Pedagogical Agents for Fostering Question-Asking Skills in Children

Question asking is an important tool for constructing academic knowledge, and a self-reinforcing driver of curiosity. However, research has found that question asking is infrequent in the classroom and children's questions are often superficial, lacking deep reasoning. In this work, we developed a pedagogical agent that encourages children to ask divergent-thinking questions, a more complex form of questions that is associated with curiosity. We conducted a study with 95 fifth grade students, who interacted with an agent that encourages either convergent-thinking or divergent-thinking questions. Results showed that both interventions increased the number of divergent-thinking questions and the fluency of question asking, while they did not significantly alter children's perception of curiosity despite their high intrinsic motivation scores. In addition, children's curiosity trait has a mediating effect on question asking under the divergent-thinking agent, suggesting that question-asking interventions must be personalized to each student based on their tendency to be curious.Comment: Accepted at CHI 202

Targeting of epigenetic co-dependencies enhances anti-AML efficacy of Menin inhibitor in AML with MLL1-r or mutant NPM1

Monotherapy with Menin inhibitor (MI), e.g., SNDX-5613, induces clinical remissions in patients with relapsed/refractory AML harboring MLL1-r or mtNPM1, but most patients either fail to respond or eventually relapse. Utilizing single-cell RNA-Seq, ChiP-Seq, ATAC-Seq, RNA-Seq, RPPA, and mass cytometry (CyTOF) analyses, present pre-clinical studies elucidate gene-expression correlates of MI efficacy in AML cells harboring MLL1-r or mtNPM1. Notably, MI-mediated genome-wide, concordant, log2 fold-perturbations in ATAC-Seq and RNA-Seq peaks were observed at the loci of MLL-FP target genes, with upregulation of mRNAs associated with AML differentiation. MI treatment also reduced the number of AML cells expressing the stem/progenitor cell signature. A protein domain-focused CRISPR-Cas9 screen in MLL1-r AML cells identified targetable co-dependencies with MI treatment, including BRD4, EP300, MOZ and KDM1A. Consistent with this, in vitro co-treatment with MI and BET, MOZ, LSD1 or CBP/p300 inhibitor induced synergistic loss of viability of AML cells with MLL1-r or mtNPM1. Co-treatment with MI and BET or CBP/p300 inhibitor also exerted significantly superior in vivo efficacy in xenograft models of AML with MLL1-r. These findings highlight novel, MI-based combinations that could prevent escape of AML stem/progenitor cells following MI monotherapy, which is responsible for therapy-refractory AML relapse

A simulation study comparing aberration detection algorithms for syndromic surveillance

BACKGROUND: The usefulness of syndromic surveillance for early outbreak detection depends in part on effective statistical aberration detection. However, few published studies have compared different detection algorithms on identical data. In the largest simulation study conducted to date, we compared the performance of six aberration detection algorithms on simulated outbreaks superimposed on authentic syndromic surveillance data. METHODS: We compared three control-chart-based statistics, two exponential weighted moving averages, and a generalized linear model. We simulated 310 unique outbreak signals, and added these to actual daily counts of four syndromes monitored by Public Health – Seattle and King County's syndromic surveillance system. We compared the sensitivity of the six algorithms at detecting these simulated outbreaks at a fixed alert rate of 0.01. RESULTS: Stratified by baseline or by outbreak distribution, duration, or size, the generalized linear model was more sensitive than the other algorithms and detected 54% (95% CI = 52%–56%) of the simulated epidemics when run at an alert rate of 0.01. However, all of the algorithms had poor sensitivity, particularly for outbreaks that did not begin with a surge of cases. CONCLUSION: When tested on county-level data aggregated across age groups, these algorithms often did not perform well in detecting signals other than large, rapid increases in case counts relative to baseline levels

Light and flow regimes regulate the metabolism of rivers

Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial counterparts, most river ecosystems respire far more carbon than they fix and have less pronounced and consistent seasonality in their metabolic rates. We find that variation in annual solar energy inputs and stability of flows are the primary drivers of GPP and ER across rivers. A classification schema based on these drivers advances river science and informs management.We thank Ted Stets, Jordan Read, Tom Battin, Sophia Bonjour, Marina Palta, and members of the Duke River Center for their help in developing these ideas. This work was supported by grants from the NSF 1442439 (to E.S.B. and J.W.H.), 1834679 (to R.O.H.), 1442451 (to R.O.H.), 2019528 (to R.O.H. and J.R.B.), 1442140 (to M.C.), 1442451 (to A.M.H.), 1442467 (to E.H.S.), 1442522 (to N.B.G.), 1624807 (to N.B.G.), and US Geological Survey funding for the working group was supported by the John Wesley Power Center for Analysis and Synthesis. Phil Savoy contributed as a postdoc- toral associate at Duke University and as a postdoctoral associate (contractor) at the US Geological Survey

