Background:CrossFit is a conditioning and training program that has been gaining recognition and interest among the physically active population. Approximately 440 certified and registered CrossFit fitness centers and gyms exist in Brazil, with approximately 40,000 athletes. To date, there have been no epidemiological studies about the CrossFit athlete in Brazil.Purpose:To evaluate the profile, sports history, training routine, and presence of injuries among athletes of CrossFit.Study Design:Descriptive epidemiological study.Methods:This cross-sectional study was based on a questionnaire administered to CrossFit athletes from various specialized fitness centers in Brazil. Data were collected from May 2015 to July 2015 through an electronic questionnaire that included demographic data, level of sedentary lifestyle at work, sports training history prior to starting CrossFit, current sports activities, professional monitoring, and whether the participants experienced any injuries while practicing CrossFit.Results:A total of 622 questionnaires were received, including 566 (243 women [42.9%] and 323 men [57.1%]) that were completely filled out and met the inclusion criteria and 9% that were incompletely filled out. Overall, 176 individuals (31.0%) mentioned having experienced some type of injury while practicing CrossFit. We found no significant difference in injury incidence rates regarding demographic data. There was no significant difference regarding previous sports activities because individuals who did not practice prior physical activity showed very similar injury rates to those who practiced at any level.Conclusion:CrossFit injury rates are comparable to those of other recreational or competitive sports, and the injuries show a profile similar to weight lifting, power lifting, weight training, Olympic gymnastics, and running, which have an injury incidence rate nearly half that of soccer.

Traditionally, most data-to-text applications have been designed using a modular pipeline architecture, in which non-linguistic input data is converted into natural language through several intermediate transformations. By contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in between. This study introduces a systematic comparison between neural pipeline and endto-end data-to-text approaches for the generation of text from RDF triples. Both architectures were implemented making use of the encoder-decoder Gated-Recurrent Units (GRU) and Transformer, two state-of-the art deep learning methods. Automatic and human evaluations together with a qualitative analysis suggest that having explicit intermediate steps in the generation process results in better texts than the ones generated by end-to-end approaches. Moreover, the pipeline models generalize better to unseen inputs. Data and code are publicly available. 1

Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines. Data and models are publicly available 1 .

Hypertonic saline and pentoxifylline, both alone and in combination, attenuated oxidative stress and the activation of NF-κB, leading to a decrease in the inflammatory response.

In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrasebased and Neural MT). We systematically study the effects of 3 AMR preprocessing steps (Delexicalisation, Compression, and Linearisation) applied before the MT phase. Our results show that preprocessing indeed helps, although the benefits differ for the two MT models. The implementation of the models are publicly available 1 .

In this study, we introduce a nondeterministic method for referring expression generation. We describe two models that account for individual variation in the choice of referential form in automatically generated text: a Naive Bayes model and a Recurrent Neural Network. Both are evaluated using the VaREG corpus. Then we select the best performing model to generate referential forms in texts from the GREC-2.0 corpus and conduct an evaluation experiment in which humans judge the coherence and comprehensibility of the generated texts, comparing them both with the original references and those produced by a random baseline model.

Drilling PLT and FCL femoral tunnels at 20° axial/20° coronal angulation is a safe positioning for simultaneous ACL and PLC reconstructions. However, in smaller knees, the risk for tunnel collision could be greater. Surgeons should consider the possibility of tunnel collision when performing simultaneous ACL and PLC anatomical reconstruction, especially in knees with a small LFC width where the risk for tunnel collision could be greater.

This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a,b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation. We also produce a silverstandard German translation of the corpus to enable the exploitation of NLG approaches to other languages than English. The enriched corpus is publicly available 1 .