The methods that fueled the microscale revolution (top-down design/fabrication, combined with application of forces large enough to overpower stochasticity) constitute an approach that will not scale down to nanoscale systems. In contrast, in nanotechnology, we strive to embrace nature’s quite different paradigms to create functional systems, such as self-assembly to create structures, exploiting stochasticity, rather than overwhelming it, in order to create deterministic, yet highly adaptable, behavior. Nature’s approach, through billions of years of evolutionary development, has achieved self-assembling, self-duplicating, self-healing, adaptive systems. Compared to microprocessors, nature’s approach has achieved eight orders of magnitude higher memory density and three orders of magnitude higher computing capacity while utilizing eight orders of magnitude less power. Perhaps the most complex of functions, homeostatis by a biological cell – i.e., the regulation of its internal environment to maintain stability and function – in a fluctuating and unpredictable environment, emerges from the interactions between perhaps 50M molecules of a few thousand different types. Many of these molecules (e.g. proteins, RNA) are produced in the stochastic processes of gene expression, and the resulting populations of these molecules are distributed across a range of values. So although homeostasis is maintained at the system (i.e. cell) level, there are considerable and unavoidable fluctuations at the component (protein, RNA) level. While on at least some level, we understand the variability in individual components, we have no understanding of how to integrate these fluctuating components together to achieve complex function at the system level. This thesis will explore the regulation and control of stochasticity in cells. In particular, the focus will be on (1) how genetic circuits use noise to generate more function in less space; (2) how stochastic and deterministic responses are co-regulated to enhance function at a system level; and (3) the development of high-throughput analytical techniques that enable a comprehensive view of the structure and distribution of noise on a whole organism level.

Cerebral autoregulation is an intrinsic physiological response that maintains a constant cerebral blood flow (CBF) despite dynamic changes in the systemic blood pressure. Autoregulation is achieved through changes in the resistance of the small blood vessels in the brain through reflexive vasodilatation and vasoconstriction. Cerebrovascular reactivity (CVR) is a measure of this response. CVR is defined as a change in CBF in response to a given vasodilatory stimulus. CVR therefore potentially reflects the vasodilatory reserve capacity of the cerebral vasculature to maintain a constant cerebral blood flow. A decrease in CVR (which is interpreted as a reduction in the vasodilatory reserve capacity) in the vascular territory downstream of a larger stenosed supply artery correlates strongly with the risk of a hemodynamic stroke. As a result, the use of CVR studies to evaluate the state of the cerebral autoregulatory capacity has clinical utility. Application of CVR studies in the clinical scenario depends on a thorough understanding of the normal response. The goal of this thesis therefore was to map CVR throughout the brain in normal healthy individuals using Blood Oxygen Level Dependant functional Magnetic Resonance Imaging (BOLD MRI) as an index to CBF and precisely controlled changes in end-tidal partial pressure of carbon dioxide (PETCO2) as the global flow stimulus.

Cerebral autoregulation is an intrinsic physiological response that maintains a constant cerebral blood flow (CBF) despite dynamic changes in the systemic blood pressure. Autoregulation is achieved through changes in the resistance of the small blood vessels in the brain through reflexive vasodilatation and vasoconstriction. Cerebrovascular reactivity (CVR) is a measure of this response. CVR is defined as a change in CBF in response to a given vasodilatory stimulus. CVR therefore potentially reflects the vasodilatory reserve capacity of the cerebral vasculature to maintain a constant cerebral blood flow. A decrease in CVR (which is interpreted as a reduction in the vasodilatory reserve capacity) in the vascular territory downstream of a larger stenosed supply artery correlates strongly with the risk of a hemodynamic stroke. As a result, the use of CVR studies to evaluate the state of the cerebral autoregulatory capacity has clinical utility. Application of CVR studies in the clinical scenario depends on a thorough understanding of the normal response. The goal of this thesis therefore was to map CVR throughout the brain in normal healthy individuals using Blood Oxygen Level Dependant functional Magnetic Resonance Imaging (BOLD MRI) as an index to CBF and precisely controlled changes in end-tidal partial pressure of carbon dioxide (PETCO2) as the global flow stimulus.

