Quantifying Joint Activities using Cross-Recurrence Block Representation

Humans, as social beings, are capable of employing variousbehavioral cues, such as gaze, speech, manual action, and bodyposture, in everyday communication. However, to extract fine-grained interaction patterns in social contexts has beenpresented with methodological challenges. Cross-RecurrencePlot Quantification Analysis (CRQA) is an analysis methodinvented in theoretical physics and recently applied tocognitive science to study interpersonal coordination. In thispaper, we extend this approach to analyzing joint activities inchild-parent interaction. We define a new representation asCross Recurrence Block based on CRQA. With thisrepresentation, we are able to capture interpersonal dynamicsfrom more than two behavioral streams in one CrossRecurrence Plot and derive a suite of measures to quantifydetailed characteristics of coordination. Using a datasetcollected from a child-parent interaction study, we show thatthese quantitative measures of joint activities revealdevelopmental changes in coordinative behavioral patternsbetween children and parents

Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Musculoskeletal shoulder modelling for clinical applications

The shoulder is the most commonly dislocated joint in the human body, with the vast majority of these dislocations being located anteriorly. Anterior shoulder dislocations are commonly associated with capsuloligamentous injuries and osseous defects. Recurrent anterior instability is a common clinical problem and understanding the influence of structural damage on joint stability is an important adjunct to surgical decision-making. Clinical practice is guided by experience, radiology, retrospective analyses and physical cadaver experiments. As the stability of the shoulder is load dependent, with higher joint forces increasing instability, the aim of this thesis was to develop and validate computational shoulder models to simulate the effect of structural damage on joint stability under in-vivo loading conditions to aid surgical decision-making for patients with anterior shoulder instability. The UK National Shoulder Model, consisting of 21 upper limb muscles crossing 5 functional joints, was customised to accurately quantify shoulder loading during functional activities. Ten subject-specific shoulder models were developed from Magnetic Resonance Imaging and validated against electromyographic signals. These models were used to identify the best combination of anthropometric parameters that yield best model outcomes in shoulder loading through linear scaling of personalised shoulder models. These parameters were gender and the ratio of body height to shoulder width (p<0.04) and these model predictions are significantly improved (p<0.02) when compared to the generic model. The forces derived from the modelling were used in two subject-specific finite element models with an anatomically accurate representation of the labrum, to assess shoulder stability through concavity compression under physiological joint loading for pathologies associated with anterior shoulder instability. The key results from these studies were that there is a high risk of shoulder dislocation under physiological joint loading for patients with a 2 mm anterior or 4 mm anteroinferior osseous defect. The loss in anterior shoulder stability in overhead throwing athletes with intact glenoid following biceps tenodesis is compensated by a non-significant increase in rotator cuff muscle force which maintain shoulder stability across all overhead throwing sports, except baseball pitching, where biceps tenodesis has significantly decreased (p<0.02) anterior shoulder stability. The work in this thesis has advanced the technology of musculoskeletal modelling of the shoulder through the inclusion of concavity compression and has applied this to various relevant clinical questions through the further development of an anatomical atlas, and an atlas of tasks of daily living. The applications of such modelling are broader than those addressed here and therefore this work serves as the foundation for potential further studies, including the bespoke design of arthroplasty or other soft tissue procedures.Open Acces

Symbolic Play and Language Acquisition: The Dynamics of Infant-Caretaker Communication during Symbolic Play

Infant symbolic play and language acquisition have long been linked. While both activities are inherently social and their acquisition is typically scaffolded by a competent other (Vygotsky, 1978), most studies investigating the symbolic play-language link have considered it in contexts of solitary play. This thesis examines the dynamic nature of the relationship in a semi-naturalistic setting. Fifty-two infant-caretaker dyads engaged in a 20-minute play session that manipulated play type through the use of different toy sets (symbolic versus non-symbolic). Study 1 showed that play contexts influenced language: in symbolic play, infants spoke more and their language and interactions were more complex. CDS was more interactionally demanding (more questions and mimetics) in symbolic play, while in non-symbolic play it was more directive (imperatives and naming). Study 2 established that conversational turn dynamics patterns differed: there were more conversational turns in symbolic play, turn transitions were longer, and infants were more likely to control entire turn sequences. Study 3 demonstrated that symbolic play allowed for greater and richer content alignment: there were more semantic repetitions and infants were more likely to choose the topic of conversation than their parents. Study 4 revealed more complex and demanding epistemic exchanges of information in symbolic play: infants were more likely to inform, assert, and build on previous information when they spoke. Parents were more likely to actively engage the infants in symbolic play by seeking or requesting information, but the ambiguity of symbolic play also meant that it was more difficult for participants to understand each other. When combined, the results of these four studies suggest that symbolic play is a challenging but communicatively rich environment for infants' language development, constituting a zone of proximal development deriving from the need to establish shared intentionality during interaction

Assessment Of Blood Pressure Regulatory Controls To Detect Hypovolemia And Orthostatic Intolerance