PRMT1-dependent regulation of RNA metabolism and DNA damage response sustains pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer that has remained clinically challenging to manage. Here we employ an RNAi-based in vivo functional genomics platform to determine epigenetic vulnerabilities across a panel of patient-derived PDAC models. Through this, we identify protein arginine methyltransferase 1 (PRMT1) as a critical dependency required for PDAC maintenance. Genetic and pharmacological studies validate the role of PRMT1 in maintaining PDAC growth. Mechanistically, using proteomic and transcriptomic analyses, we demonstrate that global inhibition of asymmetric arginine methylation impairs RNA metabolism, which includes RNA splicing, alternative polyadenylation, and transcription termination. This triggers a robust downregulation of multiple pathways involved in the DNA damage response, thereby promoting genomic instability and inhibiting tumor growth. Taken together, our data support PRMT1 as a compelling target in PDAC and informs a mechanism-based translational strategy for future therapeutic development. Statement of significance PDAC is a highly lethal cancer with limited therapeutic options. This study identified and characterized PRMT1-dependent regulation of RNA metabolism and coordination of key cellular processes required for PDAC tumor growth, defining a mechanism-based translational hypothesis for PRMT1 inhibitors

Global change drivers and the risk of infectious disease

Anthropogenic change is contributing to the rise in emerging infectious diseases, but it remains unclear which global change drivers most increase disease and under what contexts. We amassed a dataset from the literature that includes 1,832 observations of infectious disease responses to global change drivers across 1,202 host-parasite combinations. We found that biodiversity loss, climate change, and introduced species were associated with increases in disease-related endpoints or harm (i.e., enemy release for introduced species), whereas urbanization was associated with decreases in disease endpoints. Natural biodiversity gradients, deforestation, forest fragmentation, and most classes of chemical contaminants had non-significant effects on these endpoints. Overall, these results were consistent across human and non-human diseases. Context-dependent effects of the global change drivers on disease were common and are discussed. These findings will help better target disease management and surveillance efforts towards global change drivers that increase disease.One-Sentence SummaryHere we quantify which global change drivers increase infectious diseases the most to better target global disease management and surveillance efforts

BET Bromodomain Inhibition as a Therapeutic Strategy to Target c-Myc

SummaryMYC contributes to the pathogenesis of a majority of human cancers, yet strategies to modulate the function of the c-Myc oncoprotein do not exist. Toward this objective, we have targeted MYC transcription by interfering with chromatin-dependent signal transduction to RNA polymerase, specifically by inhibiting the acetyl-lysine recognition domains (bromodomains) of putative coactivator proteins implicated in transcriptional initiation and elongation. Using a selective small-molecule bromodomain inhibitor, JQ1, we identify BET bromodomain proteins as regulatory factors for c-Myc. BET inhibition by JQ1 downregulates MYC transcription, followed by genome-wide downregulation of Myc-dependent target genes. In experimental models of multiple myeloma, a Myc-dependent hematologic malignancy, JQ1 produces a potent antiproliferative effect associated with cell-cycle arrest and cellular senescence. Efficacy of JQ1 in three murine models of multiple myeloma establishes the therapeutic rationale for BET bromodomain inhibition in this disease and other malignancies characterized by pathologic activation of c-Myc.PaperFlic

Epidemiological impact of waning immunization on a vaccinated population

This is an epidemiological SIRV model based study that is de- signed to analyze the impact of vaccination in containing infection spread, in a 4-tiered population compartment comprised of susceptible, infected, recov- ered and vaccinated agents. While many models assume a lifelong protection through vaccination, we focus on the impact of waning immunization due to conversion of vaccinated and recovered agents back to susceptible ones. Two asymptotic states exist, the \disease-free equilibrium" and the \endemic equi- librium" and we express the transitions between these states as function of the vaccination and conversion rates and using the basic reproduction number. We nd that the vaccination of newborns and adults have dierent consequences on controlling an epidemic. Also, a decaying disease protection within the re- covered sub-population is not sucient to trigger an epidemic on the linear level. We perform simulations for a parameter set modelling a disease with waning immunization like pertussis. For a diusively coupled population, a transition to the endemic state can proceed via the propagation of a traveling infection wave, described successfully within a Fisher-Kolmogorov framework

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453