Sperm aminopeptidase N identifies the potential for high-quality blastocysts and viable embryos in oocyte-donation cycles.

STUDY QUESTION: Is there a relationship between human sperm aminopeptidase N (APN) and embryo development in humans? SUMMARY ANSWER: Human sperm APN could possibly become a new molecular biomarker for identifying the potential for high-quality and usable embryos. WHAT IS KNOWN ALREADY: The diagnosis of male fertility is one of the major concerns of reproductive medicine. Approximately 30-40% of men with otherwise normal fertility parameters are still unable to achieve pregnancy. The predictive clinical value of semen analysis to identify fertile or infertile males is limited; therefore, new diagnostic methodologies for sperm are urgently required. Sperm APN may be a relevant molecular marker due to its high concentration in sperm cells and its important roles in sperm physiology, such as its functions in motility, acrosome reaction and embryo development. STUDY DESIGN, SIZE, DURATION: This study included 81 couples who underwent oocyte-donation cycles at Clínica IVI Bilbao (Spain), yielding 611 embryos, between September 2014 and July 2015. PARTICIPANTS/MATERIALS, SETTING, METHODS: This study was conducted in an assisted reproduction unit and an academic research laboratory. All the semen samples were examined and classified following World Health Organization guidelines. Spermatozoa were isolated from semen using the discontinuous colloidal silica gradient (45-90%) technique. Embryo quality and development were determined according to the Spanish Association of Reproduction Biology Studies (ASEBIR) criteria. Human sperm APN levels were analyzed by quantitative and semiquantitative flow cytometry. MAIN RESULTS AND THE ROLE OF CHANCE: The most well-developed and usable blastocysts were associated with low sperm APN levels. Semen samples that had lower APN levels generated more expanded, hatched and usable blastocysts and fewer early, arrested and non-usable blastocysts. The cumulative probability of having well-developed blastocysts increased by 1.38-fold at Day 5 and 1.90-fold at Day 6 of embryo development, and the likelihood of having usable embryos increased by 1.48-fold, when semen samples with low APN levels were used during the ICSI technique. LIMITATIONS, REASONS FOR CAUTION: The data were obtained from a single fertility clinic. A multicentre study will be required to confirm the results. WIDER IMPLICATIONS OF THE FINDINGS: Human sperm APN has the potential to become a new molecular biomarker to help identify the potential for high-quality embryos and diagnose male infertility, especially when seminal parameters are close to the threshold values. It could be a crucial tool for couples for whom the number of usable blastocysts is critical for ART success. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by grants from the Basque Government (GIC15/165) and the University of the Basque Country (UPV/EHU) (EHUA14/17). The authors declare that they have no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Learning about phraseology from corpora: A linguistically motivated approach for Multiword Expression identification.

Multiword Expressions (MWEs) are idiosyncratic combinations of words which pose important challenges to Natural Language Processing. Some kinds of MWEs, such as verbal ones, are particularly hard to identify in corpora, due to their high degree of morphosyntactic flexibility. This paper describes a linguistically motivated method to gather detailed information about verb+noun MWEs (VNMWEs) from corpora. Although the main focus of this study is Spanish, the method is easily adaptable to other languages. Monolingual and parallel corpora are used as input, and data about the morphosyntactic variability of VNMWEs is extracted. This information is then tested in an identification task, obtaining an F score of 0.52, which is considerably higher than related work.

Neural machine translation of clinical texts between long distance languages.

OBJECTIVE: To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish. MATERIALS AND METHODS: We trained recurrent neural networks on an out-of-domain corpus with different hyperparameter values. Subsequently, we used the optimal configuration to evaluate machine translation of EHR templates between Basque and Spanish, using manual translations of the Basque templates into Spanish as a standard. We successively added to the training corpus clinical resources, including a Spanish-Basque dictionary derived from resources built for the machine translation of the Spanish edition of SNOMED CT into Basque, artificial sentences in Spanish and Basque derived from frequently occurring relationships in SNOMED CT, and Spanish monolingual EHRs. Apart from calculating bilingual evaluation understudy (BLEU) values, we tested the performance in the clinical domain by human evaluation. RESULTS: We achieved slight improvements from our reference system by tuning some hyperparameters using an out-of-domain bilingual corpus, obtaining 10.67 BLEU points for Basque-to-Spanish clinical domain translation. The inclusion of clinical terminology in Spanish and Basque and the application of the back-translation technique on monolingual EHRs significantly improved the performance, obtaining 21.59 BLEU points. This was confirmed by the human evaluation performed by 2 clinicians, ranking our machine translations close to the human translations. DISCUSSION: We showed that, even after optimizing the hyperparameters out-of-domain, the inclusion of available resources from the clinical domain and applied methods were beneficial for the described objective, managing to obtain adequate translations of EHR templates. CONCLUSION: We have developed a system which is able to properly translate health record templates from Basque to Spanish without making use of any bilingual corpus of clinical texts or health records.