Auxiliary material and basic geochemical and benthic foraminiferal stable isotope data for the interval bracketing Eocene Thermal Maximum 2 (ETM2) at DSDP Sites 401 and 550 in the northeastern Atlantic Ocean

Ever since its discovery, Eocene Thermal Maximum 2 (ETM2; ~53.7 Ma) has been considered as one of the "little brothers" of the Paleocene-Eocene Thermal Maximum (PETM; ~56 Ma) as it displays similar characteristics including abrupt warming, ocean acidification, and biotic shifts. One of the remaining key questions is what effect these lesser climate perturbations had on ocean circulation and ventilation and, ultimately, biotic disruptions. Here we characterize ETM2 sections of the NE Atlantic (Deep Sea Drilling Project Sites 401 and 550) using multispecies benthic foraminiferal stable isotopes, grain size analysis, XRF core scanning, and carbonate content. The magnitude of the carbon isotope excursion (0.85-1.10 per mil) and bottom water warming (2-2.5°C) during ETM2 seems slightly smaller than in South Atlantic records. The comparison of the lateral d13C gradient between the North and South Atlantic reveals that a transient circulation switch took place during ETM2, a similar pattern as observed for the PETM. New grain size and published faunal data support this hypothesis by indicating a reduction in deepwater current velocity. Following ETM2, we record a distinct intensification of bottom water currents influencing Atlantic carbonate accumulation and biotic communities, while a dramatic and persistent clay reduction hints at a weakening of the regional hydrological cycle. Our findings highlight the similarities and differences between the PETM and ETM2. Moreover, the heterogeneity of hyperthermal expression emphasizes the need to specifically characterize each hyperthermal event and its background conditions to minimalize artifacts in global climate and carbonate burial models for the early Paleogene.

Parameter estimation in the presence of auxiliary information

Dissertação para obtenção do Grau de Doutora em Estatística e Gestão de Risco, Especialidade em Estatística

Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

A Data mining approach to indirect inference

Consider a model with parameter phi, and an auxiliary model with parameter theta. Let phi be a randomly sampled from a given density over the known parameter space. Monte Carlo methods can be used to draw simulated data and compute the corresponding estimate of theta, say theta_tilde. A large set of tuples (phi, theta_tilde) can be generated in this manner. Nonparametric methods may be use to fit the function E(phi|theta_tilde=a), using these tuples. It is proposed to estimate phi using the fitted E(phi|theta_tilde=theta_hat), where theta_hat is the auxiliary estimate, using the real sample data. This is a consistent and asymptotically normally distributed estimator, under certain assumptions. Monte Carlo results for dynamic panel data and vector autoregressions show that this estimator can have very attractive small sample properties. Confidence intervals can be constructed using the quantiles of the phi for which theta_tilde is close to theta_hat. Such confidence intervals are found to have very accurate coverage.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Analysis of the contribution of the β-oxidation auxiliary enzymes in the degradation of the dietary conjugated linoleic acid 9-cis-11-trans-octadecadienoic acid in the peroxisomes of Saccharomyces cerevisiae

Beta-oxidation of the conjugated linoleic acid 9-cis,11-trans-octadecadienoic acid (rumenic acid) was analyzed in vivo in Saccharomyces cerevisiae by monitoring polyhydroxyalkanoate production in the peroxisome. Polyhydroxyalkanoate is synthesized by the polymerization of the beta-oxidation intermediates 3-hydroxyacyl-CoAs via a bacterial polyhydroxyalkanoate synthase targeted to the peroxisome. The amount of polyhydroxyalkanaote synthesized from the degradation of rumenic acid was found to be similar to the amount synthesized from the degradation of 10-trans,12-cis-octadecadienoic acid, oleic acid or 10-cis-heptadecenoic acid. Furthermore, the degradation of 10-cis-heptadecenoic acid was found to be unaffected by the presence of rumenic acid in the media. Efficient degradation of rumenic acid was found to be independent of the Delta(3,5),Delta(2,4)-dienoyl-CoA isomerase but instead relied on the presence of Delta(3),Delta(2)-enoyl-CoA isomerase activity. The presence of the unsaturated monomer 3-hydroxydodecenoic acid in polyhydroxyalkanoate derived from rumenic acid degradation was found to be dependent on the presence of a Delta(3),Delta(2)-enoyl-CoA isomerase activity. Together, these data indicate that rumenic acid is mainly degraded in vivo in S. cerevisiae through a pathway requiring only the participation of the auxiliary enzymes Delta(3),Delta(2)-enoyl-CoA isomerase, along with the enzyme of the core beta-oxidation cycle.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Analysis of the contribution of the beta-oxidation auxiliary enzymes in the degradation of the dietary conjugated linoleic acid 9-cis-11-trans-octadecadienoic acid in the peroxisomes of Saccharomyces cerevisiae.

Beta-oxidation of the conjugated linoleic acid 9-cis,11-trans-octadecadienoic acid (rumenic acid) was analyzed in vivo in Saccharomyces cerevisiae by monitoring polyhydroxyalkanoate production in the peroxisome. Polyhydroxyalkanoate is synthesized by the polymerization of the beta-oxidation intermediates 3-hydroxyacyl-CoAs via a bacterial polyhydroxyalkanoate synthase targeted to the peroxisome. The amount of polyhydroxyalkanaote synthesized from the degradation of rumenic acid was found to be similar to the amount synthesized from the degradation of 10-trans,12-cis-octadecadienoic acid, oleic acid or 10-cis-heptadecenoic acid. Furthermore, the degradation of 10-cis-heptadecenoic acid was found to be unaffected by the presence of rumenic acid in the media. Efficient degradation of rumenic acid was found to be independent of the Delta(3,5),Delta(2,4)-dienoyl-CoA isomerase but instead relied on the presence of Delta(3),Delta(2)-enoyl-CoA isomerase activity. The presence of the unsaturated monomer 3-hydroxydodecenoic acid in polyhydroxyalkanoate derived from rumenic acid degradation was found to be dependent on the presence of a Delta(3),Delta(2)-enoyl-CoA isomerase activity. Together, these data indicate that rumenic acid is mainly degraded in vivo in S. cerevisiae through a pathway requiring only the participation of the auxiliary enzymes Delta(3),Delta(2)-enoyl-CoA isomerase, along with the enzyme of the core beta-oxidation cycle.

Using computational fluid dynamics to predict head losses in the auxiliary elements of a microirrigation sand filter

It is often assumed that total head losses in a sand filter are solely due to the filtration media and that there are analytical solutions, such as the Ergun equation, to compute them. However, total head losses are also due to auxiliary elements (inlet and outlet pipes and filter nozzles), which produce undesirable head losses because they increase energy requirements without contributing to the filtration process. In this study, ANSYS Fluent version 6.3, a commercial computational fluid dynamics (CFD) software program, was used to compute head losses in different parts of a sand filter. Six different numerical filter models of varying complexities were used to understand the hydraulic behavior of the several filter elements and their importance in total head losses. The simulation results show that 84.6% of these were caused by the sand bed and 15.4% were due to auxiliary elements (4.4% in the outlet and inlet pipes, and 11.0% in the perforated plate and nozzles). Simulation results with different models show the important role of the nozzles in the hydraulic behavior of the sand filter. The relationship between the passing area through the nozzles and the passing area through the perforated plate is an important design parameter for the reduction of total head losses. A reduced relationship caused by nozzle clogging would disproportionately increase the total head losses in the sand filter

