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Structure dissection of zebrafish progranulins identifies a well-folded granulin/epithelin module protein with pro-cell survival activities
No full textThe ancient and pluripotent progranulins contain multiple repeats of a cysteine\u2010rich sequence motif of 3c60 amino acids, called the granulin/epithelin module (GEM) with a prototypic structure of four \u3b2\u2010hairpins zipped together by six inter\u2010hairpin disulfide bonds. Prevalence of this disulfide\u2010enforced structure is assessed here by an expression screening of 19 unique GEM sequences of the four progranulins in the zebrafish genome, progranulins 1, 2, A and B. While a majority of the expressed GEM peptides did not exhibit uniquely folded conformations, module AaE from progranulin A and AbB from progranulin B were found to fold into the protopypic 4\u2010hairpin structure along with disulfide formation. Module AaE has the most\u2010rigid three\u2010dimensional structure with all four \u3b2\u2010hairpins defined using high\u2010resolution (H\u201315N) NMR spectroscopy, including 492 inter\u2010proton nuclear Overhauser effects, 23 3J(HN,H\u3b1) coupling constants, 22 hydrogen bonds as well as 45 residual dipolar coupling constants. Three\u2010dimensional structure of AaE and the partially folded AbB re\u2010iterate the conformational stability of the N\u2010terminal stack of two beta\u2010hairpins and varying degrees of structural flexibility for the C\u2010terminal half of the 4\u2010hairpin global fold of the GEM repeat. A cell\u2010based assay demonstrated a functional activity for the zebrafish granulin AaE in promoting the survival of neuronal cells, similarly to what has been found for the corresponding granulin E module in human progranulin. Finally, this work highlights the remaining challenges in structure\u2010activity studies of proteins containing the GEM repeats, due to the apparent prevalence of structural disorder in GEM motifs despite potentially a high density of intramolecular disulfide bonds.Peer reviewed: YesNRC publication: Ye
Web-based geospatial decision support system to facilitate marine renewable energy site selection in British Columbia, Canada
No full textThe authors present the British Columbia Marine Energy Resource Atlas (hereafter referred to as \u201cthe Atlas\u201d), a web-based geospatial decision support system (DSS) to facilitate preliminary marine renewable energy (MRE) site selection and feasibility investigations within the rivers and coastal waters of Canada\u2019s westernmost province. The Atlas was developed such that users are able to interact with the underlying datasets that drive the hotspot delineation. As such, the Atlas is an effective tool to quickly investigate multiple case-scenarios with different resource, socio-economic, and environmental criteria. Furthermore, the Atlas is the first MRE DSS to offer support for tidal, wave, and river hydrokinetic resources under a common system and interface.Peer reviewed: YesNRC publication: Ye
Application of organics for infrastructure construction on permafrost and community resiliency in northern Canada
No full textPeer reviewed: NoNRC publication: Ye
Experimental study of velocity deficit and wake due to single and multiple rectangular porous plates in steady flow
No full textPeer reviewed: YesNRC publication: Ye
Water/ice loads and scour at Canadian bridges in a changing climate: state of practice review
No full textPeer reviewed: NoNRC publication: Ye
Comparison of large woody debris prototypes in a large scale non-flume physical model
No full textDue to excessive rainfall in June of 2013, several rivers located in and near the City of Calgary, Canada experienced significant flooding events. These events caused severe damage to infrastructure throughout the city, precipitating a renewed interest in flood control and mitigation strategies for the area. A major potential strategy involves partial diversion of Elbow River flood water to the proposed Springbank Off-Stream Storage Reservoir. A large scale physical model study was conducted to optimize and validate the design of a portion of the new project. The goals of the physical model were to investigate diversion system behaviors such as flow rates, water levels, sediment transport and, debris accumulation, and optimize the design of new flow control structures to be constructed on the Elbow River. In order to accurately represent the behavior of debris within the system due to flooding, large woody debris created from natural sources was utilized in the physical model and its performance was compared to that of debris of the same size fabricated from pressed cylindrical wood dowels. In addition to comparing the performance of these two debris types, the impact of root wads on debris damming was also investigated. Significant differences in damming behavior was shown to exist between the natural debris and the fabricated debris, while the impact of root wad on damming affected the dam structure and formation. The results of this experiment indicate that natural debris is preferred for studies involving debris accumulation.Peer reviewed: YesNRC publication: Ye
Machine learning methods for analysis of metabolic data and metabolic pathway modeling
No full textMachine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.Peer reviewed: YesNRC publication: Ye
Connection between phase diagram, structure and ion transport in liquid, aqueous electrolyte solutions of lithium chloride
No full textThe nature of structure and ion transport in liquid electrolyte solutions are still not fully understood over the whole concentration range. In this work, we have studied aqueous solutions of lithium chloride as a model salt due to its very high solubility in water and ample knowledge of its structure and physiochemical properties. We have analyzed the ionic conductivity (\u3ba) vs. concentration (C) plots based on free volume approach and our recently developed equation: \u3ba = AC\u2009exp[ 12 BC] and conductivity vs. temperature plots based on Arrhenius equation. We find that the solutions show little variation in free volume with concentration, or even temperature, but a rapid increase in activation energy and pre-exponential factor. We relate the significant changes in conductivity to changes in structure and transport in the solutions and connect them to the binary LiCl/H2O phase diagram. We hypothesize that the changes are caused by a breakdown of the bulk water structure near the eutectic composition that causes a change in transport mechanism. We believe that this connection between solution structure, ion transport and phase diagram is common in most aqueous and non-aqueous electrolyte solutions and explains the origin of maximum in conductivity in isothermal conductivity vs. concentration plots.Peer reviewed: YesNRC publication: Ye