The structures of the liver X receptor LXR (NR1H2) have been determined in complexes with two synthetic ligands, T0901317 and GW3965, to 2.1 and 2.4 Å, respectively. Together with its isoform LXR␣ (NR1H3) it regulates target genes involved in metabolism and transport of cholesterol and fatty acids. The two LXR structures reveal a flexible ligand-binding pocket that can adjust to accommodate fundamentally different ligands. The ligand-binding pocket is hydrophobic but with polar or charged residues at the two ends of the cavity. T0901317 takes advantage of this by binding to His-435 close to H12 while GW3965 orients itself with its charged group in the opposite direction. Both ligands induce a fixed "agonist conformation" of helix H12 (also called the AF-2 domain), resulting in a transcriptionally active receptor.Liver X receptors (LXR) 1 are members of the superfamily of nuclear receptors. These transcription factors regulate target genes through a dynamic series of interactions with specific DNA response elements as well as transcriptional coregulators. The binding of ligand has profound effects on these interactions and has the potential to trigger both gene activation and, in some cases, gene silencing. There are 48 sequence-related nuclear receptors in humans and the family comprises receptors that recognize hormones, both steroidal and non-steroidal, but also receptors responding to metabolic intermediates and to xenobiotics. There are also a number of so-called orphan receptors where the natural ligand is unknown. Some of the receptors show a very specific and high affinity ligand binding, like the thyroid hormone receptors, whereas others have a substantially lower affinity for their ligands and are less discriminating in their ligand selectivity. Like many of the other nonsteroid hormone receptors, LXR functions as a heterodimer with the retinoid X receptor (RXR) to regulate gene expression (1, 2). Together with peroxisome proliferator-activated receptor (PPAR) and farnesoid X receptor (FXR), LXRs represent a subclass of so-called permissive RXR heterodimers. In this subclass, the RXR heterodimers can be activated independently by either the RXR ligand, the partner's ligand, or synergistically by both (3).LXRs consist of two closely related receptor isoforms encoded by separate genes, LXR␣ (NR1H3) and LXR (NR1H2). LXR␣ shows tissue-restricted expression with the highest mRNA levels in the liver and somewhat lower levels in the kidney, small intestine, spleen, and adrenal gland (4, 5). In contrast, LXR is ubiquitously expressed (6, 7). Both LXR isoforms can be activated by specific oxysterols that are formed in vivo (2,8,9). In view of the high degree of homology between the LXR isoforms (75% identity in the ligand-binding domain (LBD), 54% identity overall), it is perhaps not surprising that few subtypespecific biological responses have been described and that information on subtype selective ligands is limited. LXRs have been shown to regulate several genes involved in cholesterol and lipid homeos...
Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.
Abstract-In contrast to the existing approaches to bisimulation for fuzzy systems, we introduce a behavioral distance to measure the behavioral similarity of states in a nondeterministic fuzzy-transition system. This behavioral distance is defined as the greatest fixed point of a suitable monotonic function and provides a quantitative analogue of bisimilarity. The behavioral distance has the important property that two states are at zero distance if and only if they are bisimilar. Moreover, for any given threshold, we find that states with behavioral distances bounded by the threshold are equivalent. In addition, we show that two system combinators-parallel composition and productare non-expansive with respect to our behavioral distance, which makes compositional verification possible.
D espite our understanding that social media and online healthcare communities can help to eliminate health information asymmetry and improve patients' self-care engagement, we have yet to understand what happens when patients have access to others' health data and how patients' access to these shared experiences and opinions influence their health knowledge and perceived treatment outcome. In this study, we apply social information processing theory and incorporate (1) uncertainty of a treatment, (2) information exposure, and (3) credibility of the information source into patients' information evaluation function to assess how patients utilize shared health information and experiences. An empirical model, which combines various aspects of patients' firsthand experiences about treatments into a single construct, yields empirical evidence that patients' perceived treatment outcome is prone to social influence from other patients' shared experiences. By disaggregating the sources of social influence, we find that social influence created by generalized others in the community outweighs that by familiar others of one's intimate social group. In addition, we find that other factors, such as positive sentiment in comments and patients' prior experiences, also affect patients' perceived treatment outcome. Based on our findings, implications for health promotion and health behaviors are presented.
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