Modeling the dynamics of bivalent histone modifications

Epigenetic modifications to histones may promote either activation or repression of the transcription of nearby genes. Recent experimental studies show that the promoters of many lineage-control genes in stem cells have "bivalent domains" in which the nucleosomes contain both active (H3K4me3) and repressive (H3K27me3) marks. It is generally agreed that bivalent domains play an important role in stem cell differentiation, but the underlying mechanisms remain unclear. Here we formulate a mathematical model to investigate the dynamic properties of histone modification patterns. We then illustrate that our modeling framework can be used to capture key features of experimentally observed combinatorial chromatin states.Comment: 23 pages, 10 figure

Duke University Health System Demand Response Prospectus

The Duke University Health System Demand Response Prospectus is a client-based Masters Project that explores the profitability and environmental impacts of enrolling Duke University Health System and Duke University into Duke Energy’s PowerShare demand response program. Demand response programs are mechanisms used by utilities to decrease energy demand during high-usage periods (e.g. hot days when air conditioning use is highest) by incentivizing their customers to reduce grid consumption for a limited time. This temporary demand reduction results in cost savings to utilities because it allows them to avoid using their most inefficient and expensive power plants. In our project, we analyze the economic, environmental, and regulatory feasibility of using Duke University and Duke Medicine emergency generators in a Duke Energy demand response program called PowerShare, more specifically the Generator Curtailment Option. Duke Carbon Offset Initiative credits, a Duke University funding mechanism to reduce carbon dioxide emissions, were also considered as a potential revenue source. In order to conduct the analysis, an MS Excel and Visual Basic model was created to calculate the impacts of enrollment. The model provided to the client was designed to offer an easy user interface to quickly conduct the analyses. It was also specially designed to offer the flexibility to incorporate future changes in the energy market and user preferences. The model results indicated that, while feasible, demand response enrollment is not currently attractive from environmental and financial perspectives. The financials are poor for two mains reasons. First, expected net revenues are strictly negative because PowerShare enrollment requires Duke University to re-enroll into paying a demand side management rider (DSM) to which they are currently exempt. The DSM fee, although minimal individually, amounts to an astronomical fee for large consumers like Duke University and Duke Medicine since it is charged per unit of energy purchased. Second, PowerShare curtailment compensation is lower than current cost of diesel fuel. From an environmental perspective, PowerShare is also not a favorable option. Instead of offering a carbon emissions reduction opportunity, PowerShare participation is actually expected to increase the amount of global carbon emissions because Duke University generators emit more carbon than Duke Energy’s natural gas peak usage plants

Doping Effects on the Performance of Paired Metal Catalysts for the Hydrogen Evolution Reaction

Metal heteroatoms dispersed in nitrogen-doped graphene display promising catalytic activity for fuel cell reactions such as the hydrogen evolution reaction (HER). Here we explore the effects of dopant concentration on the synergistic catalytic behaviour of a paired metal atom active site comprised of Co and Pt atoms. The metals are coordinated to six atoms in a vacancy of N-doped graphene. We find that HER activity is enhanced with increasing N concentration, where the free energy of hydrogen atom adsorption ranges from 0.23 to -0.42 eV as the doping changes from a single N atom doped in the pore, to fully doped coordination sites. The results indicated that the effect of N is to make the Co atom more active towards H adsorption and presents a means through which transition metals can be modified to make more effective and sustainable fuel cell catalysts

Memento Mori: The development and validation of the Death Reflection Scale

Despite its potential for advancing organizational behavior (OB) research, the topic of death awareness has been vastly understudied. Moreover, research on death awareness has predominantly focused on the anxiety‐provoking aspect of death‐related cognitions, thus overlooking the positive aspect of death awareness, death reflection. This gap is exacerbated by the lack of a valid research instrument to measure death reflection. To address this issue, we offer a systematic conceptualization of death reflection, develop the Death Reflection Scale, and assess its psychometric properties across four studies. Further, using a sample of 268 firefighters, we examine whether death reflection buffers the detrimental impact of mortality cues at work on employee well‐being and safety performance. Results provide strong support for the psychometric properties of the Death Reflection Scale. Further, moderation analysis indicates death reflection weakens the negative effect of mortality cues on firefighters' safety performance. Overall, these findings suggest the newly developed Death Reflection Scale will prove useful in future research on death‐related cognitions

