17,627 research outputs found
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Bayesian networks for disease diagnosis: What are they, who has used them and how?
A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem,
used to show dependencies or cause-and-effect relationships between variables.
They are widely applied in diagnostic processes since they allow the
incorporation of medical knowledge to the model while expressing uncertainty in
terms of probability. This systematic review presents the state of the art in
the applications of BNs in medicine in general and in the diagnosis and
prognosis of diseases in particular. Indexed articles from the last 40 years
were included. The studies generally used the typical measures of diagnostic
and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the
area under the ROC curve. Overall, we found that disease diagnosis and
prognosis based on BNs can be successfully used to model complex medical
problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape
Circadian variations in aortic stiffness, sympathetic vasoconstriction, and post-ischemic vasodilation in adults with and without type 2 diabetes.
The current literature reveals a lack of information on the circadian variations of some important cardiovascular risk factors related to the work of the heart or the capacity to provide blood and oxygen to various tissues. These factors include aortic stiffness, peripheral vasoconstrictor responsiveness, and post-ischemic vasodilation capacity. Furthermore, it is not clear whether the impact of an external stressor capable of activating the sympathetic nervous system could have greater repercussions on the cardiovascular system in the morning than in the evening. Given the higher incidence of acute cardiovascular events in the morning than in the evening, the studies undertaken in this thesis aim to investigate the circadian variations of these factors that are linked to cardiovascular risk, both at rest and during acute activation of the sympathetic nervous system. Type 2 diabetes (T2DM) is a condition that induces deleterious changes in cardiovascular function, impacting cardiovascular mortality and morbidity. Thus, the impact of diabetes will be evaluated. As a secondary purpose, considering the sex differences in the incidence and prognosis of cardiovascular disease, the effect of sex will be evaluated. Aortic stiffness proved not to be increased in the morning compared to the evening at specific times when the cardiovascular risk is significantly different, both at rest and during sympathetic activation. However, while healthy older women show similar aortic stiffness values compared to their male counterparts during acute stress, older women with T2DM reported greater aortic stiffness compared to men with T2DM. The post-ischemic forearm vasodilation is blunted in the morning compared to the evening in healthy elderly and such an attenuated vasodilation capacity impairs blood flow supply towards the ischemic area. The presence of T2DM does not affect vasodilation capacity and reactive hyperemia, but induces circadian variations in arterial pressure. The peripheral vasoconstriction triggered by a standardized sympathetic stressor is similar between morning and evening, regardless of the presence of T2DM and reduced baseline vascular conductance values in the morning. However, the peripheral vasoconstriction responsiveness is blunted in individuals with T2DM than in healthy ones as sympathetic activation induces vasodilation on the contralateral forearm in individuals with T2DM and vasoconstriction in healthy age-matched subjects. This finding highlights a neurovascular response to an external stressor altered by T2DM. Taken together, our findings suggest that the baseline state of constriction of the peripheral vascular tissue is greater in the morning than in the evening, but this fact is not due to greater sympathetic vasoconstriction responsiveness in the morning. Higher morning vasoconstriction at baseline however affects the capacity of a vascular tissue to dilate and, in turn, to supply blood to an ischemic tissue. Similar sympathetic vasoconstriction responsiveness between morning and evening is a likely factor explaining similar or lower values of central artery stiffness in the morning than in the evening, not only at rest but also during sympathetic excitation. Paradoxically, adults with T2DM report an increase in sympathetic-mediated dilatation capacity on the vascular tissue, which might be a defense mechanism that allows to reduce the central pressor response during sympathetic excitation
Chiral active fluids: Odd viscosity, active turbulence, and directed flows of hydrodynamic microrotors
While the number of publications on rotating active matter has rapidly increased in recent years, studies on purely hydrodynamically interacting rotors on the microscale are still rare, especially from the perspective of particle based hydrodynamic simulations. The work presented here targets to fill this gap. By means of high-performance computer simulations, performed in a highly parallelised fashion on graphics processing units, the dynamics of ensembles of up to 70,000 rotating colloids immersed in an explicit mesoscopic solvent consisting out of up to 30 million fluid particles, are investigated. Some of the results presented in this thesis have been worked out in collaboration with experimentalists, such that the theoretical considerations developed in this thesis are supported by experiments, and vice versa. The studied system, modelled in order to resemble the essential physics of the experimentally realisable system, consists out of rotating magnetic colloidal particles, i.e., (micro-)rotors, rotating in sync to an externally applied magnetic field, where the rotors solely interact via hydrodynamic and steric interactions. Overall, the agreement between simulations and experiments is very good, proving that hydrodynamic interactions play a key role in this and related systems.
