Blue bananas: development of a task to investigate the representation of synaesthetic experiences

Research has mainly focussed on the perceptual nature of synaesthesia. However, synaesthetic experiences are also semantically represented. It was our aim to develop a task to investigate the semantic representation of the concurrent and its relation to the inducer in grapheme-colour synaesthesia. Non-synaesthetes were either tested with a lexical-decision (i.e., word / non-word) or a semantic-classification (i.e., edibility decision) task. Targets consisted of words which were strongly associated with a specific colour (e.g., banana - yellow) and words which were neutral and not associated with a specific colour (e.g., aunt). Target words were primed with colours: the prime target relationship was either intramodal (i.e., word - word) or crossmodal (colour patch - word). Each of the four task versions consisted of three conditions: congruent (same colour for prime and target), incongruent (different colour), and unrelated (neutral target). For both tasks (i.e., lexical and semantic) and both versions of the task (i.e., intramodal and crossmodal), we expected faster reaction times (RTs) in the congruent condition than in the neutral condition and slower RTs in the incongruent condition than the neutral condition. Stronger effects were expected in the intramodal condition due to the overlap in the prime target modality. The results suggest that the hypotheses were partly confirmed. We conclude that the tasks and hypotheses can be readily adopted to investigate the nature of the representation of the synaesthetic experiences.

Tit. Redaktion der "Welt"

D. Farbstein

Das katholische Landkapitel Regensberg an den Tit. Großen Rath des Kantons Aargau

[Friedrich Rohner, Heinrich Mohr]

BIOMEDICAL LANGUAGE UNDERSTANDING AND EXTRACTION (BLUE-TEXT): A MINIMAL SYNTACTIC, SEMANTIC METHOD

Clinical text understanding (CTU) is of interest to health informatics because critical clinical information frequently represented as unconstrained text in electronic health records are extensively used by human experts to guide clinical practice, decision making, and to document delivery of care, but are largely unusable by information systems for queries and computations. Recent initiatives advocating for translational research call for generation of technologies that can integrate structured clinical data with unstructured data, provide a unified interface to all data, and contextualize clinical information for reuse in multidisciplinary and collaborative environment envisioned by CTSA program. This implies that technologies for the processing and interpretation of clinical text should be evaluated not only in terms of their validity and reliability in their intended environment, but also in light of their interoperability, and ability to support information integration and contextualization in a distributed and dynamic environment. This vision adds a new layer of information representation requirements that needs to be accounted for when conceptualizing implementation or acquisition of clinical text processing tools and technologies for multidisciplinary research. On the other hand, electronic health records frequently contain unconstrained clinical text with high variability in use of terms and documentation practices, and without commitmentto grammatical or syntactic structure of the language (e.g. Triage notes, physician and nurse notes, chief complaints, etc). This hinders performance of natural language processing technologies which typically rely heavily on the syntax of language and grammatical structure of the text. This document introduces our method to transform unconstrained clinical text found in electronic health information systems to a formal (computationally understandable) representation that is suitable for querying, integration, contextualization and reuse, and is resilient to the grammatical and syntactic irregularities of the clinical text. We present our design rationale, method, and results of evaluation in processing chief complaints and triage notes from 8 different emergency departments in Houston Texas. At the end, we will discuss significance of our contribution in enabling use of clinical text in a practical bio-surveillance setting.

Predicting smokeless tobacco cessation in a blue -collar population

The use of smokeless tobacco products is undergoing an alarming resurgence in the United States. Several national surveys have reported a higher prevalence of use among those employed in blue-collar occupations. National objectives now target this group for health promotion programs which reduce the health risks associated with tobacco use.^ Drawn from a larger data set measuring health behaviors, this cross-sectional study tested the applicability of two related theories, the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), to smokeless tobacco (SLT) cessation in a blue-collar population of gas pipeline workers. In order to understand the determinants of SLT cessation, measures were obtained of demographic and normative characteristics of the population and specific constructs. Attitude toward the act of quitting (AACT) and subjective norm (SN) are constructs common to both models, perceived behavioral control (PBC) is unique to the TPB, and the number of past quit attempts is not contained in either model. In addition, a self-reported measure was taken of SLT use at two-month follow-up.^ The study population was comprised of all male SLT users who were field employees in a large gas pipeline company with gas compressor stations extending from Texas to the Canadian border. At baseline, 199 employees responded to the SLT portion of the survey, 118 completed some portion of the two-month follow-up, and 101 could be matched across time.^ As hypothesized, significant correlations were found between constructs antecedent to AACT and SN, although crossover effects occurred. Significant differences were found between SLT cessation intenders and non-intenders with regard to their personal and normative beliefs about quitting as well as their outcome expectancies and motivation to comply with others' beliefs. These differences occurred in the expected direction, with the mean intender score consistently higher than that of the non-intender.^ Contrary to hypothesis, AACT predicted intention to quit but SN did not. However, confirmatory of the TPB, PBC, operationalized as self-efficacy, independently contributed to the prediction of intention. Statistically significant relationships were not found between intention, perceived behavioral control, their interactive effects, and use behavior at two-month follow-up. The introduction of number of quit attempts into the logistic regression model resulted in insignificant findings for independent and interactive effects.^ The findings from this study are discussed in relation to their implications for program development and practice, especially within the worksite. In order to confirm and extend the findings of this investigation, recommendations for future research are also discussed. ^

Tab. 1: Results of the movement measurements on the blue ice-field Blåisen

In January/February 1985 a German-South African expedition had the opportunity to repeat measurements made by means of stakes planted in 1951 (Norwegian-British-Swedish Antarctic Expedition 1949-52) and 1966 (SANAE VII). Although the rediscovery of the old stakes had not been expected, the stakes could be identified and it was possible to derive movement vectors on the basis of old and heterogenic measurement data. The long-term movement rates established basically confirm and complement the values determined in 1951. The flow rates of 9,1 cm/a to 66.4 cm/a proved to be extremly low. Observations of the stake lengths showed very little accumulation in the fringe areas of the blue ice-field (ca. 0.7 to 2.6 cm/a snow/firn); on bare ice an ablation of 2.6 cm/a water equivalent (2.9 cm/a ice) was measured. The paper begins with a description of the essential conditions for the formation of the blue ice-field. Subsequently the measurements are explained in detail and their results are discussed.

Effects of various pollutant mixtures on immune responses of the blue mussel (Mytilus edulis) collected at a salinity gradient in Danish coastal waters, links to supplementary material

The Baltic Sea is a semi-enclosed sea with a steady salinity gradient (3 per mil-30 per mil). Organisms have adapted to such low salinities, but are suspected to be more susceptible to stress. Within the frame of the integrated environmental monitoring BONUS + project "BEAST" the applicability of immune responses of the blue mussel was investigated in Danish coastal waters. The sampling sites were characterised by a salinity range (11-19 per mil) and different mixtures of contaminants (metals, PAHs and POPs), according to chemical analysis of mussel tissues. Variation partitioning (redundancy analysis) was applied to decompose salinity and contamination effects. The results indicated that cellular immune responses (total and differential haemocyte count, phagocytic activity and apoptosis) were mainly influenced by contaminants, whereas humoral factors (haemolytic activity) were mainly impacted by salinity. Hence, cellular immune functions may be suitable as biomarkers in monitoring programmes for the Baltic Sea and other geographic regions with salinity variances of the studied range.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) and Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic (2004-2012)

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2007

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2005

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2008

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2006

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.