Ex Vivo Smooth Muscle Pharmacological Effects of a Novel Bradykinin-Related Peptide, and Its Analogue, from Chinese Large Odorous Frog, Odorrana livida Skin Secretions

Bradykinin-related peptides (BRPs) are one of the most extensively studied frog secretions-derived peptide families identified from many amphibian species. The diverse primary structures of BRPs have been proven essential for providing valuable information in understanding basic mechanisms associated with drug modification. Here, we isolated, identified and characterized a dodeca-BRP (RAP-L1, T6-BK), with primary structure RAPLPPGFTPFR, from the skin secretions of Chinese large odorous frogs, Odorrana livida. This novel peptide exhibited a dose-dependent contractile property on rat bladder and rat ileum, and increased the contraction frequency on rat uterus ex vivo smooth muscle preparations; it also showed vasorelaxant activity on rat tail artery smooth muscle. In addition, the analogue RAP-L1, T6, L8-BK completely abolished these effects on selected rat smooth muscle tissues, whilst it showed inhibition effect on bradykinin-induced rat tail artery relaxation. By using canonical antagonist for bradykinin B1 or B2 type receptors, we found that RAP-L1, T6-BK -induced relaxation of the arterial smooth muscle was very likely to be modulated by B2 receptors. The analogue RAP-L1, T6, L8-BK further enhanced the bradykinin inhibitory activity only under the condition of co-administration with HOE140 on rat tail artery, suggesting a synergistic inhibition mechanism by which targeting B2 type receptors.

Large-scale dynamic hydrofracturing, healing and fracture network characterization

Permeability of a rock is a dynamic property that varies spatially and temporally. Fractures provide the most efficient channels for fluid flow and thus directly contribute to the permeability of the system. Fractures usually form as a result of a combination of tectonic stresses, gravity (i.e. lithostatic pressure) and fluid pressures. High pressure gradients alone can cause fracturing, the process which is termed as hydrofracturing that can determine caprock (seal) stability or reservoir integrity. Fluids also transport mass and heat, and are responsible for the formation of veins by precipitating minerals within open fractures. Veining (healing) thus directly influences the rock’s permeability. Upon deformation these closed factures (veins) can refracture and the cycle starts again. This fracturing-healing-refacturing cycle is a fundamental part in studying the deformation dynamics and permeability evolution of rock systems. This is generally accompanied by fracture network characterization focusing on network topology that determines network connectivity. Fracture characterization allows to acquire quantitative and qualitative data on fractures and forms an important part of reservoir modeling. This thesis highlights the importance of fracture-healing and veins’ mechanical properties on the deformation dynamics. It shows that permeability varies spatially and temporally, and that healed systems (veined rocks) should not be treated as fractured systems (rocks without veins). Field observations also demonstrate the influence of contrasting mechanical properties, in addition to the complexities of vein microstructures that can form in low-porosity and permeability layered sequences. The thesis also presents graph theory as a characterization method to obtain statistical measures on evolving network connectivity. It also proposes what measures a good reservoir should have to exhibit potentially large permeability and robustness against healing. The results presented in the thesis can have applications for hydrocarbon and geothermal reservoir exploration, mining industry, underground waste disposal, CO2 injection or groundwater modeling.

How institutions shaped the last major evolutionary transition to large-scale human societies.

What drove the transition from small-scale human societies centred on kinship and personal exchange, to large-scale societies comprising cooperation and division of labour among untold numbers of unrelated individuals? We propose that the unique human capacity to negotiate institutional rules that coordinate social actions was a key driver of this transition. By creating institutions, humans have been able to move from the default ‘Hobbesian’ rules of the ‘game of life’, determined by physical/environmental constraints, into self-created rules of social organization where cooperation can be individually advantageous even in large groups of unrelated individuals. Examples include rules of food sharing in hunter–gatherers, rules for the usage of irrigation systems in agriculturalists, property rights and systems for sharing reputation between mediaeval traders. Successful institutions create rules of interaction that are self-enforcing, providing direct benefits both to individuals that follow them, and to individuals that sanction rule breakers. Forming institutions requires shared intentionality, language and other cognitive abilities largely absent in other primates. We explain how cooperative breeding likely selected for these abilities early in the Homo lineage. This allowed anatomically modern humans to create institutions that transformed the self-reliance of our primate ancestors into the division of labour of large-scale human social organization.

All-regime Lagrangian-Remap numerical schemes for the gas dynamics equations. Applications to the large friction and low Mach coefficients

In this talk, we propose an all regime Lagrange-Projection like numerical scheme for the gas dynamics equations. By all regime, we mean that the numerical scheme is able to compute accurate approximate solutions with an under-resolved discretization with respect to the Mach number M, i.e. such that the ratio between the Mach number M and the mesh size or the time step is small with respect to 1. The key idea is to decouple acoustic and transport phenomenon and then alter the numerical flux in the acoustic approximation to obtain a uniform truncation error in term of M. This modified scheme is conservative and endowed with good stability properties with respect to the positivity of the density and the internal energy. A discrete entropy inequality under a condition on the modification is obtained thanks to a reinterpretation of the modified scheme in the Harten Lax and van Leer formalism. A natural extension to multi-dimensional problems discretized over unstructured mesh is proposed. Then a simple and efficient semi implicit scheme is also proposed. The resulting scheme is stable under a CFL condition driven by the (slow) material waves and not by the (fast) acoustic waves and so verifies the all regime property. Numerical evidences are proposed and show the ability of the scheme to deal with tests where the flow regime may vary from low to high Mach values.

High-sensitivity refractive index sensor based on large-angle tilted fiber grating with carbon nanotube deposition

This paper presents a highly sensitive ambient refractive index (RI) sensor based on 81° tilted fiber grating (81°-TFG) structure UV-inscribed in standard telecom fiber (62.5μm cladding radius) with carbon nanotube (CNT) overlay deposition. The sensing mechanism is based on the ability of CNT to induce change in transmitted optical power and the high sensitivity of 81°-TFG to ambient refractive index. The thin CNT film with high refractive index enhances the cladding modes of the TFG, resulting in the significant interaction between the propagating light and the surrounding medium. Consequently, the surrounding RI change will induce not only the resonant wavelength shift but also the power intensity change of the attenuation band in the transmission spectrum. Result shows that the change in transmitted optical power produces a corresponding linear reduction in intensity with increment in RI values. The sample shows high sensitivities of ∼207.38nm/RIU, ∼241.79nm/RIU at RI range 1.344-1.374 and ∼113.09nm/RIU, ∼144.40nm/RIU at RI range 1.374-1.392 (for X-pol and Y-pol respectively). It also shows power intensity sensitivity of ∼ 65.728dBm/RIU and ∼ 45.898 (for X-pol and Y-pol respectively). The low thermal sensitivity property of the 81°-TFG offers reduction in thermal cross-sensitivity and enhances specificity of the sensor.

Biological Impacts and Uses of Black Bass Competitive Fishing Tournaments on Large Lake Systems

Thesis (Master, Biology) -- Queen's University, 2016-09-28 15:06:46.124

Subfilter-scale Stress Modelling for Large Eddy Simulations

A subfilter-scale (SFS) stress model is developed for large-eddy simulations (LES) and is tested on various benchmark problems in both wall-resolved and wall-modelled LES. The basic ingredients of the proposed model are the model length-scale, and the model parameter. The model length-scale is defined as a fraction of the integral scale of the flow, decoupled from the grid. The portion of the resolved scales (LES resolution) appears as a user-defined model parameter, an advantage that the user decides the LES resolution. The model parameter is determined based on a measure of LES resolution, the SFS activity. The user decides a value for the SFS activity (based on the affordable computational budget and expected accuracy), and the model parameter is calculated dynamically. Depending on how the SFS activity is enforced, two SFS models are proposed. In one approach the user assigns the global (volume averaged) contribution of SFS to the transport (global model), while in the second model (local model), SFS activity is decided locally (locally averaged). The models are tested on isotropic turbulence, channel flow, backward-facing step and separating boundary layer. In wall-resolved LES, both global and local models perform quite accurately. Due to their near-wall behaviour, they result in accurate prediction of the flow on coarse grids. The backward-facing step also highlights the advantage of decoupling the model length-scale from the mesh. Despite the sharply refined grid near the step, the proposed SFS models yield a smooth, while physically consistent filter-width distribution, which minimizes errors when grid discontinuity is present. Finally the model application is extended to wall-modelled LES and is tested on channel flow and separating boundary layer. Given the coarse resolution used in wall-modelled LES, near the wall most of the eddies become SFS and SFS activity is required to be locally increased. The results are in very good agreement with the data for the channel. Errors in the prediction of separation and reattachment are observed in the separated flow, that are somewhat improved with some modifications to the wall-layer model.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.