Regulation of blood pressure is vital for maintaining organ perfusion and homeostasis. A significant decline in arterial blood pressure could lead to fainting and hypovolemic shock. In contrast to young and healthy, people with impaired autonomic control due to aging or disease find regulating blood pressure rather demanding during orthostatic challenge. This thesis performed an assessment of blood pressure regulatory controls during orthostatic challenge via traditional as well as novel approaches with two distinct applications 1) to design a robust automated system for early identification of hypovolemia and 2) to assess orthostatic tolerance in humans. In chapter 3, moderate intensity hemorrhage was simulated via lower-body negative pressure (LBNP) with an aim to identify moderate intensity hemorrhage (-30 and -40 mmHg LBNP) from resting baseline. Utilizing features extracted from common vital sign monitors, a classification accuracy of 82% and 91% was achieved for differentiating -30 and -40 mmHg LBNP, respectively from baseline. In chapter 4, cause-and-effect relationship between the representative signals of the cardiovascular and postural systems to ascertain blood pressure homeostasis during standing was performed. The degree of causal interaction between the two systems, studied via convergent cross mapping (CCM), showcased the existence of a significant bi-directional interaction between the representative signals of two systems to regulate blood pressure. Therefore, the two systems should be accounted for jointly when addressing physiology behind fall. Further, in chapter 5, the potential of artificial gravity (2-g) induced via short-arm human centrifuge at feet towards evoking blood pressure regulatory controls analogous to standing was investigated. The observation of no difference in the blood pressure regulatory controls, during 2-g centrifugation compared to standing, strongly supported the hypothesis of artificial hypergravity for mitigating cardiovascular deconditioning, hence minimizing post-flight orthostatic intolerance

Fractal Analysis and Chaos in Geosciences

The fractal analysis is becoming a very useful tool to process obtained data from chaotic systems in geosciences. It can be used to resolve many ambiguities in this domain. This book contains eight chapters showing the recent applications of the fractal/mutifractal analysis in geosciences. Two chapters are devoted to applications of the fractal analysis in climatology, two of them to data of cosmic and solar geomagnetic data from observatories. Four chapters of the book contain some applications of the (multi-) fractal analysis in exploration geophysics. I believe that the current book is an important source for researchers and students from universities

Numerical modelling of rockfall evolution in hard rock slopes

The aim of this thesis has been to model small rockfalls in order to better understand where, when and why they occur. High-resolution monitoring of rock slopes has revealed clustering of rockfalls through space and time, suggesting interactions, whereby one detachment from a rock slope influences the nature of those that follow. This observation contrasts with the more conventional idea of rockfalls as time-independent, discrete events that occur in response to an identifiable trigger. As the processes that give rise to observations of rockfall clustering are not well established, this thesis takes the opportunity to bring together current understanding of the controls on rockfalls with ideas around the progressive development of failure in brittle rock in an attempt to explain these patterns. The representation of these processes at scales comparable to high resolution field monitoring has not previously been attempted. Therefore this thesis has developed an approach using numerical modelling to simulate rockfalls as spatially and temporally-dependent sequences of events, to better explain the underlying mechanisms. This study begins with the analysis of a high-resolution inventory of rockfalls, concentrating on identifying patterns in rockfall occurrence. Analyses of this data reveals patterns of rockfalls that cannot be explained by environmental conditions or local geology alone. Evidence has been collected that demonstrates that rockfalls cluster in space and time, and that through time rockfall scars grow upward and outward in a consistent manner. The results of this analysis are used to inform numerical modelling that explores the mechanics driving small rockfalls, focussing upon the impact of a detachment on the location and timing of future rockfalls. Numerical modelling of idealised rock slope sections was conducted using Slope Model and demonstrated that the timing and location of failure in a rock slope could be considered as a function of accumulated damage, represented by fracture. The results suggest that time-dependent failure and associated mechanisms of stress redistribution and damage generation are one possible explanation for the propagating sequences of contiguous failures observed. Finally, this thesis has taken an exploratory approach to modelling rockfalls through the development of a new deterministic, numerical model that simulates rockfall evolution using cellular automata. This rockfall model allows the patterns and associated underlying mechanics of small rockfalls to be explored in detail using a reduced complexity approach. Critically rockfalls are modelled in a 2.5D slope face perspective to allow both rockfalls and their effects to interact across the rock slope through time. The model operates at a relatively high spatial and temporal resolution to consider the full range of rockfall characteristics that have been observed. The outputs of the model are compared with the two-year monitoring data to address key questions regarding the competing roles of endo- and exogenic forcing on rockfall occurrence. The results of the rockfall modelling shows that a consideration of stress redistribution from small scale rockfalls and time-dependent weakening provides a possible explanation for the size distribution of rockfalls, their location and timing, and the resulting changes to slope profile form as observed in the field. This has implications for how rock slopes are monitored and modelled to determine the potential for future rockfalls to occur

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis