Spontaneous abortion is a complex, multifactorial pathology, where various genetic, neural, endocrine, and immunological factors are involved. Cytokines, Toll-like receptors, and progesterone receptors play critical roles in embryonic implantation and development. A delicate, stage-specific equilibrium of these proteins is required for successful pregnancy outcome. However, genetic variation from one individual to another results in variation in levels of Th1/Th2 cytokines, strength of identification of infectious agents by Toll-like receptors, and quality of progesterone recognition. Thus, a complex study encompassing effects of major SNPs of cytokine, TLR, and PGR genes on the risk of miscarriage is needed. In this study, we investigated SNPs of 9 genes (TLR2 G753A, TLR4 C399T, TLR9 G2848A, TGF-β1 C509T, PGR PROGINS, IL-6 G174C, IL-8 C781T, IL-10 C592A, and TNFα G308A) in 106 women, whose pregnancy ended in miscarriage, and 74 women, who delivered in term without any pregnancy complication. All participants are from Ukrainian population. As a result, TLR9 and IL-10 SNPs have been found to play critical roles in the development of spontaneous abortion. TLR2, TLR4, IL-6, IL-8, and PGR SNPs were identified as secondary factors that can also affect the risk of miscarriage. There was no association found between TGF-β1 and TNFα polymorphisms and miscarriage.
Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA, (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell.
We introduce Progressive Prompts -a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task and sequentially concatenates it with the previously learned prompts, while keeping the base model frozen. Experiments on standard continual learning benchmarks show that our approach outperforms state-of-the-art methods, with an improvement >20% in average test accuracy over the previous best-preforming method on T5 model. We also explore a more challenging continual learning setup with longer sequences of tasks and show that Progressive Prompts significantly outperforms prior methods.
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