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Biology subjects

Mladenova, M.

Publications and source records attributed to Mladenova, M..

2 recordsLinked to original sources

Phenology underlies apparent urbanisation effects on avian malaria in juvenile songbirds

Urbanisation can modify species interactions, including those between parasites and their hosts. In birds, urbanisation can either increase or decrease avian malaria infection, depending on host species, parasite or study location. However, temporal coordination between parasites and hosts, which may impact infection outcomes, has not been studied in urban ecology. To fill this gap, we collected blood samples from wild blue tit nestlings (Cyanistes caeruleus) in urban and forest habitats to examine how their hatch dates affected the prevalence and intensity of malaria infection. To separate parasites and quantify parasite load, we newly developed a species-specific qPCR assay. We found that Leucocytozoon prevalence was strongly affected by urbanisation, but effects depended on study year. This was driven by hatch date: nestlings that hatched earlier in the spring had a lower probability of being infected, independent of habitat type. In the few heavily infected nestlings, intensity of infection was associated with low body weight, suggesting fitness effects of infection. These results highlight the importance of breeding early to avoid early-life infection with malaria parasites, and that apparent urbanisation effects on infection arose from phenological differences between urban and forest habitats. Underappreciated phenological changes may underline other ecological effects of urbanisation.

ecology↗

Aneuploid embryos as a proposal for improving Artificial Intelligence performance

RESEARCH QUESTIONCould we improve the performance of Machine Learning algorithms by using aneuploid embryos instead of non-implanted embryos as the contrary reference to Live-Birth embryos? DESIGNA single-center retrospective analysis of 343 embryos through 3 ML algorithms, based on manually annotated morphokinetics from Day 1 to Day 3. Two datasets were built including the same Live-Birth embryos (117). Dataset A included 123 non-implanted embryos, while Dataset B included 103 aneuploid embryos. V-Fold Cross-Validation was performed for each dataset and algorithm and the Area Under the Curve (AUC) was registered. RESULTSAUC for Dataset A did not reach 0.6 for any of the algorithms; while AUC values for "Dataset B" surpassed 0.7. According to this, different morphokinetic patterns were detected by Machine Learning algorithms. CONCLUSIONSAlgorithms minor performance with non-implanted embryos may be due to an increased Label Noise effect, suggesting that including aneuploid embryos could be more appropriate when building predictive algorithms for embryo viability. Machine Learning algorithms results were improved when aneuploid embryos were taken into consideration.

genetics↗