While already an isolated rotating colloid is driven out of equilibrium, only collections of two or more rotors have experimentally shown to be able to convert the rotational energy input into translational dynamics in an orbital rotating fashion. The rotating colloids inject circular flows into the fluid, such that detailed balance is broken, and it is not a priori known whether equilibrium properties of colloids can be extended to isolated rotating colloids. A joint theoretical and experimental analysis of isolated, pairs, and small groups of hydrodynamically interacting rotors is given in chapter 2. While the translational dynamics of isolated rotors effectively resemble the dynamics of non-rotating colloids, the orbital rotation of pairs of rotors can be described with leading order hydrodynamics and a two-dimensional analogy of FaxĂ©nâs law is derived.
In chapter 3, a homogeneously distributed ensemble of rotors (bulk) as a realisation of a chiral active fluid is studied and it is explicitly shown computationally and experimentally that it carries odd viscosity. The mutual orbital translation of rotors and an increase of the effective solvent viscosity with rotor density lead to a non-monotonous behaviour of the average translational velocity. Meanwhile, the rotor suspension bears a finite osmotic compressibility resulting from the long-ranged nature of hydrody- namic interactions such that rotational and odd stresses are transmitted through the solvent also at small and intermediate rotor densities. Consequently, density inhomogeneities predicted for chiral active fluids with odd viscosity can be found and allow for an explicit measurement of odd viscosity in simulations and experiments. At intermediate densities, the collective dynamics shows the emergence of multi-scale vortices and chaotic motion which is identified as active turbulence with a self-similar power-law decay in the energy spectrum, showing that the injected energy on the rotor scale is transported to larger scales, similar to the inverse energy cascade of clas- sical two-dimensional turbulence. While either odd viscosity or active turbulence have been reported in chiral active matter previously, the system studied here shows that the emergence of both simultaneously is possible resulting from the osmotic compressibility and hydrodynamic mediation of odd and active stresses. The collective dynamics of colloids rotating out of phase, i.e., where a constant torque instead of a constant angular velocity is applied, is shown to be qualitatively very similar. However, at smaller densities, local density inhomogeneities imply position dependent angular velocities of the rotors resulting from inter-rotor friction.
While the friction of a quasi-2D layer of active colloids with the substrate is often not easily modifiable in experiments, the incorporation of substrate friction into the simulation models typically implies a considerable increase in computational effort. In chapter 4, a very efficient way of incorporating the friction with a substrate into a two-dimensional multiparticle collision dynamics solvent is introduced, allowing for an explicit investigation of the influences of substrate on active dynamics. For the rotor fluid, it is explicitly shown that the influence of the substrate friction results in a cutoff of the hydrodynamic interaction length, such that the maximum size of the formed vortices is controlled by the substrate friction, also resulting in a cutoff in the energy spectrum, because energy is taken out of the system at the respective length. These findings are in agreement with the experiments.
Since active particles in confinement are known to organise in states of collective dynamics, ensembles of rotationally actuated colloids are studied in circular confinement and in the presence of periodic obstacle lattices in chapters 5 and 6, respectively. The results show that the chaotic active turbulent transport of rotors in suspension can be enhanced and guided resulting from edge flows generated at the boundaries, as has recently been reported for a related chiral active system. The consequent collective rotor dynamics can be regarded as a superposition of active turbulent and imposed flows, leading to on average stationary flows. In contrast to the bulk dynamics, the imposed flows inject additional energy into the system on the long length scales, and the same scaling behaviour of the energy spectrum as in bulk is only obtained if the energy injection scales, due to the mutual generation of rotor translational dynamics throughout the system and the edge flows, are well separated. The combination of edge flow and entropic layering at the boundaries leads to oscillating hydrodynamic stresses and consequently to an oscillating vorticity profile. In the presence of odd viscosity, this consequently leads to non-trivial steady-state density modulations at the boundary, resulting from a balance of osmotic pressure and odd stresses.
