Experimental evidence for improved neuroimaging interpretation using three-dimensional graphic models

Three-dimensional (3D) or volumetric visualization is a useful resource for learning about the anatomy of the human brain. However, the effectiveness of 3D spatial visualization has not yet been assessed systematically. This report analyzes whether 3D volumetric visualization helps learners to identify and locate subcortical structures more precisely than classical cross-sectional images based on a two dimensional (2D) approach. Eighty participants were assigned to each experimental condition: 2D cross-sectional visualization vs. 3D volumetric visualization. Both groups were matched for age, gender, visual-spatial ability, and previous knowledge of neuroanatomy. Accuracy in identifying brain structures, execution time, and level of confidence in the response were taken as outcome measures. Moreover, interactive effects between the experimental conditions (2D vs. 3D) and factors such as level of competence (novice vs. expert), image modality (morphological and functional), and difficulty of the structures were analyzed. The percentage of correct answers (hit rate) and level of confidence in responses were significantly higher in the 3D visualization condition than in the 2D. In addition, the response time was significantly lower for the 3D visualization condition in comparison with the 2D. The interaction between the experimental condition (2D vs. 3D) and difficulty was significant, and the 3D condition facilitated the location of difficult images more than the 2D condition. 3D volumetric visualization helps to identify brain structures such as the hippocampus and amygdala, more accurately and rapidly than conventional 2D visualization. This paper discusses the implications of these results with regards to the learning process involved in neuroimaging interpretation.     

Lernmittelfreiheit und Einheitslehrbuch: Eine Vergleichende Untersuchung

Ohne Zusammenfassung     

Dependency parsing with finite state transducers and compression rules

This article proposes a syntactic parsing strategy based on a dependency grammar containing formal rules and a compression technique that reduces the complexity of those rules. Compression parsing is mainly driven by the ‘single-head’ constraint of Dependency Grammar, and can be seen as an alternative method to the well-known constructive strategy. The compression algorithm simplifies the input sentence by progressively removing from it the dependent tokens as soon as binary syntactic dependencies are recognized. This strategy is thus similar to that used in deterministic dependency parsing. A compression parser was implemented and released under General Public License, as well as a cross-lingual grammar with Universal Dependencies, containing only broad-coverage rules applied to Romance languages. The system is an almost delexicalized parser which does not need training data to analyze Romance languages. The rule-based cross-lingual parser was submitted to CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. The performance of our system was compared to the other supervised systems participating in the competition, paying special attention to the parsing of different treebanks of the same language. We also trained a supervised delexicalized parser for Romance languages in order to compare it to our rule-based system. The results show that the performance of our cross-lingual method does not change across related languages and across different treebanks, while most supervised methods turn out to be very dependent on the text domain used to train the system.     

Factors Influencing Scientific Achievements of Medical Students in the Anatomy Research Group: An Analysis From the Autonomous University of Nuevo León in Mexico

Scientific research and student involvement are critical to the formation of physicians, yet the number of medical researchers has decreased over time. To implement corrective strategies, the variables associated with positive research attitudes and productivity among medical students must be identified. The aim of this study was to evaluate the variables associated with students interested or involved in research. A validated questionnaire was applied to the student members of an established anatomy research group in a Mexican medical school with a six-year medical program. Data were collected and analyzed. A total of 85.5% (n = 77/90) students answered the survey with most respondents being second-year medical students. The majority of respondents indicated that the important component of conducting research was a contribution to the new knowledge (45.5%) and to the scientific community (42.9%). More than half of respondents mentioned a professor or a peer as the initial motivation to become involved in research. Lack of time was the main limitation (59.7%) to research involvement. Perceived benefits were knowledge and team work skills. Of those involved, most (85.7%) wished to continue participating in research as a complement to their clinical work. Professors and student colleagues were found to play an important motivational and recruitment role for medical research. These efforts in turn have developed into long-lasting mentor-mentee relationships. Students also anticipated that early involvement in research will positively influence the likelihood of future physicians' contribution and collaboration in research.