Elastic interactions of active cells with soft materials

Anchorage-dependent cells collect information on the mechanical properties of the environment through their contractile machineries and use this information to position and orient themselves. Since the probing process is anisotropic, cellular force patterns during active mechanosensing can be modelled as anisotropic force contraction dipoles. Their build-up depends on the mechanical properties of the environment, including elastic rigidity and prestrain. In a finite sized sample, it also depends on sample geometry and boundary conditions through image strain fields. We discuss the interactions of active cells with an elastic environment and compare it to the case of physical force dipoles. Despite marked differences, both cases can be described in the same theoretical framework. We exactly solve the elastic equations for anisotropic force contraction dipoles in different geometries (full space, halfspace and sphere) and with different boundary conditions. These results are then used to predict optimal position and orientation of mechanosensing cells in soft material.Comment: Revtex, 38 pages, 8 Postscript files included; revised version, accepted for publication in Phys. Rev.

Development and Construction of a new Photoelectron Imaging Spectrometer for Studying the Spectroscopy and Ultrafast Dynamics of Molecular Anions

We present a detailed account of the development, construction, and commissioning of a new experiment for studying the spectroscopy and ultrafast dynamics of molecular anions in the gas phase. The new instrument incorporates: an electrospray ionisation source, which is capable of generating a vast class of molecular anions; a Wiley-McLaren time-of-flight mass spectrometer; and a compact photoelectron imaging arrangement for anions, which negates the use of pulsed high voltages. We use this instrument in conjunction with a femtosecond laser system to perform the first ultrafast time-resolved photoelectron imaging experiments on molecular anions generated through electrospray ionisation. A method for reconstructing three dimensional charged particle distributions from their associated two dimensional projections on an imaging detector plane is described. This new method utilises: (1) onion-peeling in polar co-ordinates (POP) to perform the reconstruction; and (2) basis set concepts to significantly enhance the algorithms computational speed. We compare this new POP algorithm with other reconstruction algorithms, which shows that the method is as good as the benchmark pBASEX method in terms of accuracy. Importantly, we show that it is also computationally fast, allowing images to be reconstructed as they are acquired in a typical imaging experiment. Original work is presented which investigates the spectroscopy and ultrafast excited dynamics of the 7,7,8,8-tetracyanoquinodimethane (TCNQ) radical anion. The photoelectron spectrum of TCNQ– is measured at 3.1 eV, which is used to gain insight into the electronic structure and geometries of both the anion and neutral states. Time-resolved photoelectron imaging experiments explore the relaxation dynamics of its first excited 1 2B3u state, which we show undergoes internal conversion back to the 2B2g ground state on a timescale of 650 fs. Results also provide evidence of a wave packet motion on the excited state, which exhibits a characteristic frequency of 30 cm–1. Finally, we describe, for the first time, a formulism which allows ultrafast relaxation timescales to be extracted from the photoelectron angular distributions of isoenergetic photoelectron features. As an example, we use the time-resolved photoelectron angular distributions of a nearly isoenergetic feature in the photoelectron images of TCNQ–. From this model we extract a relaxation time for the 1 2B3u state, which quantitatively agrees with those extracted from fits to the features in the photoelectron spectra derived from the images

Selected aspects of complex, hypercomplex and fuzzy neural networks

This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.Comment: 46 page

Increased Dimensionality of Raman Cooling in a Slightly Nonorthogonal Optical Lattice

We experimentally study the effect of a slight nonorthogonality in a two-dimensional optical lattice onto resolved-sideband Raman cooling. We find that when the trap frequencies of the two lattice directions are equal, the trap frequencies of the combined potential exhibit an avoided crossing and the corresponding eigenmodes are rotated by 45 degrees relative to the lattice beams. Hence, tuning the trap frequencies makes it possible to rotate the eigenmodes such that both eigenmodes have a large projection onto any desired direction in the lattice plane, in particular, onto the direction along which Raman cooling works. Using this, we achieve two-dimensional Raman ground-state cooling in a geometry where this would be impossible, if the eigenmodes were not rotated. Our experiment is performed with a single atom inside an optical resonator but this is inessential and the scheme is expected to work equally well in other situations

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Intelligent computer vision processing techniques for fall detection in enclosed environments

Detecting unusual movement (falls) for elderly people in enclosed environments is receiving increasing attention and is likely to have massive potential social and economic impact. In this thesis, new intelligent computer vision processing based techniques are proposed to detect falls in indoor environments for senior citizens living independently, such as in intelligent homes. Different types of features extracted from video-camera recordings are exploited together with both background subtraction analysis and machine learning techniques. Initially, an improved background subtraction method is used to extract the region of a person in the recording of a room environment. A selective updating technique is introduced for adapting the change of the background model to ensure that the human body region will not be absorbed into the background model when it is static for prolonged periods of time. Since two-dimensional features can generate false alarms and are not invariant to different directions, more robust three-dimensional features are next extracted from a three-dimensional person representation formed from video-camera measurements of multiple calibrated video-cameras. The extracted three-dimensional features are applied to construct a single Gaussian model using the maximum likelihood technique. This can be used to distinguish falls from non-fall activity by comparing the model output with a single. In the final works, new fall detection schemes which use only one uncalibrated video-camera are tested in a real elderly person s home environment. These approaches are based on two-dimensional features which describe different human body posture. The extracted features are applied to construct a supervised method for posture classification for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build a robust fall detection model

Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position