ABSTRACT
OBJECTIVE: To investigate the effects and mechanism of bromodomain and extra-terminal (BET) inhibitor JQ1 on the double-expressor lymphoma (DEL) cell lines. METHODS: Protein expressions of cMyc and BCL-2 in 3 lymphoma cell lines were checked by Western blot so as to identify DEL cell lines. CCK-8 assay was used to detect the effects of JQ1 on anti-proliferation in the DEL cell lines. Western blot and RT-PCR were used to measure the protein and mRNA expressions of cMyc, BCL-2 and BCL-6 in DEL cell lines which treated by JQ1. Flow cytometry was used to detect the effect of JQ1 on cell apoptosis. RESULTS: Based on the expressions of cMyc and BCL-2, the SU-DHL6 and OCILY3 cell lines were confirmed as DEL cell lines. CCK-8 assay showed that the proliferation of DEL cell lines was inhibited by JQ1, which was similar to non-DEL cell lines and mainly regulated the expression of cMyc and BCL-6 but not BCL-2. JQ1 had no effects on apoptosis in the DEL cell lines. CONCLUSION: BET inhibitor JQ1 has anti-tumor effect in the DEL cell lines, thus providing evidence for the therapeutic potential of BET inhibitor JQ1.
Subject(s)
Azepines , Proto-Oncogene Proteins c-myc , Apoptosis , Azepines/pharmacology , Cell Line, Tumor , Cell Proliferation , Humans , Proto-Oncogene Proteins c-myc/metabolism , Triazoles/pharmacology , Xenograft Model Antitumor AssaysABSTRACT
Human subject experiments are performed to assess the impact of artificial intelligence (AI) agents on distributed human design teams and individual human designers. In the team experiment, participants in teams of six develop and operate a drone fleet to deliver parcels routed to multiple locations of a target market. Among the design teams in the experiment, half of the design teams are human-only teams with no available AI agent. The other half of the design teams, designated as hybrid teams, have drone design and operation AI agents to advise them. Halfway through the team experiment, team structure is changed unexpectedly, requiring participants to adapt to the change. In the individual experiment, participants develop drones based on given design specifications, either on their own or with the availability of a drone design AI agent to advise them. During these experiments, participants configure, test, and share their designs and communicate with their teammates through an online research platform. The platform collects a step-by-step log of the actions made by participants. This article contains data sets collected from 44 teams (264 participants) in the team experiment and 73 participants in the individual experiment. These data sets can be used for behavioral analysis, sequence-based analysis, and natural language processing.
ABSTRACT
The Ebola virus in West Africa has infected almost 30,000 and killed over 11,000 people. Recent models of Ebola Virus Disease (EVD) have often made assumptions about how the disease spreads, such as uniform transmissibility and homogeneous mixing within a population. In this paper, we test whether these assumptions are necessarily correct, and offer simple solutions that may improve disease model accuracy. First, we use data and models of West African migration to show that EVD does not homogeneously mix, but spreads in a predictable manner. Next, we estimate the initial growth rate of EVD within country administrative divisions and find that it significantly decreases with population density. Finally, we test whether EVD strains have uniform transmissibility through a novel statistical test, and find that certain strains appear more often than expected by chance.
