First Results with the RISING active stopper

This paper outlines some of the physics opportunities available with the GSI RISING active stopper and presents preliminary results from an experiment aimed at performing beta-delayed gamma-ray spectroscopic studies in heavy-neutron-rich nuclei produced following the projectile fragmentation of a 1 GeV per nucleon 208Pb primary beam. The energy response of the silicon active stopping detector for both heavy secondary fragments and beta-particles is demonstrated and preliminary results on the decays of neutron-rich Tantalum (Ta) to Tungsten (W) isotopes are presented as examples of the potential of this technique to allow new structural studies in hitherto experimentally unreachable heavy, neutron-rich nuclei. The resulting spectral information inferred from excited states in the tungsten daughter nuclei are compared with results from axially symmetric Hartree–Fock calculations of the nuclear shape and suggest a change in ground state structure for the N = 116 isotone 190W compared to the lighter isotopes of this element.

Probabilistic skill in ensemble seasonal forecasts

Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add, either to purely empirical statistical models or to simpler simulation models. An evaluation of seasonal probability forecasts from the Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction (DEMETER) and ENSEMBLES multi-model ensemble experiments is presented. Two particular regions are considered: Nino3.4 in the Pacific and the Main Development Region in the Atlantic; these regions were chosen before any spatial distribution of skill was examined. The ENSEMBLES models are found to have skill against the climatological distribution on seasonal time-scales. For models in ENSEMBLES that have a clearly defined predecessor model in DEMETER, the improvement from DEMETER to ENSEMBLES is discussed. Due to the long lead times of the forecasts and the evolution of observation technology, the forecast-outcome archive for seasonal forecast evaluation is small; arguably, evaluation data for seasonal forecasting will always be precious. Issues of information contamination from in-sample evaluation are discussed and impacts (both positive and negative) of variations in cross-validation protocol are demonstrated. Other difficulties due to the small forecast-outcome archive are identified. The claim that the multi-model ensemble provides a ‘better’ probability forecast than the best single model is examined and challenged. Significant forecast information beyond the climatological distribution is also demonstrated in a persistence probability forecast. The ENSEMBLES probability forecasts add significantly more information to empirical probability forecasts on seasonal time-scales than on decadal scales. Current operational forecasts might be enhanced by melding information from both simulation models and empirical models. Simulation models based on physical principles are sometimes expected, in principle, to outperform empirical models; direct comparison of their forecast skill provides information on progress toward that goal.

New insights into the structure of exotic nuclei using the RISING active stopper

This conference paper outlines the operation and some of the preliminary physics results using the GSI RISING active stopper. Data are presented from an experiment using combined isomer and beta‐delayed gamma‐ray spectroscopy to study low‐lying spectral and decay properties of heavy‐neutron‐rich nuclei around A∼190 produced following the relativistic projectile fragmentation of 208Pb primary beam. The response of the RISING active stopper detector is demonstrated for both the implantation of heavy secondary fragments and in‐situ decay of beta‐particles. Beta‐delayed gamma‐ray spectroscopy following decays of the neutron‐rich nucleus 194Re is presented to demonstrate the experimental performance of the set‐up. The resulting information inferred from excited states in the W and Os daughter nuclei is compared with results from Skyrme Hartree‐Fock predictions of the evolution of nuclear shape.

TESSA - Toolkit for experimentation with multimodal sensory substitution and augmentation

TESSA is a toolkit for experimenting with sensory augmentation. It includes hardware and software to facilitate rapid prototyping of interfaces that can enhance one sense using information gathered from another sense. The toolkit contains a range of sensors (e.g. ultrasonics, temperature sensors) and actuators (e.g. tactors or stereo sound), designed modularly so that inputs and outputs can be easily swapped in and out and customized using TESSA’s graphical user interface (GUI), with “real time” feedback. The system runs on a Raspberry Pi with a built-in touchscreen, providing a compact and portable form that is amenable for field trials. At CHI Interactivity, the audience will have the opportunity to experience sensory augmentation effects using this system, and design their own sensory augmentation interfaces.

Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest

We utilized an ecosystem process model (SIPNET, simplified photosynthesis and evapotranspiration model) to estimate carbon fluxes of gross primary productivity and total ecosystem respiration of a high-elevation coniferous forest. The data assimilation routine incorporated aggregated twice-daily measurements of the net ecosystem exchange of CO2 (NEE) and satellite-based reflectance measurements of the fraction of absorbed photosynthetically active radiation (fAPAR) on an eight-day timescale. From these data we conducted a data assimilation experiment with fifteen different combinations of available data using twice-daily NEE, aggregated annual NEE, eight-day f AP AR, and average annual fAPAR. Model parameters were conditioned on three years of NEE and fAPAR data and results were evaluated to determine the information content from the different combinations of data streams. Across the data assimilation experiments conducted, model selection metrics such as the Bayesian Information Criterion and Deviance Information Criterion obtained minimum values when assimilating average annual fAPAR and twice-daily NEE data. Application of wavelet coherence analyses showed higher correlations between measured and modeled fAPAR on longer timescales ranging from 9 to 12 months. There were strong correlations between measured and modeled NEE (R2, coefficient of determination, 0.86), but correlations between measured and modeled eight-day fAPAR were quite poor (R2 = −0.94). We conclude that this inability to determine fAPAR on eight-day timescale would improve with the considerations of the radiative transfer through the plant canopy. Modeled fluxes when assimilating average annual fAPAR and annual NEE were comparable to corresponding results when assimilating twice-daily NEE, albeit at a greater uncertainty. Our results support the conclusion that for this coniferous forest twice-daily NEE data are a critical measurement stream for the data assimilation. The results from this modeling exercise indicate that for this coniferous forest, average annuals for satellite-based fAPAR measurements paired with annual NEE estimates may provide spatial detail to components of ecosystem carbon fluxes in proximity of eddy covariance towers. Inclusion of other independent data streams in the assimilation will also reduce uncertainty on modeled values.

MOLES3: implementing an ISO standards driven data catalogue

ISO19156 Observations and Measurements (O&M) provides a standardised framework for organising information about the collection of information about the environment. Here we describe the implementation of a specialisation of O&M for environmental data, the Metadata Objects for Linking Environmental Sciences (MOLES3). MOLES3 provides support for organising information about data, and for user navigation around data holdings. The implementation described here, “CEDA-MOLES”, also supports data management functions for the Centre for Environmental Data Archival, CEDA. The previous iteration of MOLES (MOLES2) saw active use over five years, being replaced by CEDA-MOLES in late 2014. During that period important lessons were learnt both about the information needed, as well as how to design and maintain the necessary information systems. In this paper we review the problems encountered in MOLES2; how and why CEDA-MOLES was developed and engineered; the migration of information holdings from MOLES2 to CEDA-MOLES; and, finally, provide an early assessment of MOLES3 (as implemented in CEDA-MOLES) and its limitations. Key drivers for the MOLES3 development included the necessity for improved data provenance, for further structured information to support ISO19115 discovery metadata export (for EU INSPIRE compliance), and to provide appropriate fixed landing pages for Digital Object Identifiers (DOIs) in the presence of evolving datasets. Key lessons learned included the importance of minimising information structure in free text fields, and the necessity to support as much agility in the information infrastructure as possible without compromising on maintainability both by those using the systems internally and externally (e.g. citing in to the information infrastructure), and those responsible for the systems themselves. The migration itself needed to ensure continuity of service and traceability of archived assets.

Single embryo and oocyte lipid fingerprinting by mass spectrometry

Methods used for lipid analysis in embryos and oocytes usually involve selective lipid extraction from a pool of many samples followed by chemical manipulation, separation and characterization of individual components by chromatographic techniques. Herein we report direct analysis by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) of single and intact embryos or oocytes from various species. Biological samples were simply moisturized with the matrix solution and characteristic lipid ( represented by phosphatidylcholines, sphingomyelins and triacylglycerols) profiles were obtained via MALDI-MS. As representative examples, human, bovine, sheep and fish oocytes, as well as bovine and insect embryos were analyzed. MALDI-MS is shown to be capable of providing characteristic lipid profiles of gametes and embryos and also to respond to modifications due to developmental stages and in vitro culture conditions of bovine embryos. Investigation in developmental biology of the biological roles of structural and reserve lipids in embryos and oocytes should therefore benefit from these rapid MALDI-MS profiles from single and intact species.-Ferreira, C. R., S. A. Saraiva, R. R. Catharino, J. S. Garcia, F. C. Gozzo, G. B. Sanvido, L. F. A. Santos, E. G. Lo Turco, J. H. F. Pontes, A. C. Basso, R. P. Bertolla, R. Sartori, M. M. Guardieiro, F. Perecin, F. V. Meirelles, J. R. Sangalli, and M. N. Eberlin. Single embryo and oocyte lipid fingerprinting by mass spectrometry. J. Lipid Res. 2010. 51: 1218-1227.

