contacts and the gap morphology between cells and substrates, DICM is used to examine the coverage area of cells, and impedance measurements are used to correlate these two parameters. Advances in the understanding of vascular bio-transport in endothelial cells will have an impact on many aspects of cell biology, tissue engineering, and pharmacology. Particularly important will be the ability to test the popular hypothesis that the cell barrier function is regulated by specific cytoskeleton elements controlling intercellular and extracellular coupling

Optical characterisation of subwavelength dielectric particles using particle tracking beyond the Stokes-Einstein relation

As the importance of nanoparticles continues to increase in both biology and industrial processes, so does the need for accurate and versatile characterisation methods. However, most light-based methods to quantify size and refractive index of individual particles are either limited to snapshot observations, particles larger than the wavelength of light, non-dynamic particle properties, or assuming the hydrodynamic boundary conditions without experimental evaluation. The aim of this thesis is to partially overcome these limitations by further developing two different characterisation methods based on optical microscopy combined with particle tracking, where the analysis goes beyond the ordinary Stokes-Einstein relation. The first method combines off-axis holographic nanoparticle tracking with deep learning (Paper I). By utilizing the optical signal, both size and refractive index of individual particles with a minimum size of R=150 nm were accurately determined using only five particle observations. The method was evaluated using particles of different sizes, refractive indices, surrounding media as well as for polystyrene nanoparticle clusters, for which reversible fluctuations of the number of monomers could be resolved while the fractal dimension remained constant. The second method is based particles tethered to a laterally fluid supported lipid bilayer and quantification of their diffusivity and flow-induced motion (Paper II). By separating the friction contributions from the tethers and the particle, simultaneous measurement of size and diffusivity enabled a comparison with theory using partial slip as a fitting parameter. This was used to quantify the slip length for different lipid vesicles, and to clarify the size-dependent mechanistic aspects concerning the mobility of membrane-attached nanoparticles

Image Analysis Algorithms for Single-Cell Study in Systems Biology

With the contiguous shift of biology from a qualitative toward a quantitative field of research, digital microscopy and image-based measurements are drawing increased interest. Several methods have been developed for acquiring images of cells and intracellular organelles. Traditionally, acquired images are analyzed manually through visual inspection. The increasing volume of data is challenging the scope of manual analysis, and there is a need to develop methods for automated analysis. This thesis examines the development and application of computational methods for acquisition and analysis of images from single-cell assays. The thesis proceeds with three different aspects.First, a study evaluates several methods for focusing microscopes and proposes a novel strategy to perform focusing in time-lapse imaging. The method relies on the nature of the focus-drift and its predictability. The study shows that focus-drift is a dynamical system with a small randomness. Therefore, a prediction-based method is employed to track the focus-drift overtime. A prototype implementation of the proposed method is created by extending the Nikon EZ-C1 Version 3.30 (Tokyo, Japan) imaging platform for acquiring images with a Nikon Eclipse (TE2000-U, Nikon, Japan) microscope.Second, a novel method is formulated to segment individual cells from a dense cluster. The method incorporates multi-resolution analysis with maximum-likelihood estimation (MAMLE) for cell detection. The MAMLE performs cell segmentation in two phases. The initial phase relies on a cutting-edge filter, edge detection in multi-resolution with a morphological operator, and threshold decomposition for adaptive thresholding. It estimates morphological features from the initial results. In the next phase, the final segmentation is constructed by boosting the initial results with the estimated parameters. The MAMLE method is evaluated with de novo data sets as well as with benchmark data from public databases. An empirical evaluation of the MAMLE method confirms its accuracy.Third, a comparative study is carried out on performance evaluation of state-ofthe-art methods for the detection of subcellular organelles. This study includes eleven algorithms developed in different fields for segmentation. The evaluation procedure encompasses a broad set of samples, ranging from benchmark data to synthetic images. The result from this study suggests that there is no particular method which performs superior to others in the test samples. Next, the effect of tetracycline on transcription dynamics of tetA promoter in Escherichia coli (E. coli ) cells is studied. This study measures expressions of RNA by tagging the MS2d-GFP vector with a target gene. The RNAs are observed as intracellular spots in confocal images. The kernel density estimation (KDE) method for detecting the intracellular spots is employed to quantify the individual RNA molecules.The thesis summarizes the results from five publications. Most of the publications are associated with different methods for imaging and analysis of microscopy. Confocal images with E. coli cells are targeted as the primary area of application. However, potential applications beyond the primary target are also made evident. The findings of the research are confirmed empirically

