1,033 research outputs found

    Markov field models of molecular kinetics

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    Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

    Characterising Shape Variation in the Human Right Ventricle Using Statistical Shape Analysis: Preliminary Outcomes and Potential for Predicting Hypertension in a Clinical Setting

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    Variations in the shape of the human right ventricle (RV) have previously been shown to be predictive of heart function and long term prognosis in Pulmonary Hypertension (PH), a deadly disease characterised by high blood pressure in the pulmonary arteries. The extent to which ventricular shape is also affected by non-pathological features such as sex, body mass index (BMI) and age is explored in this thesis. If fundamental differences in the shape of a structurally normal RV exist, these might also impact the success of a predictive model. This thesis evaluates the extent to which non-pathological features affect the shape of the RV and determines the best ways, in terms of procedure and analysis, to adapt the model to consistently predict PH. It also identifies areas where the statistical shape analysis procedure is robust, and considers the extent to which specific, non-pathological, characteristics impact the diagnostic potential of the statistical shape model. Finally, recommendations are made on next steps in the development of a classification procedure for PH. The dataset was composed of clinically-obtained, cardiovascular magnetic resonance images (CMR) from two independent sources; The University of Pittsburgh Medical Center and Newcastle University. Shape change is assessed using a 3D statistical shape analysis technique, which topologically maps heart meshes through an harmonic mapping approach to create a unique shape function for each shape. Proper Orthogonal Decomposition (POD) was applied to the complete set of shape functions in order to determine and rank a set of shape features (i.e. modes and corresponding coefficients from the decomposition). MRI scanning protocol produced the most significant difference in shape; a shape mode associated with detail at the RV apex and ventricular length from apex to base strongly correlated with the MRI sequence used to record each subject. Qualitatively, a protocol which skipped slices produced a shorter RV with less detail at the apex. Decomposition of sex, age and BMI also derives unique RV shape descriptors which correspond to anatomically meaningful features. The shape features are shown to be able to predict presence of PH. The predictive model can be improved by including BMI as a factor, but these improvements are mainly concentrated in identification of healthy subjects

    Application of Advanced MRI to Fetal Medicine and Surgery

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    Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution. This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development. I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death

    DIAGNOSTIC ET ANALYSE DE L’ENVIRONNEMENT ACOUSTIQUE DES CONFIGURATIONS URBAINES SAHARIENNES. CAS DE BISKRA.

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    La pollution sonore affecte une part considérable de la population mondiale, y compris les habitants Sahariens. L’expansion proliférante des villes et le trafic routier constituent les principales causes de la recrudescence des effets négatifs du bruit sur l’environnement et la qualité de vie de ces habitants. Par ailleurs, les pays développés et les agglomérations Sahariennes connaissent un manque de données acoustiques et une énorme lacune en matière des normes et des procédures nécessaires à la planification urbaine. La présente étude vise à examiner l’environnement acoustique des différents tissus urbains constituant la ville de Biskra, comme elle s’appuie principalement sur des approches méthodologiques multidisciplinaires traitant la dimension objective, voire subjective. Premièrement, la théorie de la Syntaxe Spatiale visant à analyser la morphologie urbaine. En se focalisant sur l’analyse des segments angulaires qui permet un diagnostic détaillé des propriétés de l’avant-plan et de l’arrière-plan. Le potentiel des deux concepts de « à-mouvement » et « à travers-mouvement » permet une exploration perspicace des mouvements mécaniques et piétonniers à l'échelle locale et globale. Par conséquent, plusieurs rayons métriques ont été utilisés : 400 m, 800 m, 1200 m, 1600 m, 2000 m, 2400 m et 3200 m. Ensuite, une approche expérimentale, consistant à évaluer l’environnement acoustique en effectuant 240 stations de mesures à l’aide d’un sonomètre étalonné. Ces stations, enregistrant chacune 600 valeurs de niveau sonore équivalent continu pondéré A (LeqA), sont pour la plupart installées à proximité des zones résidentielles et des bords des principales autoroutes, voies de circulation et axes piétonniers. Les données obtenues ont été modélisées à l’aide de différents modèles d’interpolation fournis par le système d’information géographique (QGIS et SAGA GIS), notamment : Distance Inverse Pondérée (Gaussienne, Exponentielle, Quadratique K2) et Krigeage (Ordinaire, Universel). Finalement, l’approche subjective consiste à élaborer un sondage abordant trois axes distincts : la qualité affective perçue, la carte mentale sonore et les préférences en matière de paysage sonore. Les résultats mettent en évidence le caractère bruyant de Biskra, tout en soulignant l’efficacité du model IDW. Ils démontrent aussi une corrélation positive moyenne à forte des mesures syntaxiques à l’échelle globale et locale, impliquant une explication partielle des configurations urbaines et acoustiques par ces variables spatiales. Cette étude approfondie constitue un point de départ pour soulever cette question auprès des planificateurs et décideurs de la ville afin de créer un plan d’action pratique pour une stratégie de développement durable

    Computer Vision Methods for Autonomous Remote Sizing in Manufacturing

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    In the grand scheme of Industry 4.0, the employment of modern intelligent digital technology has been utilised to facilitate industrial production, leveraging automation to elevate production efficiency. Building upon this, Industry 5.0 takes a step forward, accentuating the concept of human-machine symbiosis. It directs its focus on augmenting human performance within the industry, mitigating errors made by workers, and honing the overarching performance of human-machine systems. Across various manufacturing domains, an escalating demand for this level of automation has been noticed. One such area is the speciality steel industry, whose tasks are the primary consideration of this dissertation. Speciality steel rolling forms the backbone of industrial sectors as diverse as aerospace and oil and gas. The key to the sustained survival of steel plants hinges on the digitalisation of the rolling process. Despite this, a significant number of steel rolling plants in the present day continue to place a heavy reliance on human operators to oversee and regulate the manufacturing process. With a view to securing the safety of workers in high-risk factory environments and optimising the control of steel production, this dissertation puts forth machine vision approaches. These are aimed at supervising the direction of hot steel sections and remotely gauging their dimensions, both conducted in real-time. This dissertation further contributes a novel image registration approach founded on extrinsic features. This approach is then amalgamated with frequency domain image fusion of optical images. The resultant fused image is designated to evaluate the size of high-quality hot steel sections from a remote standpoint. With the integration of the remote imaging sizing module, operators can stay abreast of the section dimensions in real time. Concurrently, the mill stands can be pre-adjusted to facilitate quality assurance. The efficacy of the developed approaches has been tested over real data, delivering an accuracy rate exceeding 95%. This suggests that the approach not only ensures worker safety but also contributes significantly to the enhancement of production control and efficiency in the speciality steel industry

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Development, Validation and Applications of MRI-Only Treatment Planning in Radiotherapy

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    Magnetic resonance imaging (MRI) has superior soft tissue visualization to guide radiotherapy treatment planning but does not provide the electron density information required for the dose calculation. Thus, MRI has been used in a complementary way, registering to the gold standard computed tomography (CT) scan. Development of methods to allow accurate planning from the MRI images would remove the requirement for additional (CT) scans as well as improve clinical workflow and remove potential registration errors. Various methods have been reported to generate datasets with electron density information from MRI data, with these being termed substitute, synthetic or pseudo CT (sCT) datasets. This thesis explores the potential variation in planning and optimization error from MRI-only treatment planning for a range of situations. sCT generation was explored with a deep learning methodology applied to a set of retrospective H&N patient data. A lung MRI sequence was investigated for its potential application for sCT generation, with various methods trialed and assessed for clinical suitability. For an existing sCT generation method used clinically for prostate cancer treatment planning, a time-reduced MRI sequence was investigated, optimizing scan parameters for this by initial assessment in a volunteer cohort, followed by clinical validation in a patient cohort. A pancreas MRI volunteer study was also conducted to investigate internal organ motion effects on treatment planning and potential treatment delivery to assess the suitability of treatment regimes for pancreatic cancer patients. This work provides evidence that MRI-only treatment planning is achievable and acceptably accurate. This has led to current and future implementations of findings into clinical practice locally, and potentially more widely. MRI-only treatment planning in radiotherapy could lead to improved patient outcomes, via both better target delineation and reduced normal tissue toxicity

    Review of Particle Physics

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    The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE
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