The etiology of renal dysfunction in sepsis is currently attributed to altered perfusion, microcirculatory abnormalities and cellular alterations. To clarify these mechanisms, we characterized the changes in renal perfusion and cortex metabolism in a large animal model of sepsis. In this model, sepsis was associated with metabolic alterations that may reflect early induction of cortical glycolysis. Septic shock was associated with reduced renal perfusion and decreased cortical and medullary blood flow, followed by signs of anaerobic metabolism in the cortex when flow reductions became critical. Attempts to correct renal hypoperfusion and alleviate the associated perfusion/metabolism mismatch with fenoldopam or renal denervation were unsuccessful. In the final study we focussed on the role of renal autoregulation in experimental sepsis and septic shock. Evidence suggests that higher blood pressure targets are needed in patients with impaired renal autoregulation and septic shock, but the effects of vasopressors should also be considered. We therefore investigated the effects of arginine vasopressin and norepinephrine on renal autoregulation in ovine septic shock. In experimental septic shock, arginine vasopressin was associated with a lower autoregulatory threshold than norepinephrine. As vasopressors may have different effects on renal autoregulation, individualized therapy of blood pressure management in patients with septic shock should take into account drug-specific effects. / Doctorat en Sciences médicales (Médecine) / info:eu-repo/semantics/nonPublished

Developement of a procedure to calculate intracranial pressure by means of arterial blood pressure and blood flow velocity in a big cerebral artery. Methods of systems theory are used. / Entwicklung eines Verfahrens zur Berechnung des Hirndrucks aus dem Bludrucksignal und der Blutströmungsgeschwindigkeit in einer großen Hirnarterie. Es werden Methoden der Systemtheorie verwendet.

Specification of floral organ identity in angiosperm flowers is accomplished by the coordinated activity of A-, B-, C-, and E-class MADS-box genes. In the eudicots, B-class genes specify petal and stamen identity. This eudicot B-class function depends on the simultaneous expression of genes from two paralogous B-class lineages (the DEFICIENS/APETALA3 lineage and the GLOBOSA/PISTILLATA lineage). Proteins produced by genes from these two lineages interact as obligate heterodimers and together regulate the transcription of various downstream targets. These obligate heterodimers also positively regulate the transcription of the B-class genes themselves, thereby mediating a unique B-class autoregulatory feedback loop. There is compelling evidence that B-class function at the phenotypic and molecular level is highly conserved among the eudicots. The degree to which B-class homeotic function, obligate heterodimerization, and autoregulation are conserved in non-eudicot, however, remains a topic of debate. Here we describe loss of function in Sterile tassel silky ear1 (Sts1) a maize ortholog of GLOBOSA/PISTILLATA formerly known as Zmm16. Mutation in Sts1 results in homeotic transformation of lodicules and stamens into bract-like organs in male inflorescences. Female inflorescences are affected in a similar manner. Stamens in these inflorescences are, however, transformed into carpels instead of into bract-like organs. This mutant phenotype suggests that Sts1 has a B-class homeotic function. Using qRT-PCR we also demonstrate that Sts1 participates in positive transcriptional regulation of all of the maize B-class genes. These findings suggest a high degree of B-class functional conservation between the monocots and the eudicots. Analysis of tasselseed1/sts1 and grassy tillers1/sts1 double mutants suggests that maize B-class genes also play a role in the sex determination process.

No description available.

<p dir="ltr">Impaired cerebral vasoregulation is one of the most common post-ischemic stroke effects. Diagnosis and prevention of this condition is often invasive, costly and in-effective. This impairment restricts the cerebral blood vessels to properly regulate blood flow, which is very important for normal brain functioning. Developing accurate, non-invasive and efficient methods to detect this condition aids in better stroke diagnosis and prevention. </p><p dir="ltr">The aim of this thesis is to develop deep learning techniques for the purpose of detection of cerebral vasoregulation impairments by analyzing physiological signals. This research employs various Deep learning techniques like Convolution Neural Networks (CNN), MobileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or non-stroke subjects. The effectiveness of these algorithms is demonstrated by a classification accuracy of 90\% for the combination of ECG and EMG signals. </p><p dir="ltr">Furthermore, this research explores the importance of analyzing dynamic physiological activities in determining the impairment. The dynamic activities include Sit-stand, Sit-stand-balance, Head-up-tilt, and Walk dataset from the PhysioNet website. CNN and MobileNetV3 are employed in classification purposes of these signals, attempting to identify cerebral health. The accuracy of the model and robustness of these methods is greatly enhanced when multiple signals are integrated. </p><p dir="ltr">Overall, this study highlights the potential of deep multimodal physiological learning in the development of precision brain medicine further enhancing stroke diagnosis. The results pave the way for the development of advanced diagnostic tools to determine cerebral health. </p>

