Group Cognition in Problem Solving Dialogues: Analyzing differences between voice and computer transcripts

This project shadows the work of student groups in Math 110, a quantitative literacy class, engaged in exploratory learning excercises. An instructor monitors these groups by both walking around the room and observing group conversation at another computer. Our goal is to put this exercise online, and as a result leave the entire monitoring process up to the computer, assuming the role that the instructor traditionally assumes. Using annotation techniques to decipher meaning in dialogue of students working in groups for a Math 110, we try to see how students collaborate to solve problems together. “Bits of realization”, conversation, and problem solving tags are sorted out and gathered to identify the main points that are expressed during the problem solving of the two-person game, Poison. Expanding upon previous research done by other students, we are able to add bits of realization that students encounter in their work. Our first effort is to explore the differences between voice recorded dialogue and computer-mediated chat dialogue

Group Cognition in Problem Solving Dialogues: Analyzing differences between voice and computer transcripts

This project shadows the work of student groups in Math 110, a quantitative literacy class, engaged in exploratory learning excercises. An instructor monitors these groups by both walking around the room and observing group conversation at another computer. Our goal is to put this exercise online, and as a result leave the entire monitoring process up to the computer, assuming the role that the instructor traditionally assumes. Using annotation techniques to decipher meaning in dialogue of students working in groups for a Math 110, we try to see how students collaborate to solve problems together. “Bits of realization”, conversation, and problem solving tags are sorted out and gathered to identify the main points that are expressed during the problem solving of the two-person game, Poison. Expanding upon previous research done by other students, we are able to add bits of realization that students encounter in their work. Our first effort is to explore the differences between voice recorded dialogue and computer-mediated chat dialogue

Exploring the gap between dynamic and constraint-based models of metabolism

Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of E. coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identied by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.This research was supported by PhD Grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the Development of Improved Microbial Cell Factories" (MIT-Pt/BS-BB/0082/2008)

Regulation of the DC response to β-glucan by the IL-1-induced nuclear factor IκB-ζ.

<p>(A) Human monocyte-derived DCs were cultured with or without particulate β-glucan and/or IL-1RA (25 µg/ml). Gene expression data are from the experiment of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone-0114516-g004" target="_blank">Figure 4A</a> and results shown are normalized mRNA counts from technical triplicates from one representative donor out of 8 analyzed. (B) Human monocyte-derived DCs were cultured for 12 h in the indicated condition. IL-1RA was used at 250 µg/ml. Total extracts were analyzed by Western blotting with the indicated antibodies. Data shown are from one representative donor out of 9 tested. Arrows indicate specific bands, while asterisks show nonspecific bands. (C) Human monocyte-derived DCs were cultured in the presence or absence of particulate β-glucan and/or IL-1RA as indicated for 6 h and nuclear extracts prepared for Western blotting. Data are from one donor and are representative of those obtained in at least 3 independent experiments with cells from different donors. Arrows indicate specific bands, while asterisks nonspecific bands. (D) Human monocyte-derived DCs from different donors were stimulated for 12 h with β-glucan in presence of transfection reagent (none) after pre-incubation with a non-targeting siRNA pool (ctr siRNA) or a pool of four siRNA directed against <i>NFKBIZ</i> (<i>NFBIZ</i> siRNA). mRNA levels in cell lysates for 64 selected genes were quantitated by NanoString's nCounter technology and the map shows genes with significant changes in expression (FDR<0.1) following knockdown of <i>NFKBIZ</i>. Results are expressed as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone-0114516-g003" target="_blank">Figure 3</a>.</p

IL-1-mediated transcriptional regulation of β-glucan-induced late immunoregulatory cytokines in human DCs.

<p>(A) Human monocyte-derived DCs were cultured with or without particulate β-glucan and IL-1RA (2.5 µg/ml). Transcript accumulation was determined by absolute Real-Time qRT-PCR using total RNA and primers (Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone.0114516.s001" target="_blank">File S1</a>) for the detection of either primary or mature transcripts. Results are mean, n = 4. The times when significant differences were found as well as their p values are indicated in the figure. (B) Human monocyte-derived DCs were cultured in the presence or absence of particulate β-glucan and IL-1RA (25 µg/ml) for 6 h. ChIP was performed using sheared chromatin from fixed cells. DNA-protein complexes were immunoprecipitated with control IgG or RNAPII antibody (anti-RNAPII). Eluted DNA was analyzed by Real-Time qRT-PCR using primers (Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone.0114516.s001" target="_blank">File S1</a>) specific for <i>IL6</i>, <i>IL12B</i>, and <i>IL23A</i> promoter regions, proximal to the transcription start site. Results are mean ± SEM, n = 4. Statistics: paired t-test.</p

Expression profiles of immunoregulatory genes in DCs activated by β-glucan or LPS.

<p>Human monocyte-derived DCs were cultured in the presence or absence of particulate β-glucan or LPS for the times shown. mRNA levels in cell lysates for 64 selected genes were quantitated by NanoString's nCounter technology. The gene symbols are listed in the leftmost column of the heat map. Technical replicates were collapsed and genes hierarchically clustered (rows, genes; columns, donors). Data from individual donors are expressed as fold change between the mRNA counts of each condition and the corresponding average levels of mRNA (baseline) across donors, times, and treatments.</p

IFN-γ-mediated regulation of immune response to β-glucan <i>via</i> inhibition of IL-1β and IκB-ζ in activated DCs.