Bridging Single-Particle Characterisation Gaps of Optical Microscopy in the Nano-Submicron Regime

As the practical importance of particles in the nano-submicron size regime continues to increase in both biomedical applications and industrial processes, so does the need for accurate and versatile characterisation methods. Optical scattering microscopy methods are commonly used for single-particle characterisation as they provide quick measurements at physiologically relevant conditions with detection limits reaching down to individual biomolecules. However, quantitative particle characterisation using optical microscopy often rely on assumptions about the surrounding media and theparticle, including solution viscosity, boundary conditions, as well as particle shape and material. Since these assumptions are difficult to evaluate, particle characterisation beyond hydrodynamic radius and/or mass remains challenging.The aim of this thesis is to contribute to bridging the gaps that limit quantitative optical microscopy-based characterisation of individual particles in the nano-submicron regime by both developing new and improving existing microscopy methods. Specifically, in Paper I a method was developed to evaluate the relation between diffusivity and particle size to enable measurements of the hydrodynamic boundary condition. Papers II-V are based around the development of holographic nanoparticle tracking (H-NTA) and extensions thereof, with the intent of using the complex-valued optical field for material sensitive particle characterisation with minimal dependence on the surrounding media. In Paper II, H-NTA by itself was used to characterise suspensions containing nanobubbles and molecular aggregates. In Paper III, the combination of H-NTA with deep learning was used to achieve simultaneous quantification of size and refractive index directly from single microscopy images, which allowed detection of reversible fluctuations in nanoparticle aggregates. In Paper IV, H-NTA augmented with a low frequency attenuation filter, coined twilight holography, was used to investigate the interaction between herpes viruses and functionalised gold nanoparticles in terms of size, bound gold mass, and virus refractive index. In Paper V, the combination of twilight holography and interferometric scattering microscopy (iSCAT) was used to quantify both size and polarizability of individual nanoparticles without the need of detailed knowledge about the surrounding media. Taken together, the presented results in this thesis provide both new insights into heterogenous nanoparticle systems and contributes to narrowing the gap for detailed optical particle characterisation

Advanced Fluorescence Microscopy Techniques-FRAP, FLIP, FLAP, FRET and FLIM

Fluorescence microscopy provides an efficient and unique approach to study fixed and living cells because of its versatility, specificity, and high sensitivity. Fluorescence microscopes can both detect the fluorescence emitted from labeled molecules in biological samples as images or photometric data from which intensities and emission spectra can be deduced. By exploiting the characteristics of fluorescence, various techniques have been developed that enable the visualization and analysis of complex dynamic events in cells, organelles, and sub-organelle components within the biological specimen. The techniques described here are fluorescence recovery after photobleaching (FRAP), the related fluorescence loss in photobleaching (FLIP), fluorescence localization after photobleaching (FLAP), Forster or fluorescence resonance energy transfer (FRET) and the different ways how to measure FRET, such as acceptor bleaching, sensitized emission, polarization anisotropy, and fluorescence lifetime imaging microscopy (FLIM). First, a brief introduction into the mechanisms underlying fluorescence as a physical phenomenon and fluorescence, confocal, and multiphoton microscopy is given. Subsequently, these advanced microscopy techniques are introduced in more detail, with a description of how these techniques are performed, what needs to be considered, and what practical advantages they can bring to cell biological research

Manual and automatic image analysis segmentation methods for blood flow studies in microchannels

In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.This project has been funded by Portuguese national funds of FCT/MCTES (PIDDAC) through the base funding from the following research units: UIDB/00532/2020 (Transport Phenomena Research Center—CEFT), UIDB/04077/2020 (Mechanical Engineering and Resource Sustainability Center—MEtRICs), UIDB/00690/2020 (CIMO). The authors are also grateful for the partial funding of FCT through the projects, NORTE-01-0145-FEDER-029394 (PTDC/EMD-EMD/29394/2017) and NORTE-01-0145-FEDER-030171 (PTDC/EMD-EMD/30171/2017) funded by COMPETE2020, NORTE2020, PORTUGAL2020 and FEDER. D. Bento acknowledges the PhD scholarship SFRH/BD/ 91192/2012 granted by FCT

Development of Computer Aided Technique for the Identification of Selected Herbal Drugs

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