Forming social impressions from voices in native and foreign languages

We form very rapid personality impressions about speakers on hearing a single word. This implies that the acoustical properties of the voice (e.g., pitch) are very powerful cues when forming social impressions. Here, we aimed to explore how personality impressions for brief social utterances transfer across languages and whether acoustical properties play a similar role in driving personality impressions. Additionally, we examined whether evaluations are similar in the native and a foreign language of the listener. In two experiments we asked Spanish listeners to evaluate personality traits from different instances of the Spanish word “Hola” (Experiment 1) and the English word “Hello” (Experiment 2), native and foreign language respectively. The results revealed that listeners across languages form very similar personality impressions irrespective of whether the voices belong to the native or the foreign language of the listener. A social voice space was summarized by two main personality traits, one emphasizing valence (e.g., trust) and the other strength (e.g., dominance). Conversely, the acoustical properties that listeners pay attention to when judging other’s personality vary across languages. These results provide evidence that social voice perception contains certain elements invariant across cultures/languages, while others are modulated by the cultural/linguistic background of the listener.This study was funded by the Agencia Estatal de Investigación (AEI, National Research Agency) and Fondo Europeo de Desarrollo Regional (FEDER, European Regional Development Fund) under projects PSI2017- 84539-P and PSI2014-52181-P, the Catalan Government (2017 SGR 268), and the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 613465 - AThEME. CB was supported by the People Program (Marie Curie Actions, FP7-PEOPLE 2014–2016) under REA agreement n°623845 and now is supported by the Beatriu de Pinòs program (AGAUR, BP00381). PB was supported by supported by grant AJE201214 from French Foundation for Medical Research, and grants ANR-16-CONV-0002 (Institute of Language, Communication and the Brain), ANR-11-LABX-0036 (Brain and Language Research Institute) and the Excellence Initiative of Aix-Marseille University (A*MIDEX)

187 Expert-developed ICD-AIS map for measuring serious road traffic injuries

This is a conference abstract. The author accepted manuscript version is entitled ICD-AIS MAP FOR SERIOUS MAIS 3+ INJURIES. It has been published in the journal Injury Prevention Vol 22 Supp 2 as part of the Safety 2016 World Conference, 18–21 September 2016, Tampere, Finland. The definitive version is available at: http://dx.doi.org/10.1136/injuryprev-2016-042156.18

Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008

Objective: This paper describes how maps were developed from the Clinical Modifications of the 9th and 10th Revisions of the International Classification of Diseases (ICD) to the Abbreviated Injury Scale 2005 Update 2008 (AIS08). The development of the mapping methodology is described, with discussion of the major assumptions used in the process to map ICD codes to AIS severities. There were many intricacies to developing the maps, since the two coding systems, ICD and AIS, were developed for different purposes and contain unique classification structures to meet these purposes. Methods: Experts in ICD and AIS analyzed the rules and coding guidelines of both injury coding schemes to develop rules for mapping ICD injury codes to the AIS08. This involved subject matter expertise, detailed knowledge of anatomy, and an in-depth understanding of injury terms and definitions as applied in both taxonomies. The official ICD-9-CM and ICD-10-CM versions (injury sections) were mapped to the AIS08 codes and severities, following the rules outlined in each coding manual. The panel of experts was comprised of coders certified in ICD and/or AIS from around the world. In the process of developing the map from ICD to AIS, the experts created rules to address issues with the differences in coding guidelines between the two schemas and assure a consistent approach to all codes. Results: Over 19,000 ICD codes were analyzed and maps were generated for each code to AIS08 chapters, AIS08 severities, and ISS body regions. After completion of the maps, 14,101 (74%) of the eligible 19,012 injury related ICD-9-CM and ICD-10-CM codes were assigned valid AIS08 severity scores between 1 and 6. The remaining 4,911 codes were assigned an AIS08 of 9 (unknown) or were determined to be non-mappable because the ICD description lacked sufficient qualifying information for determining severity according to AIS rules. There were also 15,214 (80%) ICD codes mapped to AIS08 chapter and ISS body region, which allow for ISS calculations for patient datasets. Conclusion: This mapping between ICD and AIS provides a comprehensive, expert-designed solution for analysts to bridge the data gap between the injury descriptions provided in hospital codes (ICD-9-CM, ICD-10-CM) and injury severity codes (AIS08). By applying consistent rules from both the ICD and AIS taxonomies, the expert panel created these definitive maps, which are the only ones endorsed by AAAM. Initial validation upheld the quality of these maps for the estimation of AIS severity, but future work should include verification of these maps for MAIS and ISS estimations with large datasets. These ICD-AIS maps will support data analysis from databases with injury information classified in these two different systems and open new doors for the investigation of injury from traumatic events using large injury datasets

The very forward CASTOR calorimeter of the CMS experiment

International audienceThe physics motivation, detector design, triggers, calibration, alignment, simulation, and overall performance of the very forward CASTOR calorimeter of the CMS experiment are reviewed. The CASTOR Cherenkov sampling calorimeter is located very close to the LHC beam line, at a radial distance of about 1 cm from the beam pipe, and at 14.4 m from the CMS interaction point, covering the pseudorapidity range of $-$6.6 $\lt\eta\lt$ $-$5.2. It was designed to withstand high ambient radiation and strong magnetic fields. The performance of the detector in measurements of forward energy density, jets, and processes characterized by rapidity gaps, is reviewed using data collected in proton and nuclear collisions at the LHC