<p>(A) Human monocyte-derived DCs, unprimed or primed overnight with IFN-γ were stimulated or not for 12 h with β-glucan as indicated. mRNA levels in cell lysates for 64 selected genes were quantitated by NanoString's nCounter technology and the map shows genes with significant changes in expression (FDR<0.1) upon IFN-γ. Results from 3 different donors are expressed as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone-0114516-g003" target="_blank">Figure 3</a>. (B) Human monocyte-derived DCs, unprimed or primed overnight with IFN-γ were stimulated or not for 24 h with β-glucan as indicated. Protein secretion in the culture supernatants was measured by ELISA. Results are mean ± SEM, n = 7. Statistics: Tobit model analysis. (C) Human monocyte-derived DCs, unprimed or primed overnight with IFN-γ were cultured in the presence or not of particulate β-glucan as indicated for 6 h and nuclear extracts prepared for Western blotting. Data are from one donor and are representative of those obtained in at least 3 independent experiments with cells derived from different donors. Arrows indicate specific bands, while asterisks nonspecific bands. (D) Optical density of nuclear IκB-ζ bands from Western blotting analysis in (C). Data shown are fold induction over background from unstimulated cells. Results are mean ± SEM, n = 3. Statistics: t-test.</p

Regulation of the DC response to β-glucan or LPS by IL-1, TNF, and IFN-I.

<p>(A) Human monocytes-derived DCs were stimulated with particulate β-glucan or LPS, in the presence or absence of IL-1RA (25 µg/ml) or the indicated neutralizing antibodies, for the times shown. mRNA levels in cell lysates for 64 selected genes were quantitated by NanoString's nCounter technology. The gene symbols are listed in the leftmost column of the heat map. Technical replicates were collapsed and genes hierarchically clustered (rows, genes; columns, donors). Results from individual donors were analyzed as fold change between the mRNA counts of each condition and the corresponding average levels of mRNA (baseline) across donors, times, and treatments. Final data are expressed as fold change between the mRNA counts of each condition and the corresponding baseline mRNA counts from the same cells in the absence of IL-1RA or antibodies (red, stimulation from baseline; blue, inhibition from baseline; yellow, unchanged from baseline). The heat map reports the results with all the genes analyzed by NanoString technology. The data for all the comparisons were analyzed by one-way ANOVA model using Methods of Moments <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone.0114516-Eisenhart1" target="_blank">[65]</a>. The modulated genes mentioned in the text are those in which the changes induced by the perturbations were significant (p<0.05) at least at 12 h post-stimulation. (B) Human monocyte-derived DCs were cultured for 24 h as in (A). Protein secretion in the culture supernatants was measured by ELISA. Results are mean ± SEM, n = 5 to 10. Statistics: paired t-test when measurements were uncensored or the Tobit model analysis in presence of left-censored data.</p

Gene expression in β-glucan-stimulated human DCs.

<p>(A) Human monocyte-derived DCs were cultured for 24 h with or without particulate β-glucan or LPS. Protein secretion was measured by ELISA in culture supernatants. Results are mean ± SEM, n = 12 to 31. Statistics: Wilcoxon signed-rank test (TNF, IL-6, IL-12p40, and IL-10) or Tobit model analysis (IL-1β, IL-23, IL-1α, IL-18, and IL-12p70). (B and C) Human monocyte-derived DCs were cultured in the presence (solid lines) or absence (dashed lines) of particulate β-glucan for the times shown. Transcript accumulation was determined by absolute Real-Time qRT-PCR using total RNA and primers detecting mature transcripts (Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone.0114516.s001" target="_blank">File S1</a>). Protein secretion was measured by ELISA in the culture supernatants. Results are mean, n = 6 to 8 for transcripts, n = 5 to 13 for proteins. (D) Gene expression in human monocyte-derived DCs was evaluated by microarrays after activation with β-glucan for the indicated times. The heat map shows differentially expressed [≥2-fold change in expression (FDR <0.1) in at least one time point compared to the pre-stimulation level] genes (rows) in cells from three different donors (columns). The mRNA profiles were hierarchically clustered and subdivided into six groups (leftmost column of each heat map) according to the time when β-glucan controlled their expression. (E) Cluster models (Cluster ONE Cytoscape plugin) illustrating the pathways associated with the 3 groups of genes induced by β-glucan (GO pathway analysis) (left panels) and their predicted TFs (TRANSFAC database) (right panels). Each node of the pathway clusters depicts a single pathway. Similar pathways were grouped under a single representative name. Each node of the TF clusters indicates a redundant name or a predicted binding site for the listed TFs. The size of the nodes indicates the significance of the association of the pathway or the potential binding site for a TF.</p

Gene expression in β-glucan-stimulated human DCs and its modulation by IL-1RA.

<p>(A) The gene expression data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone-0114516-g001" target="_blank">Figure 1D</a> were analyzed using the Ingenuity data set. The list of genes from groups 4 and 5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114516#pone-0114516-g001" target="_blank">Figure 1D</a> was used to identify the potential regulators, whereas the genes in groups 5 and 6 were examined as potential targets. The cluster model shown (Cluster ONE Cytoscape plugin) illustrates predicted regulators (marked symbols in red circles) of the gene signature (small symbols) induced by β-glucan in monocyte-derived DCs. The dimensions of the red circles are proportionate to the predicted impact of the candidate regulators in the response to β-glucan. (B) Gene expression (microarrays) in human monocyte-derived DCs activated with β-glucan with or without IL-1RA (2.5 µg/ml) for the indicated times. The heat map includes the mRNA profiles of differentially expressed [≥2-fold change in expression (FDR<0.1) in the presence or absence of IL-1RA] genes (rows) in cells from three different donors (columns). The mRNA profiles were hierarchically clustered and then subdivided into six groups (leftmost column) according to the time when β-glucan controls their expression. (C) Cluster models with predicted TFs for the genes in groups 4, 5, and 6 repressed or promoted by IL-1.</p