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The architecture of coastal sequences in tectonically-active regions results mostly from a combination of sea-level and land-level changes. The objective of this study is to unravel these signals by combining sequence stratigraphy and sedimentology of near-shore sedimentary sequences in wave-built terraces. We focus on Santa Maria Island at the south-central Chile margin, which hosts excellent exposures of coastal sediments from Marine Isotope Stage 3. A novel method based on statistical analysis of grain-size distributions coupled with fades descriptions provided a detailed account of transgressive-regressive cycles. Radiocarbon ages from paleosols constrain the chronology between >53 and similar to 31 cal ka BP. Because the influence of glaciations can be neglected, we calculated relative sea-level curves by tying the onset of deposition on a bedrock abrasion platform to a global sea-level curve. The observed depositional cycles match those predicted for uplift rates between 1.2 and 1.8 m/ka. The studied sedimentary units represent depositional cycles that resulted in reoccupation events of an existing marine terrace. Our study demonstrates wave-built marine terrace deposits along clastic shorelines in temperate regions can be used to distinguish between tectonic uplift and climate-induced sea-level changes.
We study the possibility of obtaining a computational turbulence model by means of non-dissipative regularisation of the compressible atmospheric equations for climate-type applications. We use an -regularisation (Lagrangian averaging) of the atmospheric equations. For the hydrostatic and compressible atmospheric equations discretised using a finite volume method on unstructured grids, deterministic and non-deterministic numerical experiments are conducted to compare the individual solutions and the statistics of the regularised equations to those of the original model. The impact of the regularisation parameter is investigated. Our results confirm the principal compatibility of -regularisation with atmospheric dynamics and encourage further investigations within atmospheric model including complex physical parametrisations.
We selected the immunogenic cell wall beta-(1,3)-glucosyltransferase Bgl2p from Candida albicans as a target protein for the production of antibodies. We identified a unique peptide sequence in the protein and generated monoclonal anti- C. albicans Bgl2p antibodies, which bound in particular to whole C. albicans cells.
We find necessary conditions for a second order ordinary differential equation to be equivalent to the Painleve III equation under a general point transformation. Their sufficiency is established by reduction to known results for the equations of the form y ' = f (x, y). We consider separately the generic case and the case of reducibility to an autonomous equation. The results are illustrated by the primary resonance equation.
Poly(N-propargyl glycine) (PNPG) can be readily prepared by ring-opening polymerization of N-propargyl glycine N-carboxyanhydride (NCA) and modified using various addition reactions such as copper catalyzed [3+2] cycloaddition of azide, radical (photo-)addition of thiol, nucleophilic addition of ethylene oxide, and thermal induced cross-linking. It is demonstrated that PNPG can serve as a modular platform to produce a bibliography of novel functional polypeptoid or pseudopeptide materials, including polypeptoid ionic liquids and graft copolymers.
Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers in ppfolio, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.
In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision-making. Computational neuroscientists have developed biologically-inspired models of visual attention, termed saliency maps, which successfully predict where people fixate on average. Using point process theory for spatial statistics, we show that scanpaths contain, however, important statistical structure, such as spatial clustering on top of distributions of gaze positions. Here, we develop a dynamical model of saccadic selection that accurately predicts the distribution of gaze positions as well as spatial clustering along individual scanpaths. Our model relies on activation dynamics via spatially-limited (foveated) access to saliency information, and, second, a leaky memory process controlling the re-inspection of target regions. This theoretical framework models a form of context-dependent decision-making, linking neural dynamics of attention to behavioral gaze data.