AMYGDALAR ROLES DURING EXPOSURE TO A LIVE PREDATOR AND TO A PREDATOR-ASSOCIATED CONTEXT

The amygdala plays a critical role in determining the emotional significance of sensory stimuli and the production of fear-related responses. Large amygdalar lesions have been shown to practically abolish innate defensiveness to a predator; however, it is not clear how the different amygdalar systems participate in the defensive response to a live predator. Our first aim was to provide a comprehensive analysis of the amygdalar activation pattern during exposure to a live cat and to a predator-associated context. Accordingly, exposure to a live predator up-regulated Fos expression in the medial amygdalar nucleus (MEA) and in the lateral and posterior basomedial nuclei, the former responding to predator-related pheromonal information and the latter two nuclei likely to integrate a wider array of predatory sensory information, ranging from olfactory to non-olfactory ones, such as visual and auditory sensory inputs. Next, we tested how the amygdalar nuclei most responsive to predator exposure (i.e. the medial, posterior basomedial and lateral amygdalar nuclei) and the central amygdalar nucleus (CEA) influence both unconditioned and contextual conditioned anti-predatory defensive behavior. Medial amygdalar nucleus lesions practically abolished defensive responses during cat exposure, whereas lesions of the posterior basomedial or lateral amygdalar nuclei reduced freezing and increased risk assessment displays (i.e. crouch sniff and stretch postures), a pattern of responses compatible with decreased defensiveness to predator stimuli. Moreover, the present findings suggest a role for the posterior basomedial and lateral amygdalar nuclei in the conditioning responses to a predator-related context. We have further shown that the CEA does not seem to be involved in either unconditioned or contextual conditioned anti-predatory responses. Overall, the present results help to clarify the amygdalar systems involved in processing predator-related sensory stimuli and how they influence the expression of unconditioned and contextual conditioned anti-predatory responses. (C) 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.

ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

CoMFA and CoMSIA 3D QSAR Models for a Series of Cyclic Imides with Analgesic Activity

Three-dimensional quantitative structure-activity relationships (3D-QSAR) were performed for a series of analgesic cyclic imides using the CoMFA and CoMSIA methods. Significant correlation coefficients ( CoMFA, r(2) = 0.95 and q(2) = 0.72; CoMSIA, r(2) = 0.96 and q(2) = 0.76) were obtained, and the generated models were externally validated using test sets. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel cyclic imides having improved analgesic activity.

Structure-Based Approach for the Study of Estrogen Receptor Binding Affinity and Subtype Selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpa (hER alpha) and beta (hER beta). Because the levels and relative proportion of hER alpha and hER beta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hER alpha and hER beta. Significant statistical coefficients were obtained (hER alpha, q(2) = 0.76; hER beta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hER alpha and hER beta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design or novel hER modulators with improved selectivity.

Comparative molecular field analysis of a series of inhibitors of HIV-1 protease

Several protease inhibitors have reached the world market in the last fifteen years, dramatically improving the quality of life and life expectancy of millions of HIV-infected patients. In spite of the tremendous research efforts in this area, resistant HIV-1 variants are constantly decreasing the ability of the drugs to efficiently inhibit the enzyme. As a consequence, inhibitors with novel frameworks are necessary to circumvent resistance to chemotherapy. In the present work, we have created 3D QSAR models for a series of 82 HIV-1 protease inhibitors employing the comparative molecular field analysis (CoMFA) method. Significant correlation coefficients were obtained (q(2) = 0.82 and r(2) = 0.97), indicating the internal consistency of the best model, which was then used to evaluate an external test set containing 17 compounds. The predicted values were in good agreement with the experimental results, showing the robustness of the model and its substantial predictive power for untested compounds. The final QSAR model and the information gathered from the CoMFA contour maps should be useful for the design of novel anti-HIV agents with improved potency.

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds. (C) 2010 Elsevier Masson SAS. All rights reserved.

Palaeodrainage on Marajo Island, northern Brazil, in relation to Holocene relative sea-level dynamics

A large area in northeastern Marajo Island, northern Brazil, has been characterized geomorphologically, applying information acquired from Landsat imagery. This study was combined with detailed sedimentologic analysis of continuous cores, which provided a record of depositional settings developed in this area through the Holocene. The results revealed well-preserved, meandering to anastomosed drainage networks of wide palaeochannels that were superimposed by a narrower palaeochannel system. In both cases, the sedimentary record consists of sands, heterolithic deposits and muds, locally rich in plant debris. The strata are organized into fining upward successions that reach approximately 18 m thick in the wide channels and 4 m thick in the narrow channels. Sedimentary features suggestive of a coastal location for the wider palaeochannels and reworking of sediments by tidal currents include the prevalence of well to moderately sorted, rounded to sub-rounded, fine- to medium-grained sands displaying foreset packages separated by mud couplets, suggestive of tidal cycles. The data presented herein point to a rise in relative sea level reaching the Lake Arari area during the early to late/mid Holocene. This event was followed by a relative sea level drop. Tectonics seem to have contributed to an overall lowering in relative sea level in the study area since the mid-Holocene, which does not follow the same pattern recorded in other areas along the northern Brazilian coast.