Restarting automata with restricted utilization of auxiliary symbols

The restarting automaton is a restricted model of computation that was introduced by Jancar et al. to model the so-called analysis by reduction, which is a technique used in linguistics to analyze sentences of natural languages. The most general models of restarting automata make use of auxiliary symbols in their rewrite operations, although this ability does not directly correspond to any aspect of the analysis by reduction. Here we put restrictions on the way in which restarting automata use auxiliary symbols, and we investigate the influence of these restrictions on their expressive power. In fact, we consider two types of restrictions. First, we consider the number of auxiliary symbols in the tape alphabet of a restarting automaton as a measure of its descriptional complexity. Secondly, we consider the number of occurrences of auxiliary symbols on the tape as a dynamic complexity measure. We establish some lower and upper bounds with respect to these complexity measures concerning the ability of restarting automata to recognize the (deterministic) context-free languages and some of their subclasses.

Anonymisation, Deanonymisation and Data Management

Speaker: Dr Kieron O'Hara Organiser: Time: 04/02/2015 11:00-11:45 Location: B32/3077 Abstract In order to reap the potential societal benefits of big and broad data, it is essential to share and link personal data. However, privacy and data protection considerations mean that, to be shared, personal data must be anonymised, so that the data subject cannot be identified from the data. Anonymisation is therefore a vital tool for data sharing, but deanonymisation, or reidentification, is always possible given sufficient auxiliary information (and as the amount of data grows, both in terms of creation, and in terms of availability in the public domain, the probability of finding such auxiliary information grows). This creates issues for the management of anonymisation, which are exacerbated not only by uncertainties about the future, but also by misunderstandings about the process(es) of anonymisation. This talk discusses these issues in relation to privacy, risk management and security, reports on recent theoretical tools created by the UKAN network of statistics professionals (on which the author is one of the leads), and asks how long anonymisation can remain a useful tool, and what might replace it.

Testing Seasonal Unit Roots in Data at Any Frequency, an HEGY approach

This paper generalizes the HEGY-type test to detect seasonal unit roots in data at any frequency, based on the seasonal unit root tests in univariate time series by Hylleberg, Engle, Granger and Yoo (1990). We introduce the seasonal unit roots at first, and then derive the mechanism of the HEGY-type test for data with any frequency. Thereafter we provide the asymptotic distributions of our test statistics when different test regressions are employed. We find that the F-statistics for testing conjugation unit roots have the same asymptotic distributions. Then we compute the finite-sample and asymptotic critical values for daily and hourly data by a Monte Carlo method. The power and size properties of our test for hourly data is investigated, and we find that including lag augmentations in auxiliary regression without lag elimination have the smallest size distortion and tests with seasonal dummies included in auxiliary regression have more power than the tests without seasonal dummies. At last we apply the our test to hourly wind power production data in Sweden and shows there are no seasonal unit roots in the series.

Road network and GPS tracking with data processing and quality assessment

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

An auxiliary factor containing a 240-kDa protein complex is involved in apolipoprotein B RNA editing.

A protein complex involved in apolipoprotein B (apoB) RNA editing, referred to as AUX240 (auxiliary factor containing p240), has been identified through the production of monoclonal antibodies against in vitro assembled 27S editosomes. The 240-kDa protein antigen of AUX240 colocalized with editosome complexes on immunoblots of native gels. Immunoadsorbed extracts were impaired in their ability to assemble editosomes beyond early intermediates and in their ability to edit apoB RNA efficiently. Supplementation of adsorbed extract with AUX240 restored both editosome assembly and editing activities. Several proteins, in addition to p240, ranging in molecular mass from 150 to 45 kDa coimmunopurify as AUX240 under stringent wash conditions. The activity of the catalytic subunit of the editosome APOBEC-1 and mooring sequence RNA binding proteins of 66 and 44 kDa could not be demonstrated in AUX240. The data suggest that p240 and associated proteins constitute an auxiliary factor required for efficient apoB RNA editing. We propose that the role of AUX240 may be regulatory and involve mediation or stabilization of interactions between APOBEC-1 subunits and editing site recognition proteins leading the assembly of the rat liver C/U editosome.