Relevant for the efficient dispersion and mixing of inert particles on the mesoscale by means of active turbulent mixing powered by rotors, a study of the dynamics of a binary mixture consisting out of rotors and passive particles is presented in chapter 7. Because the rotors are not self-propelled, but the translational dynamics is induced by the surrounding rotors, the passive particles, which do not inject further energy into the system, are transported according to the same mechanism as the rotors. The collective dynamics thus resembles the pure rotor bulk dynamics at the respective density of only rotors. However, since no odd stresses act between the passive particles, only mutual rotor interactions lead to odd stresses leading to the accumulation of rotors in the regions of positive vorticity. This density increase is associated with a pressure increase, which balances the odd stresses acting on the rotors. However, the passive particles are only subject to the accumulation induced pressure increase such that these particles are transported into the areas of low rotor concentration, i.e., the regions of negative vorticity. Under conditions of sustained vortex flow, this results in segregation of both particle types.
Since local symmetry breaking can convert injected rotational into translational energy, microswimmers can be constructed out of rotor materials when a suitable breaking of symmetry is kept in the vicinity of a rotor. One hypothetical realisation, i.e., a coupled rotor pair consisting out of two rotors of opposite angular velocity and of fixed distance, termed a birotor, are studied in chapter 8. The birotor pumps the fluid into one direction and consequently translates into the opposite direction, and creates a flow field reminiscent of a source doublet, or sliplet flow field. Fixed in space the birotor might be an interesting realisation of a microfluidic pump. The trans- lational dynamics of a birotor can be mapped onto the active Brownian particle model for single swimmers. However, due to the hydrodynamic interactions among the rotors, the birotor ensemble dynamics do not show the emergence of stable motility induced clustering. The reason for this is the flow created by birotor in small aggregates which effectively pushes further arriving birotors away from small aggregates, which eventually are all dispersed by thermal fluctuations
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European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases.
Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging and Re-Emerging Infectious Diseases
Infectious diseases are initiated by small pathogenic living germs that are transferred from person to person by direct or indirect contact. Recently, different newly emerging and reemerging infectious viral diseases have become greater threats to human health and global stability. Investigators can anticipate epidemics through the advent of numerous mathematical tools that can predict specific pathogens and identify potential targets for vaccine and drug design and will help to fight against these challenges. Currently, computational approaches that include mathematical and essential tools have unfolded the way for a better understanding of newly originated emerging and re-emerging infectious disease, pathogenesis, diagnosis, and treatment option of specific diseases more easily, where immunoinformatics plays a crucial role in the discovery of novel peptides and vaccine candidates against the different viruses within a short time. Computational approaches include immunoinformatics, and computer-aided drug design (CADD)-based model trained biomolecules that offered reasonable and quick implementation approaches for the modern discovery of effective viral therapies. The essence of this review is to give insight into the multiple approaches not only for the detection of infectious diseases but also profound how people can pick appropriate models for the detection of viral therapeutics through computational approaches
Optimising acoustic cavitation for industrial application
The ultrasonic horn is one of the most commonly used acoustic devices in laboratories and industry. For its efficient application to cavitation mediated process, the cavitation generated at its tip as a function of its tip-vibration amplitudes still needed to be studied in detail. High-speed imaging and acoustic detection are used to investigate the cavitation generated at the tip of an ultrasonic horn, operating at a fundamental frequency, f0, of 20 kHz. Tip-vibration amplitudes are sampled at fine increments across the range of input powers available. The primary bubble cluster under the tip is found to undergo subharmonic periodic collapse, with concurrent shock wave emission, at frequencies of f0/m, with m increasing through integer values with increasing tip-vibration amplitude. The contribution of periodic shock waves to the noise spectra of the acoustic emissions is confirmed. Transitional input powers for which the value of m is indistinct, and shock wave emission irregular and inconsistent, are identified through Vrms of the acoustic detector output. For cavitation applications mediated by bubble collapse, sonications at transitional powers may lead to inefficient processing. The ultrasonic horn is also deployed to investigate the role of shock waves in the fragmentation of intermetallic crystals, nominally for ultrasonic treatment of Aluminium melt, and in a novel two-horn configuration for potential cavitation enhancement effects. An experiment investigating nitrogen fixation via cavitation generated by focused ultrasound exposures is also described. Vrms from the acoustic detector is again used to quantify the acoustic emissions for comparison to the sonochemical nitrite yield and for optimisation of sonication protocols at constant input energy. The findings revealed that the acoustic cavitation could be enhanced at constant input energy through optimisation of the pulse duration and pulse interval. Anomalous results may be due to inadequate assessment for the nitrate generated. The studies presented in this thesis have illustrated means of improving the cavitation efficiency of the used acoustic devices, which may be important to some selected industrial processes
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
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