Biology subjects

Olofsson, J.

Publications and source records attributed to Olofsson, J..

3 recordsLinked to original sources

bioRxiv2025

Genomic selection for accelerated heartwood formation in Pedunculate oak (Quercus robur L.) using whole-genome sequencing

Heartwood traits in trees are critical for timber quality but are notoriously difficult to phenotype due to their late expression and the need for destructive sampling. In this proof-of-concept study, we demonstrate that combining genome-wide association studies (GWAS) with Bayesian genomic prediction models provides an effective strategy to overcome these challenges. By using GWAS to preselect trait associated SNPs and integrating them into predictive models, we substantially improve the accuracy of genomic predictions for heartwood related traits in oaks. Our approach allows for reliable selection of superior genotypes at the seedling stage, long before heartwood traits can be directly measured, thus enabling early and cost-effective breeding decisions. We also identify the number of rings in sapwood as a genetically controlled, easily measured proxy trait that enhances selection strategies for heartwood content. Together, these findings provide a scalable framework for integrating genomics into operational tree breeding programs and demonstrate how combining GWAS and genomic prediction can accelerate the improvement of complex wood traits in long lived forest tree species.

genetics↗

bioRxiv2025

Blastocoel fluid RNA predicts pregnancy outcome in assisted reproduction

Nearly one in eight couples are affected by infertility, with many relying on assisted reproductive technologies (ART) to conceive. However, selecting the highest-quality embryo in ART remains a major challenge, as current assessment methods are often subjective, or invasive, and lack precision. Here, we introduce a novel strategy that analyses embryo-derived polyadenylated RNA in blastocoel fluid to more accurately predict pregnancy outcomes. Elevated RNA levels were strongly associated with implantation failure, particularly in embryos from women over the age of 34. Our predictive model developed using our sample cohort, incorporating both RNA and maternal age, demonstrated exceptional performance, achieving 76% accuracy in the training set and 73% in independent validation in predicting implantation outcome-- highlighting a promising advancement in embryo selection and ART success.

molecular biology↗

bioRxiv2025

Elusive scents: neurocomputational mechanisms of verbal omissions in free odor naming

Odor naming is considered a particularly challenging cognitive test, but the underlying cause of this difficulty is unknown. People often fail to report any source label to identify common odors, resulting in omissions (i.e., a lack of response). Here, with the support of a computational model, we offer a hypothesis about the neural network mechanisms underlying odor naming omissions. Based on an evaluation of behavioral data from almost 40,000 odor naming attempts, we suggest that high omission rates are driven by odors that are referred to by multiple linguistic labels. To explain this observation at the systems level, where olfactory perception and language (semantic) processing are produced by interacting cortical systems, we developed a computational model consisting of two associatively coupled attractor memory networks (odor and language networks), and investigated the effect of Hebbian-like learning on the simulated task performance. We used distributed network representations for the odor percepts and word label mental objects, and accounted for their statistical inter-relationships (correlations) extracted from collected data on odor perceptual similarity, and from a large Swedish odor language corpus, respectively. We evaluated a novel hypothesis, that Bayesian-Hebbian synaptic plasticity mechanisms can explain behavioral omissions in odor naming tasks, casting new light on the underlying mechanisms of this frequently observed memory phenomenon. Due to the nature of Bayesian-Hebbian associative learning connecting the two networks, there was a progressively weaker coupling for odors paired with multiple different labels in the encoding process (one-to-many mapping). Thus, when the model was cued with perceptual odor stimuli that established multiple word label associations (one-to-many mapping), the olfactory language network often produced subthreshold network responses, resulting in elevated omissions (opposite to one-to-few mapping scenario that led to improved performance scores). Our results are of theoretical interest, as they suggest a biologically plausible mechanism to explain a common, but poorly understood, behavioral phenomenon.

neuroscience↗