The impact of (very) young donor age on euploid rates: An analysis of 1831 trophectoderm biopsies evaluated with 24-chromosome NGS screening in oocyte donation cycles.

RESEARCH QUESTION: Conflicting data exists regarding whether a younger age of donors has a negative influence on the outcomes of oocyte donation cycles. Is there any correlation between a younger age of donors and the rate of embryonic aneuploidy in oocyte donation cycles? DESIGN: Retrospective study including 515 oocyte donation cycles carried out between February 2017 and November 2022. Comprehensive chromosomal screening was performed on 1831 blastocysts. 1793 had a result which were categorised into groups based on the age of the donor: 18-22 (n = 415), 23-25 (n = 600), 26-30 (n = 488), and 31-35 years (n = 290). The analysis aimed to determine the percentage of biopsy samples that were euploid and the number that were aneuploid, relative to the age group of the oocyte donor. Additionally, linear regression was employed to examine the relationship between age and the proportion of aneuploid embryos, while controlling for relevant variables. RESULTS: Aneuploidy increased predictably with donor age: 18-22 years: 27.5 %; 23-25 years: 31.2 %; 26-30 years: 31.8 %; and 31-35 years: 38.6 %. In the donor group aged 31-35 years, a higher percentage of aneuploid embryos was observed compared to younger donors in univariate analysis (OR: 1.66, 95 % CI: 1.21-2.29, p = 0.002) and multivariate logistic analysis (OR: 2.65, 95 % CI: 1.67-4.23, p < 0.001). The rates of embryonic mosaicism revealed no significant differences. CONCLUSION: The lowest risk of embryonic aneuploidy was found among donors aged <22 years. Conversely, an elevated prevalence was evident within the donor group aged 31-35 years, in contrast to the younger cohorts. The incidence of mosaic embryos remained consistent across all age groups.

Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps.

This paper describes the scientific achievements of a collaboration between a research group and the waste management division of a company. While these results might be the basis for several practical or commercial developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo-located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an automatic discrimination between images with and without containers. More precisely, two state-of-the-art object detectors based on deep learning techniques have been selected for testing, according to their performance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,…) and the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of size, climate, urban layout and containers' appearance.