Gordon, S. M. | Search bioRxiv

bioRxiv2019

Interferons drive development of novel interleukin-15-responsive macrophages in the uterus

Disruption in homeostasis of interleukin-15 (IL-15) is linked to poor maternal and fetal outcomes during pregnancy. The only cells described to respond to IL-15 at the early maternal-fetal interface have been natural killer (NK) cells. We now show a novel population of macrophages, evident in several organs but enriched in the uterus of mice and humans, expressing the {beta} chain of the IL-15 receptor complex (CD122) and responding to IL-15. CD122+ macrophages (CD122+Macs) are morphologic, phenotypic, and transcriptomic macrophages that can derive from bone marrow monocytes. CD122+Macs develop in the uterus and placenta with kinetics that mirror interferon (IFN) activity at the maternal-fetal interface. Macrophage colony-stimulating factor (M-CSF) permits macrophages to express CD122, and IFNs are sufficient to drive expression of CD122 on macrophages. Neither Type-I nor Type-II IFNs are required to generate CD122+Macs, however. In response to IL-15, CD122+Macs activate the ERK signaling cascade and enhance production of proinflammatory cytokines after stimulation with the Toll-like receptor 9 agonist CpG. Finally, we provide evidence of human cells that phenocopy murine CD122+Macs in secretory phase endometrium during the implantation window and in first-trimester uterine decidua. Our data support a model wherein IFNs local to the maternal-fetal interface direct novel IL-15-responsive macrophages with the potential to mediate IL-15 signals critical for optimal outcomes of pregnancy. The microarray data presented in this article have been submitted to the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE132353.

immunology↗

bioRxiv2019

Decoding P300 Variability using Convolutional Neural Networks

Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signal-to-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.

neuroscience↗