Geophysical evidence for the evolution of the California Inner Continental Borderland as a metamorphic core complex

Author Posting. © American Geophysical Union, 2000. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Solid Earth 105 (2000): 5835-5857, doi:10.1029/1999JB900318.We use new seismic and gravity data collected during the 1994 Los Angeles Region Seismic Experiment (LARSE) to discuss the origin of the California Inner Continental Borderland (ICB) as an extended terrain possibly in a metamorphic core complex mode. The data provide detailed crustal structure of the Borderland and its transition to mainland southern California. Using tomographic inversion as well as traditional forward ray tracing to model the wide-angle seismic data, we find little or no sediments, low (#6.6 km/s) P wave velocity extending down to the crust-mantle boundary, and a thin crust (19 to 23 km thick). Coincident multichannel seismic reflection data show a reflective lower crust under Catalina Ridge. Contrary to other parts of coastal California, we do not find evidence for an underplated fossil oceanic layer at the base of the crust. Coincident gravity data suggest an abrupt increase in crustal thickness under the shelf edge, which represents the transition to the western Transverse Ranges. On the shelf the Palos Verdes Fault merges downward into a landward dipping surface which separates “basement” from low-velocity sediments, but interpretation of this surface as a detachment fault is inconclusive. The seismic velocity structure is interpreted to represent Catalina Schist rocks extending from top to bottom of the crust. This interpretation is compatible with a model for the origin of the ICB as an autochthonous formerly hot highly extended region that was filled with the exhumed metamorphic rocks. The basin and ridge topography and the protracted volcanism probably represent continued extension as a wide rift until ;13 m.y. ago. Subduction of the young and hot Monterey and Arguello microplates under the Continental Borderland, followed by rotation and translation of the western Transverse Ranges, may have provided the necessary thermomechanical conditions for this extension and crustal inflow.The LARSE experiment was funded by NSF EAR-9416774, the U.S. Geological Survey’s Earthquake Hazards and Coastal and Marine Programs, and by the Southern California Earthquake Center (SCEC)

Results of 1992 seismic reflection experiment in Lake Baikal

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95684/1/eost9857.pd

Images of Crust Beneath Southern California Will Aid Study of Earthquakes and Their Effects

The Whittier Narrows earthquake of 1987 and the Northridge earthquake of 1991 highlighted the earthquake hazards associated with buried faults in the Los Angeles region. A more thorough knowledge of the subsurface structure of southern California is needed to reveal these and other buried faults and to aid us in understanding how the earthquake-producing machinery works in this region

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved