Placental abnormalities and hypertension in pregnancy

Preeclampsia is a significant and common complication of pregnancy, with characteristic signs of hypertension and proteinuria. Current theories postulate a role for altered placental perfusion as a consequence of abnormal placental development in the aetiology of preeclampsia. Animal models of human preeclampsia have shown that an imbalance of the inflammatory cytokine TNF-α leads to a similar maternal phenotype as seen with a surgical reduction of placental perfusion pressure. This suggests a role for the inflammatory response in generating the maternal signs of hypertension and proteinuria. Currently, there is no direct link showing that a cytokine imbalance (specifically increased TNF-) affects placental development in such a way as to result in altered blood flow. The ability to detect morphological changes and alterations in blood flow in experimental models of preeclampsia would provide a significant boost in our understanding of the syndrome. The aim of this study was to develop an “imbalance in pro-inflammatory cytokine (TNF-α)” experimental mouse model of preeclampsia and to utilize magnetic resonance imaging (MRI) for visualization of placental anatomy and for the analysis of changes in tissue morphology and function including blood flow and perfusion. Secondly, this study aimed to examine the relationship between; an imbalance in inflammatory cytokines; changes in placental markers involved in inflammation, hypoxia and pH homeostasis; and changes in blood flow in the aetiology of the maternal hypertensive response. Pregnant C57BL/6JArc mice were subject to either reduced utero-placental perfusion (RUPP), subcutaneous infusion of the inflammatory cytokine TNF-α, or control procedures. Blood pressure was measured by either tail cuff sphygmomanometry or by telemetry. Urine was collected to measure proteinuria and blood was collected to measure levels of the anti-angiogenic molecule soluble fms-like tyrosine kinase 1 (sFlt-1), a biomarker of the human disease. MRI images were acquired on anaesthetised mice on day 17 of gestation using a Bruker Avance 11.7 Tesla wide-bore spectrometer. Quantitative analysis of changes in T2 relaxation measurements were carried out by using Matlab™ to generate R2 (i.e., 1/T2) maps from the acquired T2 measurement data, with the T2 values being calculated from selected regions of interest. Additional high resolution MRI images were acquired on formalin fixed, Magnevist™ contrast agent infused placenta. Placentas were harvested on day 17 of pregnancy, either formalin fixed and paraffin embedded for histology or snap frozen for proteomics and genomics. Histology was performed on sections using either Haematoxylin and Eosin (H&E) or Periodic acid-Schiff (PAS) stains. Immunohistochemistry using secondary anti rabbit horse radish peroxidise linked polymer and visualising with DAB, or quantitative immunofluorescent histochemistry using Alexa 488 goat anti-rabbit IgG was performed using primary antibodies to Cytokeratin (trophoblast marker), HIF-1a (Hypoxia inducible transcription factor 1), CLIC-3 (chloride intracellular channel 3; Cl-/H+ co-transporter) and TLR-3 and TLR-4 (Toll-like receptor 3 and 4). Quantitative PCR (qPCR) was used to measure mRNA expression of mFlt-1, sFlt-1, hif-1, tlr-3, tlr-4, clic-3 and clic-4 in placental tissue. This thesis demonstrates that infusion of the inflammatory cytokine (TNF-α) is an experimental model for hypertension and proteinuria in murine pregnancy. Hypertension in the RUPP model was not definitively confirmed despite the proteinuria. No increase in sFlt-1 above the constitutively high levels of normal pregnancy was detected in the maternal serum of either model, suggesting sFlt-1 is not a reliable marker for disease in the mouse model. This thesis demonstrates that that morphologically distinct regions of the mouse placenta can be detected and quantified by MRI. Mapping of T2 relaxation times ,which are attenuated by both hypoxia (increased levels of deoxyhaemoglobin) and acidosis (increase in free protons), indicate contrast between regions which is is lost when blood flow ceases. Similar decrease in contrast is detected upon T2 mapping in the placentas of both the artificially reduced perfusion (RUPP) and imbalance of inflammatory cytokines (TNF-α) experimental models. Immunohistochemistry and qPCR detected increases in the presence of molecules involved in response to both inflammation (TLR-3 and TLR-4) and changes in oxygen (HIF-1α) and pH (CLIC-3) levels in placentas from animals subject to either TNF-α infusion or RUPP. These results demonstrate for the first time that morphological differences or abnormalities related to blood flow can be detected by T2 mapping in the placenta of mice subject to experimental models of preeclampsia and may be used to analyse changes quantitatively. This technology has the potential to be used when studying the dynamic changes in the placenta of pregnancies complicated by preeclampsia. Analysis of the MRI images suggests changes involve both increases in deoxyhaemoglobin (hypoxia) and decreases in intracellular pH (acidosis) and suggests that pH dependent mechanisms may be as equally important as hypoxia in the perturbed placenta. The results also indicate that the metabolic changes in the placenta in response to both decreased blood flow and TNF-α infusion involve upregulation of both TLR-3 and TLR-4 protein expression and upregulation of HIF-1α mRNA and protein. Alterations in expression and localisation of the H+/Cl- co transporter CLIC-3 was demonstrated in the placenta after TNF-α infusion, consistent with the metabolic change observed by MRI. Inflammation-driven changes in both oxygen and pH-dependent signalling pathways are thus implicated in alterations of the complex metabolic pathways of homeostasis and angiogenesis in the placenta that lead to the subsequent maternal hypertensive response

