Pharmacological Blockade of the Calcium Plateau Provides Neuroprotection Following Organophosphate Paraoxon Induced Status Epilepticus in Rats

Organophosphate (OP) compounds which include nerve agents and pesticides are considered chemical threat agents. Currently approved antidotes are crucial in limiting OP mediated acute mortality. However, survivors of lethal OP exposure exhibit delayed neuronal injury and chronic behavioral morbidities. In this study, we investigated neuroprotective capabilities of dantrolene and carisbamate in a rat survival model of paraoxon (POX) induced status epilepticus (SE). Significant elevations in hippocampal calcium levels were observed 48-h post POX SE survival, and treatment with dantrolene (10 mg/kg, i.m.) and carisbamate (90 mg/kg, i.m.) lowered these protracted calcium elevations. POX SE induced delayed neuronal injury as characterized by Fluoro Jade C labeling was observed in critical brain areas including the dentate gyrus, parietal cortex, amygdala, and thalamus. Dantrolene and carisbamate treatment provided significant neuroprotection against delayed neuronal damage in these brain regions when administered one-hour after POX-SE. These results indicate that dantrolene or carisbamate could be effective adjuvant therapies to the existing countermeasures to reduce neuronal injury and behavioral morbidities post OP SE survival

Observations and assessment of forest carbon dynamics following disturbance in North America

Disturbance processes of various types substantially modify ecosystem carbon dynamics both temporally and spatially, and constitute a fundamental part of larger landscape-level dynamics. Forests typically lose carbon for several years to several decades following severe disturbance, but our understanding of the duration and dynamics of post-disturbance forest carbon fluxes remains limited. Here we capitalize on a recent North American Carbon Program disturbance synthesis to discuss techniques and future work needed to better understand carbon dynamics after forest disturbance. Specifically, this paper addresses three topics: (1) the history, spatial distribution, and characteristics of different types of disturbance (in particular fire, insects, and harvest) in North America; (2) the integrated measurements and experimental designs required to quantify forest carbon dynamics in the years and decades after disturbance, as presented in a series of case studies; and (3) a synthesis of the greatest uncertainties spanning these studies, as well as the utility of multiple types of observations (independent but mutually constraining data) in understanding their dynamics. The case studies—in the southeast U.S., central boreal Canada, U.S. Rocky Mountains, and Pacific Northwest—explore how different measurements can be used to constrain and understand carbon dynamics in regrowing forests, with the most important measurements summarized for each disturbance type. We identify disturbance severity and history as key but highly uncertain factors driving post-disturbance carbon source-sink dynamics across all disturbance types. We suggest that imaginative, integrative analyses using multiple lines of evidence, increased measurement capabilities, shared models and online data sets, and innovative numerical algorithms hold promise for improved understanding and prediction of carbon dynamics in disturbance-prone forests

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

Accounting for quality improvement during the conduct of embedded pragmatic clinical trials within healthcare systems: NIH Collaboratory case studies

Embedded pragmatic clinical trials (ePCTs) and quality improvement (QI) activities often occur simultaneously within healthcare systems (HCSs). Embedded PCTs within HCSs are conducted to test interventions and provide evidence that may impact public health, health system operations, and quality of care. They are larger and more broadly generalizable than QI initiatives, and may generate what is considered high-quality evidence for potential use in care and clinical practice guidelines. QI initiatives often co-occur with ePCTs and address the same high-impact health questions, and this co-occurrence may dilute or confound the ability to detect change as a result of the ePCT intervention. During the design, pilot, and conduct phases of the large-scale NIH Collaboratory Demonstration ePCTs, many QI initiatives occurred at the same time within the HCSs. Although the challenges varied across the projects, some common, generalizable strategies and solutions emerged, and we share these as case studies. KEY LESSONS: Study teams often need to monitor, adapt, and respond to QI during design and the course of the trial. Routine collaboration between ePCT researchers and health systems stakeholders throughout the trial can help ensure research and QI are optimally aligned to support high-quality patient-centered care

