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The Department of Natural Resource Funding , operating under the FIP applied title Natural Resources Canada , is the ministry of the government of Canada responsible for natural resources, energy, minerals and metals, forests, earth science, mapping and remote sensing. It was created in 1995 by amalgamating the now-defunct Departments of Energy, Mines and Resources and Forestry. Natural Resources Canada works to ensure the responsible development of Canada's natural resources, including energy, forests, minerals and metals. NRCan also uses its expertise in earth science to build and maintain an up-to-date knowledge base of our landmass and resources." To promote internal collaboration, NRCan has implemented a departmental wide wiki based on MediaWiki. Natural Resources Canada also collaborates with American and Mexican government scientists, along with the Commission for Environmental Cooperation, to produce the North American Environmental Atlas, which is used to depict and track environmental issues for a continental perspective.Under the Canadian constitution, responsibility for natural resources belongs to the provinces, not the federal government. However, the federal government has jurisdiction over off-shore resources, trade and commerce in natural resources, statistics, international relations, and boundaries. The current Minister of Natural Resources is Greg Rickford as of March 2014.The department is governed by the Resources and Technical Surveys Act, R.S.C., c.R-7 and the Department of Natural Resources Act, S.C. 1994, c. 41."structured along business lines according to types of natural resources and areas of interest." The department currently has these sectors: Canadian Forest Service Corporate Management and Services Sector Earth science Sector Energy Sector Innovation and Energy Technology Sector Minerals and Metals Sector Science and Policy Integration Public Affairs and Portfolio Management Sector Shared Services Office Geographical Names Board of Canada↑ ↑ ↑ Wikipedia.


Rhainds M.,Natural Resources Canada
Entomologia Experimentalis et Applicata | Year: 2010

Empirical and experimental studies reporting the probability that some females remain unmated in field populations of insects (defined herein as mating failures) are reviewed in more than 100 species. The techniques used to quantify mating failures in the field are summarized, as well as factors that influence the probability that females mate during their lifetime. The existing empirical data provide partial support for hypotheses generated by theoretical models, although the trends observed in field populations are far more diverse and complex than predictions derived from ecological theory, e.g., the effect of population density on female mating success at small and large spatial scales is opposite. Mating success of females increases with the ratio of males in the population, but the relation between emergence time, sex ratio, and female mating success is variable. Females have evolved a broad range of physiological and behavioural adaptations to reduce mating failures, and exhibit a flexible context-dependent response to constraints limiting mating success. The large number of studies in Lepidoptera suggests a higher mating success in butterflies than moths. Examples of high rates of mating failure include species with gynogenous reproduction, long range migration, pre-reproductive maturation, male-biased sex ratio, acquisition of resources essential for reproduction, and female flightlessness. Species with sessile females that mate and oviposit near their emergence site provide model systems to investigate the causes and demographic consequences of female mating failure. © 2010 The Author. Journal compilation © 2010 The Netherlands Entomological Society. Source


Hamel L.-P.,Universite de Sherbrooke | Sheen J.,Harvard University | Seguin A.,Natural Resources Canada
Trends in Plant Science | Year: 2014

Calcium-dependent protein kinases (CDPKs) are multifunctional proteins that combine calcium-binding and signaling capabilities within a single gene product. This unique versatility enables multiple plant biological processes to be controlled, including developmental programs and stress responses. The genome of flowering plants typically encodes around 30 CDPK homologs that cluster in four conserved clades. In this review, we take advantage of the recent availability of genome sequences from green algae and early land plants to examine how well the previously described CDPK family from angiosperms compares to the broader evolutionary states associated with early diverging green plant lineages. Our analysis suggests that the current architecture of the CDPK family was shaped during the colonization of the land by plants, whereas CDPKs from ancestor green algae have continued to evolve independently. © 2013. Source


van Frankenhuyzen K.,Natural Resources Canada
Journal of Invertebrate Pathology | Year: 2013

The increasing number of Bacillus thuringiensis proteins with pesticidal activities across orders and phyla raises the question how widespread cross-activities are and if they are of sufficient biological significance to have implications for ecological safety of those proteins in pest control applications. Cross-activity is reported for 27 proteins and 69 taxa and is substantiated by reasonable evidence (mortality estimates) in 19 cases involving 45 taxa. Cross-activities occur in 13 primary rank families across three classes of pesticidal proteins (Cry, Cyt and Vip), and comprise 13 proteins affecting species across two orders, five proteins affecting three orders and one protein affecting four orders, all within the class Insecta. Cross-activity was quantified (LC50 estimates) for 16 proteins and 25 taxa. Compared to toxicity ranges established for Diptera-, Coleoptera-, Lepidoptera- and Nematoda-active proteins, 13 cross-activities are in the low-toxicity range (10-1000μg/ml), 12 in the medium - (0.10-10μg/ml) and two in the high-toxicity range (0.01-0.10μg/ml). Although cross-activities need to be viewed with caution until they are confirmed through independent testing, current evidence suggests that cross-activity of B. thuringiensis pesticidal proteins needs to be taken into consideration when designing and approving their use in pest control applications. © 2013 . Source


Currently, two basic models describe the genesis of the Caribbean Plate: (i) a Pacific model that derives the Caribbean Plate off southern Mexico and (ii) an in situ model. The Pacific model requires the 1100-1400 km sinistral displacement recorded across the Cayman Trough to pass through the Gulf of Tehuantepec into the Middle America Trench, but no evidence of such a connection exists. The in situ model is inconsistent with the 1100-1400 km displacement across the Cayman Trough. A way through this impasse is indicated by the northwestward curvature of active oblique reverse to sinistral transcurrent faulting in southeast Mexico. Extending this potential solution back to ca. 80 Ma forms the basis of the new Pirate model, in which the Caribbean Plate and the Chortis and Chiapas blocks are derived from the northwest by anticlockwise rotation during the latest Cretaceous and Cenozoic. Following passage of the Chortis Block, the northern and southern parts of the Yucatan block collided along the intra-Yucatan suture, producing the 11-9 Ma Chiapas fold-and-thrust belt. The Pirate model accounts for the N-trending segment of the Laramide Sierra Madre Oriental-Zongolica foldbelts by anticlockwise drag, Palaeogene palaeocanyons, the second, 66-40 Ma phase of rifting in the western Gulf of Mexico, and post-10 Ma extension in the Chortis Block (Chortis-Sula rift province). Impingement of the East Pacific Rise on the Middle America Trench led to modification of the Pirate model involving subduction erosion of the 200 km-wide, Eocene-Oligocene forearc at ca. 25 Ma, opening of the Gulf of California at ca. 6 Ma, and birth and ESE movement of the Southern Mexico block (<5 Ma) followed by its fragmentation. The Pirate mechanism indicates that the North American Plate is relatively weak and so tears and rotates into the trailing edge of the Caribbean Plate. © 2012 Copyright Taylor and Francis Group, LLC. Source


Rutledge R.G.,Natural Resources Canada
PLoS ONE | Year: 2011

Background: Linear regression of efficiency (LRE) introduced a new paradigm for real-time qPCR that enables large-scale absolute quantification by eliminating the need for standard curves. Developed through the application of sigmoidal mathematics to SYBR Green I-based assays, target quantity is derived directly from fluorescence readings within the central region of an amplification profile. However, a major challenge of implementing LRE quantification is the labor intensive nature of the analysis. Findings: Utilizing the extensive resources that are available for developing Java-based software, the LRE Analyzer was written using the NetBeans IDE, and is built on top of the modular architecture and windowing system provided by the NetBeans Platform. This fully featured desktop application determines the number of target molecules within a sample with little or no intervention by the user, in addition to providing extensive database capabilities. MS Excel is used to import data, allowing LRE quantification to be conducted with any real-time PCR instrument that provides access to the raw fluorescence readings. An extensive help set also provides an in-depth introduction to LRE, in addition to guidelines on how to implement LRE quantification. Conclusions: The LRE Analyzer provides the automated analysis and data storage capabilities required by large-scale qPCR projects wanting to exploit the many advantages of absolute quantification. Foremost is the universal perspective afforded by absolute quantification, which among other attributes, provides the ability to directly compare quantitative data produced by different assays and/or instruments. Furthermore, absolute quantification has important implications for gene expression profiling in that it provides the foundation for comparing transcript quantities produced by any gene with any other gene, within and between samples. © 2011 Robert G. Rutledge. Source

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