Plasman kortisolin vuorokausivaihtelu puunpurijoilla ja verrokkihevosilla

Kortisoli on lisämunuaisperäinen glukokortikoideihin kuuluva steroidihormoni, jolla on tärkeä säätelytehtävä elimistön aineenvaihdunnassa. Kortisoli on yksi tärkeimmistä stressihormoneista ja sen avulla elimistö kykenee selviytymään stressin aiheuttamista muutoksista. Kortisolin eritykselle on tyypillistä vuorokausivaihtelu. Hevosilla kortisolin pitoisuus on matalimmillaan yöaikaan ja korkeimmillaan aamulla. Vuorokausivaihtelun lisäksi plasman kortisolipitoisuuteen vaikuttaa esimerkiksi hevosen kokema stressi. Puunpureminen eli imppaaminen on yksi yleisimmistä stereotypioista hevosilla. Sitä on pidetty huonona tallitapana, josta hevosten omistajat ovat yrittäneet päästä eroon esimerkiksi erilaisten puunpuremista estävien pantojen avulla. Sen etiologia ja funktio on huonosti tunnettu. Yleisesti oletetaan, että puunpurijahevoset ovat muita hevosia stressiherkempiä, jolloin puunpureminen on ikään kuin keino käsitellä ja vähentää stressiä sekä nopeuttaa stressiin sopeutumista. Puunpuremisen ja plasman kortisolipitoisuuden välistä yhteyttä on tutkittu hevosilla jonkin verran. Tutkimuksissa on keskitytty enimmäkseen lyhytaikaisen kortisolipitoisuuden tutkimiseen. Hevosten stereotypioihin verrattavissa tilanteissa ihmisillä on havaittu muutoksia kortisolin vuorokausivaihtelussa. Tutkimuksen kokeellisessa osuudessa haluttiin selvittää kortisolin vuorokausivaihtelua ja verrata sitä puunpurijoiden ja verrokkihevosten välillä. Tutkimuksen kokeellinen osuus suoritettiin keväällä 2008. Tutkimuksessa ei löydetty eroavaisuuksia kortisolin pitoisuuksissa eikä vuorokausivaihtelussa puunpurijoiden ja verrokkien välillä. Selkeä kortisolipitoisuuden vuorokausivaihtelu oli havaittavissa kaikilla hevosilla

Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

In this paper, we focus on parsing rare and non-trivial constructions, in particular ellipsis. We report on several experiments in enrichment of training data for this specific construction, evaluated on five languages: Czech, English, Finnish, Russian and Slovak. These data enrichment methods draw upon self-training and tri-training, combined with a stratified sampling method mimicking the structural complexity of the original treebank. In addition, using these same methods, we also demonstrate small improvements over the CoNLL-17 parsing shared task winning system for four of the five languages, not only restricted to the elliptical constructions.</p

Circadian variation in ghrelin and certain stress hormones in crib-biting horses

Crib-biting is classified as an oral stereotypy, which may be initiated by stress susceptibility, management factors, genetic factors and gastrointestinal irritation. Ghrelin has been identified in the gastric mucosa and is involved in the control of food intake and reward, but its relationship to crib-biting is not yet known. The aim of this study was to examine the concentration and circadian variation of plasma ghrelin, cortisol, adrenocorticotropic hormone (ACTH) and β-endorphin in crib-biting horses and non-crib-biting controls. Plasma samples were collected every second hour for 24 h in the daily environment of eight horses with stereotypic crib-biting and eight non-crib-biting controls. The crib-biting horses had significantly higher mean plasma ghrelin concentrations than the control horses. The circadian rhythm of cortisol was evident, indicating that the sampling protocol did not inhibit the circadian regulation in these horses. Crib-biting had no statistically significant effect on cortisol, ACTH or β-endorphin concentrations. The inter-individual variations in β-endorphin and ACTH were higher than the intra-individual differences, which made inter-individual comparisons difficult and complicated the interpretation of results. Further research is therefore needed to determine the relationship between crib-biting and ghrelin concentration.Peer reviewe

CoNLL 2017 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.Peer reviewe

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

Artificial Treebank with Ellipsis

Artificially created treebank of elliptical constructions (gapping), in the annotation style of Universal Dependencies. Data taken from UD 2.1 release, and from large web corpora parsed by two parsers. Input data are filtered, sentences are identified where gapping could be applied, then those sentences are transformed, one or more words are omitted, resulting in a sentence with gapping. Details in Droganova et al.: Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions, LREC 2018, Miyazaki, Japan

Artificial Treebank with Ellipsis

Artificially created treebank of elliptical constructions (gapping), in the annotation style of Universal Dependencies. Data taken from UD 2.1 release, and from large web corpora parsed by two parsers. Input data are filtered, sentences are identified where gapping could be applied, then those sentences are transformed, one or more words are omitted, resulting in a sentence with gapping. Details in Droganova et al.: Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions, LREC 2018, Miyazaki, Japan

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating syste

Universal Dependencies 2.0 alpha (obsolete)

This release contains errors in several files. Please use http://hdl.handle.net/11234/1-1983 instead

Universal Dependencies 2.0

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). This release is special in that the treebanks will be used as training/development data in the CoNLL 2017 shared task (http://universaldependencies.org/conll17/). Test data are not released, except for the few treebanks that do not take part in the shared task. 64 treebanks will be in the shared task, and they correspond to the following 45 languages: Ancient Greek, Arabic, Basque, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Gothic, Greek, Hebrew, Hindi, Hungarian, Indonesian, Irish, Italian, Japanese, Kazakh, Korean, Latin, Latvian, Norwegian, Old Church Slavonic, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Turkish, Ukrainian, Urdu, Uyghur and Vietnamese. This release fixes a bug in http://hdl.handle.net/11234/1-1976. Changed files: ud-tools-v2.0.tgz (conllu_to_text.pl, conllu_to_conllx.pl; added text_without_spaces.pl), ud-treebanks-conll2017.tgz (fi_ftb-ud-train.txt, he-ud-train.txt, it-ud-train.txt, pt_br-ud-train.txt, es-ud-train.txt) and ud-treebanks-v2.0.tgz (fi_ftb-ud-train.txt, he-ud-train.txt, it-ud-train.txt, pt_br-ud-train.txt, es-ud-train.txt, ar_nyuad-ud-dev.txt, ar_nyuad-ud-test.txt, ar_nyuad-ud-train.txt, cop-ud-dev.txt, cop-ud-test.txt, cop-ud-train.txt, sa-ud-dev.txt, sa-ud-test.txt, sa-ud-train.txt)