The brush border is comprised of microvilli surface protrusions on the apical surface of epithelia. This specialized structure greatly increases absorptive surface area and plays crucial roles in human health. However, transcriptional regulatory networks controlling brush border genes are not fully understood. Here, we identify that hepatocyte nuclear factor 4 (HNF4) transcription factor is a conserved and important regulator of brush border gene program in multiple organs, such as intestine, kidney and yolk sac. Compromised brush border gene signatures and impaired transport were observed in these tissues upon HNF4 loss. By ChIP-seq, we find HNF4 binds and activates brush border genes in the intestine and kidney. H3K4me3 HiChIP-seq identifies that HNF4 loss results in impaired chromatin looping between enhancers and promoters at gene loci of brush border genes, and instead enhanced chromatin looping at gene loci of stress fiber genes in the intestine. This study provides comprehensive transcriptional regulatory mechanisms and a functional demonstration of a critical role for HNF4 in brush border gene regulation across multiple murine epithelial tissues.
I n a modern aircraft, data fusion is the process by which data about the environment are gathered, coinbiaed, reasoned over, and presented to the pilot. Lkterinining which data to gather is obviously important to achieving effective data fusion. But the need for data depends OIL uncertain, interrelated and dynamic factors. This fact has pushed data-gathering determination beyond the ability of the human aitd led researchers to study structured decision-aiding systems called seiuor managers. This paper discusses sensor tnanagerneiit, focusing first O I L the problem it poses i n a modern tactical aircraft aitd then on attributes that would be desirable i n an effective sensor manager. Several techniques that offer promise are discussed.
Research reported in this article is motivated, in part, by current U.S. military programs aimed at the development of efficient data integration and sensor management methods capable of handling large sensor suites and achieving robust target recognition performance in real time scenarios. Modern sensor systems have shown good recognition abilities against a few isolated targets. However, these capabilities decline steeply when multiple sensors are acting against large target groups under realistic conditions requiring dynamic allocation of the sensor resources and efficient on-line integration and disambiguation of multiple sensor outputs. Neural networks and other sensor integration technologies have been inspired by cognitive models attributing human perceptual integration to parallel processing and convergence of simultaneous data streams. This article explores a different model emphasizing serial processing and association of consecutive memory traces in the Long Term Memory (LTM) into a globally connected memory structure called a Virtual Associative Network (VAN). Information integration in VAN is called blending. Target representation is constructed dynamically from the segments of virtual net matched serially against the input segments in the Short Term Memory (STM). This article will elaborate the concept of blending, reference its biological foundations, explain the difference between information blending and conventional sensor fusion techniques, and demonstrate blending applications in a large scale sensor management task.
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