1,829 research outputs found

    Effect of Bacteria on Airway Submucosal Glands Liquid Secretion in Swine

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    Cystic fibrosis (CF) is a genetic disorder caused by mutations in the gene encoding for the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel. Currently, more than 4,100 Canadians have CF. The major cause of CF morbidity and mortality is airway disease, for which there is no cure. The events leading from CFTR gene mutation to CF airway disease are not fully understood, and there is controversy regarding the primary defect responsible for CF airway disease pathogenesis. Newborn CFTRΔF508/ΔF508 and CFTR-/- swine show no sign of infection and inflammation in the lung but suffer from defective bacteria eradication caused by abnormal innate immune system. The cornerstone of the airway’s innate immune defense is mucociliary clearance, which relies on the normal regulation of airway surface liquid (ASL), which covers the airway epithelium. It has been hypothesized that abnormal ASL is the primary defect that leads to the failure of the airway innate immune defense in CF. Evidence show that the airway submucosal gland functions abnormally in both CF patients and in animal models of CF. This is not surprising since airway submucosal glands normally express CFTR. However, the function of the gland in health and disease is not fully understood. The response of airway submucosal gland to inhaled bacteria has never been tested and its ion transport properties have not been fully described. Our objective is to investigate the effect of inhaled bacteria on airway submucosal gland secretion, and to study and compare the function of different segments of airway submucosal gland in wild-type and CF airway. Knowledge generated by this thesis would help better understand CF airway pathophysiology and may contribute to improving treatment methods

    Dynamic dissipative cooling of a mechanical oscillator in strong-coupling optomechanics

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    Cooling of mesoscopic mechanical resonators represents a primary concern in cavity optomechanics. Here in the strong optomechanical coupling regime, we propose to dynamically control the cavity dissipation, which is able to significantly accelerate the cooling process while strongly suppressing the heating noise. Furthermore, the dynamic control is capable of overcoming quantum backaction and reducing the cooling limit by several orders of magnitude. The dynamic dissipation control provides new insights for tailoring the optomechanical interaction and offers the prospect of exploring macroscopic quantum physics.Comment: accepetd in Physical Review Letter

    An effective Denial of Service Attack Detection Method in Wireless Mesh Networks

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    AbstractIn order to detect the DoS attack (Denial-of-Service attack) when wireless mesh networks adopt AODV routing protocol of Ad Hoc networks. Such technologies as an end-to-end authentication, utilization rate of cache memory, two pre-assumed threshold value and distributed voting are used in this paper to detect DoS attacker, which is on the basic of hierarchical topology structure in wireless mesh networks. Through performance analysis in theory and simulations experiment, the scheme would improve the flexibility and accuracy of DoS attack detection, and would obviously improve its security in wireless mesh networks

    Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

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    The performance limit of Graph Convolutional Networks (GCNs) and the fact that we cannot stack more of them to increase the performance, which we usually do for other deep learning paradigms, are pervasively thought to be caused by the limitations of the GCN layers, including insufficient expressive power, etc. However, if so, for a fixed architecture, it would be unlikely to lower the training difficulty and to improve performance by changing only the training procedure, which we show in this paper not only possible but possible in several ways. This paper first identify the training difficulty of GCNs from the perspective of graph signal energy loss. More specifically, we find that the loss of energy in the backward pass during training nullifies the learning of the layers closer to the input. Then, we propose several methodologies to mitigate the training problem by slightly modifying the GCN operator, from the energy perspective. After empirical validation, we confirm that these changes of operator lead to significant decrease in the training difficulties and notable performance boost, without changing the composition of parameters. With these, we conclude that the root cause of the problem is more likely the training difficulty than the others
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