Transfer Learning in Natural Language Processing through Interactive Feedback

Machine learning models cannot easily adapt to new domains and applications. This drawback becomes detrimental for natural language processing (NLP) because language is perpetually changing. Across disciplines and languages, there are noticeable differences in content, grammar, and vocabulary. To overcome these shifts, recent NLP breakthroughs focus on transfer learning. Through clever optimization and engineering, a model can successfully adapt to a new domain or task. However, these modifications are still computationally inefficient or resource-intensive. Compared to machines, humans are more capable at generalizing knowledge across different situations, especially in low-resource ones. Therefore, the research on transfer learning should carefully consider how the user interacts with the model. The goal of this dissertation is to investigate “human-in-the-loop” approaches for transfer learning in NLP. First, we design annotation frameworks for inductive transfer learning, which is the transfer of models across tasks. We create an interactive topic modeling system for users to find topics useful for classifying documents in multiple languages. The user-constructed topic model bridges improves classification accuracy and bridges cross-lingual gaps in knowledge. Next, we look at popular language models, like BERT, that can be applied to various tasks. While these models are useful, they still require a large amount of labeled data to learn a new task. To reduce labeling, we develop an active learning strategy which samples documents that surprise the language model. Users only need to annotate a small subset of these unexpected documents to adapt the language model for text classification. Then, we transition to user interaction in transductive transfer learning, which is the transfer of models across domains. We focus our efforts on low-resource languages to develop an interactive system for word embeddings. In this approach, the feedback from bilingual speakers refines the cross-lingual embedding space for classification tasks. Subsequently, we look at domain shift for tasks beyond text classification. Coreference resolution is fundamental for NLP applications, like question-answering and dialogue, but the models are typically trained and evaluated on one dataset. We use active learning to find spans of text in the new domain for users to label. Furthermore, we provide important insights on annotating spans for domain adaptation. Finally, we summarize the contributions of each chapter. We focus on aspects like the scope of applications and model complexity. We conclude with a discussion of future directions. Researchers may extend the ideas in our thesis to topics like user-centric active learning and proactive learning

Pharmacological modulation of oncogenic Ras by natural products and their derivatives: renewed hope in the discovery of novel anti-Ras drugs

Oncogenic rat sarcoma (Ras) is linked to the most fatal cancers such as those of the pancreas, colon, and lung. Decades of research to discover an efficacious drug that can block oncogenic Ras signaling have yielded disappointing results; thus, Ras was considered “undruggable” until recently. Inhibitors that directly target Ras by binding to previously undiscovered pockets have been recently identified. Some of these molecules are either isolated from natural products or derived from natural compounds. In this review, we described the potential of these compounds and other inhibitors of Ras signaling in drugging Ras. We highlighted the modes of action of these compounds in suppressing signaling pathways activated by oncogenic Ras, such as mitogen-activated protein kinase (MAPK) signaling and the phosphoinositide-3-kinase (PI3K) pathways. The anti-Ras strategy of these compounds can be categorized into four main types: inhibition of Ras–effector interaction, interference of Ras membrane association, prevention of Ras–guanosine triphosphate (GTP) formation, and downregulation of Ras proteins. Another promising strategy that must be validated experimentally is enhancement of the intrinsic Ras–guanosine triphosphatase (GTPase) activity by small chemical entities. Among the inhibitors of Ras signaling that were reported thus far, salirasib and TLN-4601 have been tested for their clinical efficacy. Although both compounds passed phase I trials, they failed in their respective phase II trials. Therefore, new compounds of natural origin with relevant clinical activity against Ras-driven malignancies are urgently needed. Apart from salirasib and TLN-4601, some other compounds with a proven inhibitory effect on Ras signaling include derivatives of salirasib, sulindac, polyamine, andrographolide, lipstatin, levoglucosenone, rasfonin, and quercetin

Interpreting Patterns of Gene Expression: Signatures of Coregulation, the Data Processing Inequality, and Triplet Motifs

Various methods of reconstructing transcriptional regulatory networks infer transcriptional regulatory interactions (TRIs) between strongly coexpressed gene pairs (as determined from microarray experiments measuring mRNA levels). Alternatively, however, the coexpression of two genes might imply that they are coregulated by one or more transcription factors (TFs), and do not necessarily share a direct regulatory interaction. We explore whether and under what circumstances gene pairs with a high degree of coexpression are more likely to indicate TRIs, coregulation or both. Here we use established TRIs in combination with microarray expression data from both Escherichia coli (a prokaryote) and Saccharomyces cerevisiae (a eukaryote) to assess the accuracy of predictions of coregulated gene pairs and TRIs from coexpressed gene pairs. We find that coexpressed gene pairs are more likely to indicate coregulation than TRIs for Saccharomyces cerevisiae, but the incidence of TRIs in highly coexpressed gene pairs is higher for Escherichia coli. The data processing inequality (DPI) has previously been applied for the inference of TRIs. We consider the case where a transcription factor gene is known to regulate two genes (one of which is a transcription factor gene) that are known not to regulate one another. According to the DPI, the non-interacting gene pairs should have the smallest mutual information among all pairs in the triplets. While this is sometimes the case for Escherichia coli, we find that it is almost always not the case for Saccharomyces cerevisiae. This brings into question the usefulness of the DPI sometimes employed to infer TRIs from expression data. Finally, we observe that when a TF gene is known to regulate two other genes, it is rarely the case that one regulatory interaction is positively correlated and the other interaction is negatively correlated. Typically both are either positively or negatively correlated

Multiparametric MRI and [18F]fluorodeoxyglucose positron emission tomography imaging is a potential prognostic imaging biomarker in recurrent glioblastoma

Purpose/objectivesMultiparametric advanced MR and [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes.Methods/materialsWe retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria.Results17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5 months [interquartile range (IQR) 3.7–12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10 months (IQR 7.2–13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUVmax to the normal contralateral brain (SUVmax/NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ≤1,400 × 10−6 mm2/s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUVmax/NB index >1.5 and ADC ≤1,400 × 10−6 mm2/s defined high-risk patients (n = 7), SUVmax/NB index ≤1.5 and ADC >1,400 × 10−6 mm2/s defined low-risk patients (n = 11), and intermediate-risk (n = 12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8 months (p < 0.01). Median PFS were 10.0, 4.4, and 1.9 months (p = 0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% (p = 0.04).ConclusionRecurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence