Stumbling blocks to entrepreneurship in low-and-moderate income communities

Community development

Analysis of protein acylation.

Proteins can be acylated with a variety of fatty acids attached by different covalent bonds, influencing, among other things, their function and intracellular localization. This unit describes methods to analyze protein acylation, both levels of acylation and also the identification of the fatty acid and the type of bond present in the protein of interest. Protocols are provided for metabolic labeling of proteins with tritiated fatty acids, for exploitation of the differential sensitivity to cleavage of different types of bonds, in order to distinguish between them, and for thin-layer chromatography to separate and identify the fatty acids associated with proteins.Accepted versio

Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling

INTRODUCTION: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. METHODS: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. RESULTS: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. CONCLUSION: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment

Dynamic causal modelling of immune heterogeneity

An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection-even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay-based on sequential serology-that provides a (1) quantitative measure of latent immunological responses and (2) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines

A protein kinase Cβ inhibitor attenuates multidrug resistance of neuroblastoma cells

BACKGROUND: The acquisition of drug resistance is a major reason for poor outcome of neuroblastoma. Protein kinase C (PKC) has been suggested to influence drug resistance in cancer cells. The aim of this study was to elucidate whether inhibition of PKCβ isoforms influences drug-resistance of neuroblastoma cells. METHODS: The effect of the PKCβ inhibitor LY379196 on the growth-suppressing effects of different chemotherapeutics on neuroblastoma cells was analyzed with MTT assays. The effect of LY379196 on the accumulation of [(3)H]vincristine was also investigated RESULTS: The PKCβ inhibitor LY379196 suppressed the growth of three neuroblastoma cell lines. LY379196 also augmented the growth-suppressive effect of doxorubicin, etoposide, paclitaxel, and vincristine, but not of carboplatin. The effect was most marked for vincristine and for the cell-line (SK-N-BE(2)) that was least sensitive to vincristine. No effect was observed on the non-resistant IMR-32 cells. Two other PKC inhibitors, Gö6976 and GF109203X, also enhanced the vincristine effect. The PKC inhibitors caused an increased accumulation of [(3)H]vincristine in SK-N-BE(2) cells. CONCLUSIONS: This indicates that inhibition of PKCβ could attenuate multidrug resistance in neuroblastoma cells by augmenting the levels of natural product anticancer drugs in resistant cells

Protein kinase Cepsilon is important for migration of neuroblastoma cells

<p>Abstract</p> <p>Background</p> <p>Migration is important for the metastatic capacity and thus for the malignancy of cancer cells. There is limited knowledge on regulatory factors that promote the migration of neuroblastoma cells. This study investigates the hypothesis that protein kinase C (PKC) isoforms regulate neuroblastoma cell motility.</p> <p>Methods</p> <p>PKC isoforms were downregulated with siRNA or modulated with activators and inhibitors. Migration was analyzed with scratch and transwell assays. Protein phosphorylation and expression levels were measured with Western blot.</p> <p>Results</p> <p>Stimulation with 12-<it>O</it>-tetradecanoylphorbol-13-acetate (TPA) induced migration of SK-N-BE(2)C neuroblastoma cells. Treatment with the general protein kinase C (PKC) inhibitor GF109203X and the inhibitor of classical isoforms Gö6976 inhibited migration while an inhibitor of PKCβ isoforms did not have an effect. Downregulation of PKCε, but not of PKCα or PKCδ, with siRNA led to a suppression of both basal and TPA-stimulated migration. Experiments using PD98059 and LY294002, inhibitors of the Erk and phosphatidylinositol 3-kinase (PI3K) pathways, respectively, showed that PI3K is not necessary for TPA-induced migration. The Erk pathway might be involved in TPA-induced migration but not in migration driven by PKCε. TPA induced phosphorylation of the PKC substrate myristoylated alanine-rich C kinase substrate (MARCKS) which was suppressed by the PKC inhibitors. Treatment with siRNA oligonucleotides against different PKC isoforms before stimulation with TPA did not influence the phosphorylation of MARCKS.</p> <p>Conclusion</p> <p>PKCε is important for migration of SK-N-BE(2)C neuroblastoma cells. Neither the Erk pathway nor MARCKS are critical downstream targets of PKCε but they may be involved in TPA-mediated migration.</p

Inclusive Electron Scattering from Nuclei at x≃1x \simeq 1

The inclusive A(e,e') cross section for $x \simeq 1$ was measured on $^2$H, C, Fe, and Au for momentum transfers $Q^2$ from 1-7 (GeV/c)$^2$. The scaling behavior of the data was examined in the region of transition from y-scaling to x-scaling. Throughout this transitional region, the data exhibit $\xi$-scaling, reminiscent of the Bloom-Gilman duality seen in free nucleon scattering.Comment: 4 pages, RevTeX; 4 figures (postscript in .tar.Z file