Structural Basis for Apoptosis Inhibition by Epstein-Barr Virus BHRF1

Epstein-Barr virus (EBV) is associated with human malignancies, especially those affecting the B cell compartment such as Burkitt lymphoma. The virally encoded homolog of the mammalian pro-survival protein Bcl-2, BHRF1 contributes to viral infectivity and lymphomagenesis. In addition to the pro-apoptotic BH3-only protein Bim, its key target in lymphoid cells, BHRF1 also binds a selective sub-set of pro-apoptotic proteins (Bid, Puma, Bak) expressed by host cells. A consequence of BHRF1 expression is marked resistance to a range of cytotoxic agents and in particular, we show that its expression renders a mouse model of Burkitt lymphoma untreatable. As current small organic antagonists of Bcl-2 do not target BHRF1, the structures of it in complex with Bim or Bak shown here will be useful to guide efforts to target BHRF1 in EBV-associated malignancies, which are usually associated with poor clinical outcomes

Glucose and glutamine fuel protein O-GlcNAcylation to control T cell self-renewal and malignancy

Sustained glucose and glutamine transport are essential for activated T lymphocytes to support ATP and macromolecule biosynthesis. We now show that glutamine and glucose also fuel an indispensible dynamic regulation of intracellular protein O-GlcNAcylation at key stages of T cell development, transformation and differentiation. Glucose and glutamine are precursors of UDP-GlcNAc, a substrate for cellular glycosyltransferases. Immune activated T cells contained higher concentrations of UDP-GlcNAc and increased intracellular protein O-GlcNAcylation controlled by the enzyme O-GlcNAc glycosyltransferase as compared to naïve cells. We identified Notch, the T cell antigen receptor and c-Myc as key controllers of T cell protein O-GlcNAcylation, via regulation of glucose and glutamine transport. Loss of O-GlcNAc transferase blocked T cell progenitor renewal, malignant transformation, and peripheral T cell clonal expansion. Nutrient-dependent signaling pathways regulated by O-GlcNAc glycosyltransferase are thus fundamental for T cell biology

A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid detection of Bacillus spores and identification of Bacillus species

Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA

Peptide Ligands for Pro-survival Protein Bfl-1 from Computationally Guided Library Screening

Pro-survival members of the Bcl-2 protein family inhibit cell death by binding short helical BH3 motifs in pro-apoptotic proteins. Mammalian pro-survival proteins Bcl-x[subscript L], Bcl-2, Bcl-w, Mcl-1, and Bfl-1 bind with varying affinities and specificities to native BH3 motifs, engineered peptides, and small molecules. Biophysical studies have determined interaction patterns for these proteins, particularly for the most-studied family members Bcl-x[subscript L] and Mcl-1. Bfl-1 is a pro-survival protein implicated in preventing apoptosis in leukemia, lymphoma, and melanoma. Although Bfl-1 is a promising therapeutic target, relatively little is known about its binding preferences. We explored the binding of Bfl-1 to BH3-like peptides by screening a peptide library that was designed to sample a high degree of relevant sequence diversity. Screening using yeast-surface display led to several novel high-affinity Bfl-1 binders and to thousands of putative binders identified through deep sequencing. Further screening for specificity led to identification of a peptide that bound to Bfl-1 with K[subscript d] < 1 nM and very slow dissociation from Bfl-1 compared to other pro-survival Bcl-2 family members. A point mutation in this sequence gave a peptide with ~50 nM affinity for Bfl-1 that was selective for Bfl-1 in equilibrium binding assays. Analysis of engineered Bfl-1 binders deepens our understanding of how the binding profiles of pro-survival proteins differ and may guide the development of targeted Bfl-1 inhibitors.National Institute of General Medical Sciences (U.S.) (Award GM084181)National Institute of General Medical Sciences (U.S.) (Award P50-GM68762

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy