The 43-kD polypeptide of heart gap junctions: immunolocalization, topology, and functional domains

Analysis by SDS-PAGE of gap junction fractions isolated from heart suggests that the junctions are comprised of a protein with an Mr 43,000. Antibodies against the electroeluted protein and a peptide representing the 20 amino terminal residues bind specifically on immunoblots to the 43-kD protein and to the major products arising from proteolysis during isolation. By immunocytochemistry, the protein is found in ventricle and atrium in patterns consistent with the known distribution of gap junctions. Both antibodies bind exclusively to gap junctions in fractions from heart examined by EM after gold labeling. Since only domains of the protein exposed at the cytoplasmic surface should be accessible to antibody, we conclude that the 43-kD protein is assembled in gap junctions with the amino terminus of the molecule exposed on the cytoplasmic side of the bilayer, that is, on the same side as the carboxy terminus as determined previously. By combining proteolysis experiments with data from immunoblotting, we can identify a third cytoplasmic region, a loop of some 4 kD between membrane protected domains. This loop carries an antibody binding site. The protein, if transmembrane, is therefore likely to cross the membrane four times. We have used the same antisera to ascertain if the 43-kD protein is involved in cell-cell communication. The antiserum against the amino terminus blocked dye coupling in 90% of cell pairs tested; the antiserum recognizing epitopes in the cytoplasmic loop and cytoplasmic tail blocked coupling in 75% of cell pairs tested. Preimmune serum and control antibodies (one against MIP and another binding to a cardiac G protein) had no or little effect on dye transfer. Our experimental evidence thus indicates that, in spite of the differences in amino acid sequence, the gap junction proteins in heart and liver share a general organizational plan and that there may be several domains (including the amino terminus) of the molecule that are involved in the control of junctional permeability

Emergency Department Physician Attitudes, Practices, and Needs Assessment for the Management of Patients with Chest Pain Secondary to Anxiety and Panic

poster abstractBackground Chest pain is a common medical complaint, accounting for 7 million annual visits to US Emergency Departments (EDs) [1]. Most research and clinical resources are focused on the management of the life-threatening acute coronary syndrome (ACS); however, about 80% of all patients presenting to EDs with chest pain do not have a cardiopulmonary emergency [2-4]. Non-ACS chest pain can be caused by anxiety or a panic disorder, and such etiologies remain undiagnosed in almost 90% of cases, and frequently have worse outcomes [5-9]. Objective and Methods The study objective was to assess ED physician’s attitudes, practices, and needs in managing chest pain related to anxiety and panic. A REDCap survey of 15 Likert-style questions was constructed using expert consensus to ensure content validity then administered to all faculty and resident physicians in the IU Department of Emergency Medicine (113 individuals, 65.5% response-rate). Results ED providers believe a significant proportion (31.5%) of patients with chest pain at low risk for ACS are due to panic/anxiety. Providers give such patients instructions on how to manage their panic/anxiety only 34.8% of the time, while even fewer (19.0%) make a diagnosis of anxiety or panic disorder in their documentation. Most providers (77.0%) would welcome a narrative to aid in discussing anxiety/panic as a cause of chest pain and nearly all (85.1%) would find it helpful to have specific clinic information available to aid in follow-up. Conclusions A significant number of ED patients with chest pain are likely due to anxiety, and a majority of physicians report not having the resources necessary to manage these patients. Further work to develop relevant resources would aim to improve provider confidence in treating these patients, and would hope to improve management of anxiety or panic as a cause of chest pain in the ED

Body Satisfaction and Body Weight: Gender Differences and Sociodemographic Determinants

Background: Given the documented links between body satisfaction, weight-related behaviors, and weight change in adolescents, we sought to examine the prevalence of poor body satisfaction in prepubescent girls and boys and its associations with body weight, socioeconomic factors, and rural residence. Methods: We obtained data from 4254 girls and boys participating in a population-based survey of grade five students in the province of Nova Scotia, Canada. We examined gender specific associations between the prevalence of poor body satisfaction and body mass index (BMI) with generalized additive models and applied multilevel logistic regression methods to estimate associations of body satisfaction with BMI, rural residence, parental education and income, and neighborhood household income. Results: We observed a linear increase in poor body satisfaction with increasing BMI in girls. Among boys, however, we found a U-shape association where boys with low BMI and those with high BMI reported higher levels of poor body satisfaction. We also found that poor body satisfaction was more prevalent among girls whose parents had lower educational attainment and among those who reside in rural areas. Conclusion: Insight into the unique relationships between body satisfaction and BMI experienced by prepubescent children, males, and populations diverse in parental education and geographic location may help to inform public health initiatives designed to improve weight-related behaviors and reduce overweight in children

Integrated Organizational Machine Learning for Aviation Flight Data

Increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations: 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) developing an embedded machine learning framework. Data cleanup and preparation have been a well-known challenge since database systems were first invented. While integration and automation of data collection efforts within many organizations is quite mature, there are special challenges for flight-based organizations (i.e., the automatic and efficient transmission of aircraft flight data to centralized analytical data processing systems). Furthermore, this creates additional constraints for the operationalization of embedded machine learning methods for classical tasks such as classification and prediction; and magnifying design challenges for the more novel ‘prescriptive-based’ architectures. Our research is focused on the application of a design pattern for a) the integration and automation of data collection and b) an organizationally embedded ensemble machine learning method

Integrated Organizational Machine Learning for Aviation Flight Data

An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has been a well known challenge since database systems were first invented. While integration and automation of data collection efforts within many organizations is quite mature, there are special challenges for flight-based organizations (i.e., the automatic and efficient transmission of aircraft flight data to centralized analytical data processing systems). Furthermore, this creates additional constraints for the operationalization of embedded machine learning methods for classical tasks such as classification and prediction; and magnifying design challenges for the more novel ‘prescriptive-based’ architectures. Our research is focused on the application of a design pattern for a) the integration and automation of data collection and b) an organizationally embedded ensemble machine learning method

Analysis of the Two-Level NO PLIF Model for Low-Temperature High-Speed Flow Applications

The current work compares experimentally obtained nitric oxide (NO) laser-induced fluorescence (LIF) spectra with the equivalent spectra obtained analytically. The experimental spectra are computed from captured images of fluorescence in a gas cell and from a laser sheet passing through the fuel-air mixing flowfield produced by a high-speed fuel injector. The fuel injector is a slender strut that is currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. This injector is placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number airflow at the entrance of a scramjet combustor, and injects helium, which is used as an inert substitute for hydrogen fuel. Experimental planar (P) LIF is obtained by using a UV laser to excite fluorescence from the NO molecules that are present in either a gas cell or the facility air used for the EIMP experiments. The experimental data are obtained for several segments of the NO fluorescence spectrum. The selected segments encompass LIF lines with rotational quantum numbers appropriate for low-to-moderate temperature flows similar to those corresponding to the nominal experimental flow conditions. The experimental LIF spectra are then evaluated from the data and compared with those obtained from the theoretical models. The theoretical spectra are obtained from LIFBASE and LINUS software, and from a simplified version of the two-level fluorescence model. The equivalent analytic PLIF images are also obtained by applying only the simplified model to the results of the Reynolds-averaged simulations (RAS) of the mixing flowfield. Good agreement between the experimental and theoretical results provides increased confidence in both the simplified LIF modeling and CFD simulations for further investigations of high-speed injector performance using this approach

Combinations of isoform-targeted histone deacetylase inhibitors and bryostatin analogues display remarkable potency to activate latent HIV without global T-cell activation

AbstractCurrent antiretroviral therapy (ART) for HIV/AIDS slows disease progression by reducing viral loads and increasing CD4 counts. Yet ART is not curative due to the persistence of CD4+ T-cell proviral reservoirs that chronically resupply active virus. Elimination of these reservoirs through the administration of synergistic combinations of latency reversing agents (LRAs), such as histone deacetylase (HDAC) inhibitors and protein kinase C (PKC) modulators, provides a promising strategy to reduce if not eradicate the viral reservoir. Here, we demonstrate that largazole and its analogues are isoform-targeted histone deacetylase inhibitors and potent LRAs. Significantly, these isoform-targeted HDAC inhibitors synergize with PKC modulators, namely bryostatin-1 analogues (bryologs). Implementation of this unprecedented LRA combination induces HIV-1 reactivation to unparalleled levels and avoids global T-cell activation within resting CD4+ T-cells.</jats:p

Dynamical Bonding Driving Mixed Valency in a Metal Boride

Samarium hexaboride is an anomaly, having many exotic and seemingly mutually incompatible properties. It was proposed to be a mixed-valent semiconductor, and later - a topological Kondo insulator, and yet has a Fermi surface despite being an insulator. We propose a new and unified understanding of SmB$_6$ centered on the hitherto unrecognized dynamical bonding effect: the coexistence of two Sm-B bonding modes within SmB$_6$, corresponding to different oxidation states of the Sm. The mixed valency arises in SmB$_6$ from thermal population of these distinct minima enabled by motion of B. Our model simultaneously explains the thermal valence fluctuations, appearance of magnetic Fermi surface, excess entropy at low temperatures, pressure-induced phase transitions, and related features in Raman spectra and their unexpected dependence on temperature and boron isotope

DG-IMEX Method for a Two-Moment Model for Radiation Transport in the O(v/c)\mathcal{O}(v/c) Limit

We consider particle systems described by moments of a phase-space density and propose a realizability-preserving numerical method to evolve a spectral two-moment model for particles interacting with a background fluid moving with nonrelativistic velocities. The system of nonlinear moment equations, with special relativistic corrections to $\mathcal{O}(v/c)$, expresses a balance between phase-space advection and collisions and includes velocity-dependent terms that account for spatial advection, Doppler shift, and angular aberration. This model is closely related to the one promoted by Lowrie et al. (2001; JQSRT, 69, 291-304) and similar to models currently used to study transport phenomena in large-scale simulations of astrophysical environments. The method is designed to preserve moment realizability, which guarantees that the moments correspond to a nonnegative phase-space density. The realizability-preserving scheme consists of the following key components: (i) a strong stability-preserving implicit-explicit (IMEX) time-integration method; (ii) a discontinuous Galerkin (DG) phase-space discretization with carefully constructed numerical fluxes; (iii) a realizability-preserving implicit collision update; and (iv) a realizability-enforcing limiter. In time integration, nonlinearity of the moment model necessitates solution of nonlinear equations, which we formulate as fixed-point problems and solve with tailored iterative solvers that preserve moment realizability with guaranteed convergence. We also analyze the simultaneous Eulerian-frame number and energy conservation properties of the semi-discrete DG scheme and propose an "energy limiter" that promotes Eulerian-frame energy conservation. Through numerical experiments, we demonstrate the accuracy and robustness of this DG-IMEX method and investigate its Eulerian-frame energy conservation properties