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose

Regulation of junction configuration by cell tension

The maintenance of cell-cell contacts is essential for tissue cohesion and a variety of different physiological processes in morphogenesis and homeostasis. Adherens junctions are protein complexes that mediate cell-cell contacts in epithelial cells and E-cadherin receptors are their main component. During junction formation, thin bundles of actin localise towards cell-cell contacts in the characteristic cytoskeletal organization of epithelia. Tension at the underlying cortex and thin bundle compaction help form tight, straight junctions and maintain cadherin receptors in place. However, how these epithelia-specific structures are formed and remodelled lacks in-depth understanding. In this study, I have addressed how contractile forces modulate junction configuration and molecular composition (adhesion receptors and actin cytoskeleton). Micropatterning was used to precisely confine the geometry of cells, control cortical forces and provide a permissive, simplistic way in which cells are allowed to interact. Three different shapes, namely squares, triangles and circles were patterned to study biophysical and junction properties. Although the average cell heights and volumes are similar between different geometries, cortical stiffness (i.e. Young’s modulus) is two-fold higher in cells grown in geometries that impose higher contractility: squares and triangles. Doublets seeded on these shapes also position their nuclei further apart and exhibit preferences in junction orientation. A majority of cells cultured on triangular and square geometries have shorter and straighter junctions with a clear presence of thin bundles parallel to the cell-cell interface. Localisation of phosphorylated myosin light chain to thin bundles reinforce the notion that these are the main contractile pool instead of the junctional actin at contacts. Counter-intuitively, E-cadherin and F-actin density are also reduced with increased contractility and tension. Taken together, higher levels of contractility and cortical tension imposed by the square and triangle geometric shapes, are necessary to properly generate the epithelial cellular architecture, configuration of junctions and their molecular makeup. This suggests that tensional constraints play an important role in regulating the stability of junctions and the organization of underlying actin filaments that support the characteristic epithelial cell shape.Open Acces

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

From Homing Behavior to Cognitive Mapping - Integration of Egocentric Pose Relations and Allocentric Landmark Information in a Graph Model

This thesis describes a behavior based approach to the problem of simultaneous localization and mapping ( SLAM ). The complex behavior of exploring an unknown environment is based on a combination of three local navigation strategies: obstacle avoidance, path integration, and scene based homing. :p: In this context the role of metric pose information is discussed. In the proposed system pose information is used to overcome several shortcomings of topological navigation, especially the problem of spatial aliasing. The spatial memory of the agent is modeled as a graph, which is embedded into the three dimensional pose space. In order to achieve global consistency a modified multidimensional scaling algorithm ( MDS ) is used. The proposed system differs from recent robotic systems in several ways. First, pose estimates are derived only between known places, i.e. there is no explicit knowledge about the location of single landmarks. Second, all pose relations are derived from odometry. Third, globally consistent position estimates are calculated separately from globally consistent heading estimates

Information embedding and retrieval in 3D printed objects

Deep learning and convolutional neural networks have become the main tools of computer vision. These techniques are good at using supervised learning to learn complex representations from data. In particular, under limited settings, the image recognition model now performs better than the human baseline. However, computer vision science aims to build machines that can see. It requires the model to be able to extract more valuable information from images and videos than recognition. Generally, it is much more challenging to apply these deep learning models from recognition to other problems in computer vision. This thesis presents end-to-end deep learning architectures for a new computer vision field: watermark retrieval from 3D printed objects. As it is a new area, there is no state-of-the-art on many challenging benchmarks. Hence, we first define the problems and introduce the traditional approach, Local Binary Pattern method, to set our baseline for further study. Our neural networks seem useful but straightfor- ward, which outperform traditional approaches. What is more, these networks have good generalization. However, because our research field is new, the problems we face are not only various unpredictable parameters but also limited and low-quality training data. To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the image segmentation area, and (ii) we cannot know everything from data, our models should be aware what key features they should learn. This thesis explores these ideas and even explore more. We show how to use end-to-end deep learning models to learn to retrieve watermark bumps and tackle covariates from a few training images data. Secondly, we introduce ideas from synthetic image data and domain randomization to augment training data and understand various covariates that may affect retrieve real-world 3D watermark bumps. We also show how the illumination in synthetic images data to effect and even improve retrieval accuracy for real-world recognization applications

