Super-resolution spatial, temporal and functional characterisation of voltage-gated calcium channels involved in exocytosis

The process of information transfer between neurons or endocrine cells is one of the most important, intricate and temporally precise processes in the body. Exocytosis, which is central to the process of excitation-­‐secretion coupling, is triggered by calcium signalling through voltage-­‐gated calcium channels. Super-­‐resolution imaging offers the possibility to fully understand the spatial relationship between the SNARE proteins involved in exocytosis, vesicles and the associated voltage-­‐gated calcium channels. In this thesis the focus is on exploring the trigger for exocytosis, specifically the spatial and functional role that voltage-­‐gated calcium channels play in this process. Super-­‐ resolution imaging techniques have been applied to measure the interaction between Cav2.2 calcium channels and the syntaxin1a SNARE protein, where binding was found to affect the overall channel distribution. A novel method of caged dye conjugated ω-­‐ conotoxin GVIA binding was developed for live cell single molecule imaging of Cav2.2 calcium channels. An innovative approach to analyse channel functionality and the distribution of calcium events at the plasma membrane was developed to create a temporal-­‐spatial map of calcium activity across the cell. These developments, combined with newly developed techniques in optical patching and simultaneous calcium and vesicle imaging reveal the functional relationship of voltage-­‐gated calcium channel and exocytosis at unprecedented spatial and temporal scales

Ericksen-Landau Modular Strain Energies for Reconstructive Phase Transformations in 2D crystals

By using modular functions on the upper complex half-plane, we study a class of strain energies for crystalline materials whose global invariance originates from the full symmetry group of the underlying lattice. This follows Ericksen's suggestion which aimed at extending the Landau-type theories to encompass the behavior of crystals undergoing structural phase transformation, with twinning, microstructure formation, and possibly associated plasticity effects. Here we investigate such Ericksen-Landau strain energies for the modelling of reconstructive transformations, focusing on the prototypical case of the square-hexagonal phase change in 2D crystals. We study the bifurcation and valley-floor network of these potentials, and use one in the simulation of a quasi-static shearing test. We observe typical effects associated with the micro-mechanics of phase transformation in crystals, in particular, the bursty progression of the structural phase change, characterized by intermittent stress-relaxation through microstructure formation, mediated, in this reconstructive case, by defect nucleation and movement in the lattice.Comment: 17 pages, 6 figures, links to 4 supplementary video

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

Design of self-repairable superhydrophobic and switchable surfaces using colloidal particles

The design of functional materials with complex properties is very important for different applications, such as coatings, microelectronics, biotechnologies and medicine. It is also crucial that such kinds of materials have a long service lifetime. Unfortunately, cracks or other types of damages may occur during everyday use and some parts of the material should be changed for the regeneration of the initial properties. One of the approaches to avoid the replacement is utilization of self-healing materials. The aim of this thesis was to design a self-repairable material with superhydrophobic and switchable properties using colloidal particles. Specific goals were the synthesis of colloidal particles and the preparation of functional surfaces incorporated with the obtained particles, which would exhibit a repairable switching behavior and repairable superhydrophobicity. In order to achieve these goals, first, methods of preparation of simple and functional colloidal particles were developed. Second, the behavior of particles at surfaces of easy fusible solid materials, namely, paraffin wax or perfluorodecane, was investigated

Secure covert communications over streaming media using dynamic steganography

Streaming technologies such as VoIP are widely embedded into commercial and industrial applications, so it is imperative to address data security issues before the problems get really serious. This thesis describes a theoretical and experimental investigation of secure covert communications over streaming media using dynamic steganography. A covert VoIP communications system was developed in C++ to enable the implementation of the work being carried out. A new information theoretical model of secure covert communications over streaming media was constructed to depict the security scenarios in streaming media-based steganographic systems with passive attacks. The model involves a stochastic process that models an information source for covert VoIP communications and the theory of hypothesis testing that analyses the adversary‘s detection performance. The potential of hardware-based true random key generation and chaotic interval selection for innovative applications in covert VoIP communications was explored. Using the read time stamp counter of CPU as an entropy source was designed to generate true random numbers as secret keys for streaming media steganography. A novel interval selection algorithm was devised to choose randomly data embedding locations in VoIP streams using random sequences generated from achaotic process. A dynamic key updating and transmission based steganographic algorithm that includes a one-way cryptographical accumulator integrated into dynamic key exchange for covert VoIP communications, was devised to provide secure key exchange for covert communications over streaming media. The discrete logarithm problem in mathematics and steganalysis using t-test revealed the algorithm has the advantage of being the most solid method of key distribution over a public channel. The effectiveness of the new steganographic algorithm for covert communications over streaming media was examined by means of security analysis, steganalysis using non parameter Mann-Whitney-Wilcoxon statistical testing, and performance and robustness measurements. The algorithm achieved the average data embedding rate of 800 bps, comparable to other related algorithms. The results indicated that the algorithm has no or little impact on real-time VoIP communications in terms of speech quality (< 5% change in PESQ with hidden data), signal distortion (6% change in SNR after steganography) and imperceptibility, and it is more secure and effective in addressing the security problems than other related algorithms

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multi-Molecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance have proved a persistent challenge in frustrating the the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. We demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries

Soft Biometric Analysis: MultiPerson and RealTime Pedestrian Attribute Recognition in Crowded Urban Environments

Traditionally, recognition systems were only based on human hard biometrics. However, the ubiquitous CCTV cameras have raised the desire to analyze human biometrics from far distances, without people attendance in the acquisition process. Highresolution face closeshots are rarely available at far distances such that facebased systems cannot provide reliable results in surveillance applications. Human soft biometrics such as body and clothing attributes are believed to be more effective in analyzing human data collected by security cameras. This thesis contributes to the human soft biometric analysis in uncontrolled environments and mainly focuses on two tasks: Pedestrian Attribute Recognition (PAR) and person reidentification (reid). We first review the literature of both tasks and highlight the history of advancements, recent developments, and the existing benchmarks. PAR and person reid difficulties are due to significant distances between intraclass samples, which originate from variations in several factors such as body pose, illumination, background, occlusion, and data resolution. Recent stateoftheart approaches present endtoend models that can extract discriminative and comprehensive feature representations from people. The correlation between different regions of the body and dealing with limited learning data is also the objective of many recent works. Moreover, class imbalance and correlation between human attributes are specific challenges associated with the PAR problem. We collect a large surveillance dataset to train a novel gender recognition model suitable for uncontrolled environments. We propose a deep residual network that extracts several posewise patches from samples and obtains a comprehensive feature representation. In the next step, we develop a model for multiple attribute recognition at once. Considering the correlation between human semantic attributes and class imbalance, we respectively use a multitask model and a weighted loss function. We also propose a multiplication layer on top of the backbone features extraction layers to exclude the background features from the final representation of samples and draw the attention of the model to the foreground area. We address the problem of person reid by implicitly defining the receptive fields of deep learning classification frameworks. The receptive fields of deep learning models determine the most significant regions of the input data for providing correct decisions. Therefore, we synthesize a set of learning data in which the destructive regions (e.g., background) in each pair of instances are interchanged. A segmentation module determines destructive and useful regions in each sample, and the label of synthesized instances are inherited from the sample that shared the useful regions in the synthesized image. The synthesized learning data are then used in the learning phase and help the model rapidly learn that the identity and background regions are not correlated. Meanwhile, the proposed solution could be seen as a data augmentation approach that fully preserves the label information and is compatible with other data augmentation techniques. When reid methods are learned in scenarios where the target person appears with identical garments in the gallery, the visual appearance of clothes is given the most importance in the final feature representation. Clothbased representations are not reliable in the longterm reid settings as people may change their clothes. Therefore, developing solutions that ignore clothing cues and focus on identityrelevant features are in demand. We transform the original data such that the identityrelevant information of people (e.g., face and body shape) are removed, while the identityunrelated cues (i.e., color and texture of clothes) remain unchanged. A learned model on the synthesized dataset predicts the identityunrelated cues (shortterm features). Therefore, we train a second model coupled with the first model and learns the embeddings of the original data such that the similarity between the embeddings of the original and synthesized data is minimized. This way, the second model predicts based on the identityrelated (longterm) representation of people. To evaluate the performance of the proposed models, we use PAR and person reid datasets, namely BIODI, PETA, RAP, Market1501, MSMTV2, PRCC, LTCC, and MIT and compared our experimental results with stateoftheart methods in the field. In conclusion, the data collected from surveillance cameras have low resolution, such that the extraction of hard biometric features is not possible, and facebased approaches produce poor results. In contrast, soft biometrics are robust to variations in data quality. So, we propose approaches both for PAR and person reid to learn discriminative features from each instance and evaluate our proposed solutions on several publicly available benchmarks.This thesis was prepared at the University of Beria Interior, IT Instituto de Telecomunicações, Soft Computing and Image Analysis Laboratory (SOCIA Lab), Covilhã Delegation, and was submitted to the University of Beira Interior for defense in a public examination session