g., IFN-g and LPS). Upregulation of platelet MHC-I during sepsis increases antigen cross-presentation and interactions with CD8+ T cells in an antigen-specific manner. Using a platelet lineage specific MHC-I deficient mouse strain (B2mf/f--Pf4Cre), we demonstrate that platelet MHC-I regulates antigen-specific CD8+ T cell proliferation in vitro, as well as the number and functional responses of CD8+ T cells in vivo during sepsis. Loss of platelet MHC-I reduced sepsis-associated mortality in mice in an antigen specific setting. These data identify a new mechanism by which platelets, through MHC-I, process and cross-present antigens, engage antigen specific CD8+ T cells, and regulate CD8+ T cell number, functional responses, and outcomes during sepsis.Endosperm is an angiosperm innovation central to their reproduction whose development, and thus seed viability, is controlled by genomic imprinting, where expression from certain genes is parent-specific. Unsuccessful imprinting has been linked to failed inter-specific and inter-ploidy hybridization. Despite their importance in plant speciation, the underlying mechanisms behind these endosperm-based barriers remain poorly understood. Here, we describe one such barrier between diploid Mimulus guttatus and tetraploid M. luteus. The two parents differ in endosperm DNA methylation, expression dynamics, and imprinted genes. Hybrid seeds suffer from underdeveloped endosperm, reducing viability, or arrested endosperm and seed abortion when M. guttatus or M. luteus is seed parent, respectively, and transgressive methylation and expression patterns emerge. The two inherited M. luteus subgenomes, genetically distinct but epigenetically similar, are expressionally dominant over the M. guttatus genome in hybrid embryos and especially their endosperm, where paternal imprints are perturbed.  https://www.selleckchem.com/products/d-4476.html  In aborted seeds, de novo methylation is inhibited, potentially owing to incompatible paternal instructions of imbalanced dosage from M. guttatus imprints. We suggest that diverged epigenetic/regulatory landscapes between parental genomes induce epigenetic repatterning and global shifts in expression, which, in endosperm, may uniquely facilitate incompatible interactions between divergent imprinting schemes, potentially driving rapid barriers.Predicting upcoming events is a critical function of the brain, and language provides a fertile testing ground for studying prediction, as comprehenders use context to predict features of upcoming words. Many aspects of the mechanisms of prediction remain elusive, partly due to a lack of methodological tools to probe prediction formation in the moment. To elucidate what features are neurally preactivated and when, we used representational similarity analysis on previously collected sentence reading data. We compared EEG activity patterns elicited by expected and unexpected sentence final words to patterns from the preceding words of the sentence, in both strongly and weakly constraining sentences. Pattern similarity with the final word was increased in an early time window following the presentation of the pre-final word, and this increase was modulated by both expectancy and constraint. This was not seen at earlier words, suggesting that predictions were precisely timed. Additionally, pre-final word activity-the predicted representation-had negative similarity with later final word activity, but only for strongly expected words. These findings shed light on the mechanisms of prediction in the brain rapid preactivation occurs following certain cues, but the predicted features may receive reduced processing upon confirmation.The brain signature concept aims to characterize brain regions most strongly associated with an outcome of interest. Brain signatures derive their power from data-driven searches that select features based solely on performance metrics of prediction or classification. This approach has important potential to delineate biologically relevant brain substrates for prediction or classification of future trajectories. Recent work has used exploratory voxel-wise or atlas-based searches, with some using machine learning techniques to define salient features. These have shown undoubted usefulness, but two issues remain. The preponderance of recent work has been aimed at categorical rather than continuous outcomes, and it is rare for non-atlas reliant voxel-based signatures to be reported that would be useful for modelling and hypothesis testing. We describe a cross-validated signature region model for structural brain components associated with baseline and longitudinal episodic memory across cognitively heterogeneousance metric was the adjusted R2 coefficient of determination of each model explaining outcomes in two cohorts other than where it was computed. We compared within-cohort performances of signature models against each other and against other recent signature models of episodic memory. Findings were (i) two independently generated signature region of interest models performed similarly in a third separate cohort; (ii) a signature region of interest generated in one imaging cohort replicated its performance level when explaining cognitive outcomes in each of other, separate cohorts; and (iii) this approach better explained baseline and longitudinal memory than other recent theory-driven and data-driven models. This suggests our approach can generate signatures that may be easily and robustly applied for modelling and hypothesis testing in mixed cognition cohorts.The COVID-19 pandemic has seen an unprecedented response from the sequencing community. Leveraging the sequence data from more than 140,000 SARS-CoV-2 genomes, we study mutation rates and selective pressures affecting the virus. Understanding the processes and effects of mutation and selection has profound implications for the study of viral evolution, for vaccine design, and for the tracking of viral spread. We highlight and address some common genome sequence analysis pitfalls that can lead to inaccurate inference of mutation rates and selection, such as ignoring skews in the genetic code, not accounting for recurrent mutations, and assuming evolutionary equilibrium. We find that two particular mutation rates, G →U and C →U, are similarly elevated and considerably higher than all other mutation rates, causing the majority of mutations in the SARS-CoV-2 genome, and are possibly the result of APOBEC and ROS activity. These mutations also tend to occur many times at the same genome positions along the global SARS-CoV-2 phylogeny (i.