Fracture characterisation and performance evaluation of corroded RC members by AE-based data analysis

Steel reinforcement corrosion has been regarded as one of the major causes of reinforced concrete (RC) structures failing prematurely, posing a serious structural durability problem worldwide. Detailed assessment of corrosion-induced damage and its effects on RC structures is critical for sustaining structural reliability and safety. This study develops and examines the feasibility of acoustic emission (AE) monitoring and data analysis methodologies to characterise corrosion-induced damage in RC members, followed by an evaluation of the effect of corrosion on load behaviour. Experimental investigations were conducted on a series of specimens of different configurations, namely concrete cubes with steel bars for pull-out tests and RC beams of different dimensions to be subjected to static and cyclic loading regimes. Focusing on developing evaluation methods based on AE monitoring and data analysis, a summary of work completed, and the associated findings are given as follows. Characterisation of the concrete cracking using parametric and waveform analysis was conducted to investigate the effect of corrosion on steel-concrete bond behaviour in the pull-out tests of concrete cubes. It was found that a small amount of corrosion (approximately 6%) could slightly increase the bond strength as a result of the rust expansion and reactionary confinement of concrete. Corrosion was also found to be able to mitigate the damage caused by cyclic loading. AE signal analysis indicates that the concrete cracking mode during the steel-concrete de-bonding process has changed as a result of steel corrosion. Characterisation of load behaviour and failure mode of corroded RC beams was conducted by flexural load tests aided by AE monitoring and digital image correlation (DIC). The DIC strain mapping results and AE signal features revealed that corrosion has an influence on the concrete cracking mechanism of the beam specimens. Corrosion has also altered the failure mode of a shear-critical beam specimen series to flexure owing to the change of steel-concrete bond behaviour. Numerical simulation of AE wave front propagation in RC media and tomographic evaluation of internal damage was implemented on one group of RC beam specimens tested in this study. The numerical model of the specimens was discretised using finite-difference grid meshing, and the different acoustic properties of steel and concrete were defined. On this basis, simulation of AE wave front propagation considering concrete cover cracking and steel rust layer formation was carried out using the fast-marching method. The effect of corrosion-induced damage on the AE rays was studied by examining non-linear ray tracing in the simulation. A tomographic reconstruction approach that solved by the quasi-Newton method provided a potential way to quantitatively evaluate the internal damage of RC beams using AE monitoring data. A novel method was developed for assessing the corrosion level in RC beams using a data-driven approach. Normalization of AE data was applied using principal component analysis to minimise variations in AE signal features caused by differences in the geometrical and material properties of RC beams as well as in the AE monitoring instrumentation setup. The machine learning models, including k-nearest neighbours (KNN) and support vector machines (SVM), were trained using the normalised AE features. The trained KNN models were found effective at predicting the corrosion level in RC beams using the secondary AE signals as input, which could be acquired from the cyclic loading of beams. Key words: Steel Corrosion, Concrete cracking, Steel-Concrete Bond, Reinforced Concrete Beam, Load Behaviour, Acoustic Emission, Digital Image Correlation, Tomographic Reconstruction, Data-driven

Ensuring the resilience of wireless sensor networks to malicious data injections through measurements inspection

Malicious data injections pose a severe threat to the systems based on \emph{Wireless Sensor Networks} (WSNs) since they give the attacker control over the measurements, and on the system's status and response in turn. Malicious measurements are particularly threatening when used to spoof or mask events of interest, thus eliciting or preventing desirable responses. Spoofing and masking attacks are particularly difficult to detect since they depict plausible behaviours, especially if multiple sensors have been compromised and \emph{collude} to inject a coherent set of malicious measurements. Previous work has tackled the problem through \emph{measurements inspection}, which analyses the inter-measurements correlations induced by the physical phenomena. However, these techniques consider simplistic attacks and are not robust to collusion. Moreover, they assume highly predictable patterns in the measurements distribution, which are invalidated by the unpredictability of events. We design a set of techniques that effectively \emph{detect} malicious data injections in the presence of sophisticated collusion strategies, when one or more events manifest. Moreover, we build a methodology to \emph{characterise} the likely compromised sensors. We also design \emph{diagnosis} criteria that allow us to distinguish anomalies arising from malicious interference and faults. In contrast with previous work, we test the robustness of our methodology with automated and sophisticated attacks, where the attacker aims to evade detection. We conclude that our approach outperforms state-of-the-art approaches. Moreover, we estimate quantitatively the WSN degree of resilience and provide a methodology to give a WSN owner an assured degree of resilience by automatically designing the WSN deployment. To deal also with the extreme scenario where the attacker has compromised most of the WSN, we propose a combination with \emph{software attestation techniques}, which are more reliable when malicious data is originated by a compromised software, but also more expensive, and achieve an excellent trade-off between cost and resilience.Open Acces