Many cerebral diseases are associated with abnormal cerebral hemodynamics and impaired cerebral autoregulation (CA). CA is a mechanism to maintain cerebral blood flow (CBF) stable when mean arterial pressure (MAP) fluctuates. Evaluating these abnormalities requires direct measurements of cerebral hemodynamics and MAP. Several near-infrared diffuse optical instruments have been developed in our laboratory for hemodynamic measurements including near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), hybrid NIRS/DCS, and dual-wavelength DCS flow-oximeter. We utilized these noninvasive technologies to quantify CBF and cerebral oxygenation in different populations under different physiological conditions/manipulations. A commercial finger plethysmograph was used to continuously monitor MAP. For investigating the impact of obstructive sleep apnea (OSA) on cerebral hemodynamics and CA, a portable DCS device was used to monitor relative changes of CBF (rCBF) during bilateral thigh cuff occlusion. Compared to healthy controls, smaller reductions in rCBF and MAP following cuff deflation were observed in patients with OSA, which might result from the impaired vasodilation. However, dynamic CAs quantified in time-domain (defined by rCBF drop/MAP drop) were not significantly different between the two groups. We also evaluated dynamic CA in frequency-domain, i.e., to quantify the phase shifts of low frequency oscillations (LFOs) at 0.1 Hz between cerebral hemodynamics and MAP under 3 different physiological conditions (i.e., supine resting, head-up tilt (HUT), paced breathing). To capture dynamic LFOs, a hybrid NIRS/DCS device was upgraded to achieve faster sampling rate and better signal-to-noise. We determined the best hemodynamic parameters (i.e., CBF, oxygenated and total hemoglobin concentrations) among the measured variables and optimal physiological condition (HUT) for detecting LFOs in healthy subjects. Finally, a novel dual-wavelength DCS flow-oximeter was developed to monitor cerebral hemodynamics during HUT-induced vasovagal presyncope (VVS) in healthy subjects. rCBF was found to have the best sensitivity for the assessment of VVS among the measured variables and was likely the final trigger of VVS. A threshold of ~50% rCBF decline was observed which can completely separate subjects with or without presyncope, suggesting its potential role for predicting VVS. With further development and applications, NIRS/DCS techniques are expected to have significant impacts on the evaluation of cerebral hemodynamics and autoregulation.

Cerebral autoregulation describes the clinically observed phenomenon that cerebral blood flow remains relatively constant in healthy human subjects despite large systemic changes in blood pressure, dissolved blood gas concentrations, heart rate and other systemic variables. Cerebral autoregulation is known to be impaired post ischaemic stroke, after severe head injury, in patients suffering from autonomic dysfunction and under the action of various drugs. Cerebral auto-regulation is a dynamic, multivariate phenomenon. Sensitive techniques are required to monitor cerebral auto-regulation in a clinical setting. This thesis presents 4 related signal processing studies of cerebral autoregulation. The first study shows how consideration of changes in blood gas concentrations simultaneously with changes in blood pressure can improve the accuracy of an existing frequency domain technique for monitoring cerebral autoregulation from spontaneous fluctuations in blood pressure and a transcranial doppler measure of cerebral blood flow velocity. The second study shows how the continuous wavelet transform can be used to investigate coupling between blood pressure and near infrared spectroscopy measures of cerebral haemodynamics in patients with autonomic failure. This introduces time information into the frequency based assessment, however neglects the contribution of blood gas concentrations. The third study shows how this limitation can be resolved by introducing a new time-varying multivariate system identification algorithm based around the dual tree undecimated wavelet transform. All frequency and time-frequency domain methods of monitoring cerebral autoregulation assume linear coupling between the variables under consideration. The fourth study therefore considers nonlinear techniques of monitoring cerebral autoregulation, and illustrates some of the difficulties inherent in this form of analysis. The general approach taken in this thesis is to formulate a simple system model; usually in the form of an ODE or a stochastic process. The form of the model is adapted to encapsulate a hypothesis about features of cerebral autoregulation, particularly those features that may be difficult to recover using existing methods of analysis. The performance of the proposed method of analysis is then evaluated under these conditions. After this testing, the techniques are then applied to data provided by the Laboratory of Human Cerebrovascular Physiology in Alberta, Canada, and the National Hospital for Neurology and Neurosurgery in London, UK.