). † These authors contributed equally to the work. SummarySuccessful male reproductive function in plants is dependent on the correct development and functioning of stamens and pollen. AGP6 and AGP11 are two homologous Arabidopsis genes encoding cell wall-associated arabinogalactan glycoproteins (AGPs). Both genes were found to be specifically expressed in stamens, pollen grains and pollen tubes, suggesting that these genes may play a role in male organ development and function. RNAi lines with reduced AGP6 and AGP11 expression were generated. These, together with lines harboring point mutations in the coding region of AGP6, were used to show that loss of function in AGP6 and AGP11 led to reduced fertility, at least partly as a result of inhibition of pollen tube growth. Our results also suggest that AGP6 and AGP11 play an additional role in the release of pollen grains from the mature anther. Thus, our study demonstrates the involvement of specific AGPs in pollen tube growth and stamen function.

During in vitro fertilization (IVF) cycles, multiple mature oocytes are retrieved from the ovary and are fertilized in the lab. The newly generated embryos can be transferred into the uterus on day-3,-4, or-5 of incubation, cryopreserved for subsequent transfers or discarded. Lacking a reliable noninvasive evaluation method of the potential to implant, pregnancy rates can be improved by cotransferring multiple embryos thus introducing health risks that are associated with multiple pregnancies. [1] Hence, the evaluation of embryo quality is required for improving live birth rates while minimizing medical complications and shortening time to pregnancy. [2-6] Machine learning was used for assessing the potential of embryos to blastulate [7,8] and to implant [9-11] based on manually annotated morphological and/or morphokientic features. Deep learning, which offers a powerful toolbox for carrying out automated and standardized classification tasks

The majority of human embryos, whether naturally or in vitro fertilized (IVF), do not poses the capacity to implant within the uterus and reach live birth. Hence, selecting the embryos with the highest developmental potential to implant is imperative for improving pregnancy rates without prolonging time to pregnancy. The developmental potential of embryos can be assessed based on temporal profiling of the discrete morphokinetic events of preimplantation development. However, manual morphokinetic annotation introduces intra- and inter-observer variation and is time-consuming. Using a large clinically-labeled multicenter dataset of video recordings of preimplantation embryo development by time-lapse incubators, we trained a convolutional neural network and developed a classifier that performs fully automated, robust, and standardized annotation of the morphokinetic events with R-square 0.994 accuracy. To delineate the morphokinetic heterogeneity of preimplantation development, we performed unsupervised clustering of high-quality embryo candidates for transfer, which was independent of maternal age and blastulation rate. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters that are distinctively marked by poor synchronization of the third meiotic cell-cleavage cycle. We expect this work to advance the integration of morphokinetic-based decision support tools in IVF treatments and deepen our understanding of preimplantation heterogeneity.

Purpose First trimester miscarriage is a major concern in IVF-ET treatments, accounting for one out of nine clinical pregnancies and for up to one out of three recognized pregnancies. To develop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development. Methods Retrospective study of a 4-year multi-center cohort of 391 women undergoing intra-cytoplasmatic sperm injection (ICSI) and fresh single or double embryo transfers. The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live-birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester. Data were recorded and obtained in hospital setting and research was performed in university setting. Results A minimal subset of six non-redundant morphodynamic features were screened that maintained high prediction capacity. Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome as evaluated using the SHapley Additive exPlanations (SHAP) methodology. Using this feature subset, XGBoost and random forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Miscarriage was predicted with AUC 0.68 to 0.69. Conclusion We report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential is expected to improve live-birth rates and shorten time-to-pregnancy.