The Wasteland of Random Supergravities

We show that in a general \cal{N} = 1 supergravity with N \gg 1 scalar fields, an exponentially small fraction of the de Sitter critical points are metastable vacua. Taking the superpotential and Kahler potential to be random functions, we construct a random matrix model for the Hessian matrix, which is well-approximated by the sum of a Wigner matrix and two Wishart matrices. We compute the eigenvalue spectrum analytically from the free convolution of the constituent spectra and find that in typical configurations, a significant fraction of the eigenvalues are negative. Building on the Tracy-Widom law governing fluctuations of extreme eigenvalues, we determine the probability P of a large fluctuation in which all the eigenvalues become positive. Strong eigenvalue repulsion makes this extremely unlikely: we find P \propto exp(-c N^p), with c, p being constants. For generic critical points we find p \approx 1.5, while for approximately-supersymmetric critical points, p \approx 1.3. Our results have significant implications for the counting of de Sitter vacua in string theory, but the number of vacua remains vast.Comment: 39 pages, 9 figures; v2: fixed typos, added refs and clarification

M-GCAT: interactively and efficiently constructing large-scale multiple genome comparison frameworks in closely related species

BACKGROUND: Due to recent advances in whole genome shotgun sequencing and assembly technologies, the financial cost of decoding an organism's DNA has been drastically reduced, resulting in a recent explosion of genomic sequencing projects. This increase in related genomic data will allow for in depth studies of evolution in closely related species through multiple whole genome comparisons. RESULTS: To facilitate such comparisons, we present an interactive multiple genome comparison and alignment tool, M-GCAT, that can efficiently construct multiple genome comparison frameworks in closely related species. M-GCAT is able to compare and identify highly conserved regions in up to 20 closely related bacterial species in minutes on a standard computer, and as many as 90 (containing 75 cloned genomes from a set of 15 published enterobacterial genomes) in an hour. M-GCAT also incorporates a novel comparative genomics data visualization interface allowing the user to globally and locally examine and inspect the conserved regions and gene annotations. CONCLUSION: M-GCAT is an interactive comparative genomics tool well suited for quickly generating multiple genome comparisons frameworks and alignments among closely related species. M-GCAT is freely available for download for academic and non-commercial use at:

Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis.

BACKGROUND: Self-monitoring of blood pressure (BP) appears to reduce BP in hypertension but important questions remain regarding effective implementation and which groups may benefit most. This individual patient data (IPD) meta-analysis was performed to better understand the effectiveness of BP self-monitoring to lower BP and control hypertension. METHODS AND FINDINGS: Medline, Embase, and the Cochrane Library were searched for randomised trials comparing self-monitoring to no self-monitoring in hypertensive patients (June 2016). Two reviewers independently assessed articles for eligibility and the authors of eligible trials were approached requesting IPD. Of 2,846 articles in the initial search, 36 were eligible. IPD were provided from 25 trials, including 1 unpublished study. Data for the primary outcomes-change in mean clinic or ambulatory BP and proportion controlled below target at 12 months-were available from 15/19 possible studies (7,138/8,292 [86%] of randomised participants). Overall, self-monitoring was associated with reduced clinic systolic blood pressure (sBP) compared to usual care at 12 months (-3.2 mmHg, [95% CI -4.9, -1.6 mmHg]). However, this effect was strongly influenced by the intensity of co-intervention ranging from no effect with self-monitoring alone (-1.0 mmHg [-3.3, 1.2]), to a 6.1 mmHg (-9.0, -3.2) reduction when monitoring was combined with intensive support. Self-monitoring was most effective in those with fewer antihypertensive medications and higher baseline sBP up to 170 mmHg. No differences in efficacy were seen by sex or by most comorbidities. Ambulatory BP data at 12 months were available from 4 trials (1,478 patients), which assessed self-monitoring with little or no co-intervention. There was no association between self-monitoring and either lower clinic or ambulatory sBP in this group (clinic -0.2 mmHg [-2.2, 1.8]; ambulatory 1.1 mmHg [-0.3, 2.5]). Results for diastolic blood pressure (dBP) were similar. The main limitation of this work was that significant heterogeneity remained. This was at least in part due to different inclusion criteria, self-monitoring regimes, and target BPs in included studies. CONCLUSIONS: Self-monitoring alone is not associated with lower BP or better control, but in conjunction with co-interventions (including systematic medication titration by doctors, pharmacists, or patients; education; or lifestyle counselling) leads to clinically significant BP reduction which persists for at least 12 months. The implementation of self-monitoring in hypertension should be accompanied by such co-interventions

Unemployment Through Learning from Experience

Digitised version produced by the EUI Library and made available online in 2020

Implementation of the CALM intervention for anxiety disorders: a qualitative study

<p>Abstract</p> <p>Background</p> <p>Investigators recently tested the effectiveness of a collaborative-care intervention for anxiety disorders: Coordinated Anxiety Learning and Management(CALM) []) in 17 primary care clinics around the United States. Investigators also conducted a qualitative process evaluation. Key research questions were as follows: (1) What were the facilitators/barriers to implementing CALM? (2) What were the facilitators/barriers to sustaining CALM after the study was completed?</p> <p>Methods</p> <p>Key informant interviews were conducted with 47 clinic staff members (18 primary care providers, 13 nurses, 8 clinic administrators, and 8 clinic staff) and 14 study-trained anxiety clinical specialists (ACSs) who coordinated the collaborative care and provided cognitive behavioral therapy. The interviews were semistructured and conducted by phone. Data were content analyzed with line-by-line analyses leading to the development and refinement of themes.</p> <p>Results</p> <p>Similar themes emerged across stakeholders. Important facilitators to implementation included the perception of "low burden" to implement, provider satisfaction with the intervention, and frequent provider interaction with ACSs. Barriers to implementation included variable provider interest in mental health, high rates of part-time providers in clinics, and high social stressors of lower socioeconomic-status patients interfering with adherence. Key sustainability facilitators were if a clinic had already incorporated collaborative care for another disorder and presence of onsite mental health staff. The main barrier to sustainability was funding for the ACS.</p> <p>Conclusions</p> <p>The CALM intervention was relatively easy to incorporate during the effectiveness trial, and satisfaction was generally high. Numerous implementation and sustainability barriers could limit the reach and impact of widespread adoption. Findings should be interpreted with the knowledge that the ACSs in this study were provided and trained by the study. Future research should explore uptake of CALM and similar interventions without the aid of an effectiveness trial.</p

Hypertension and type 2 diabetes: What family physicians can do to improve control of blood pressure - an observational study

Background: The prevalence of type 2 diabetes is rising, and most of these patients also have hypertension, substantially increasing the risk of cardiovascular morbidity and mortality. The majority of these patients do not reach target blood pressure levels for a wide variety of reasons. When a literature review provided no clear focus for action when patients are not at target, we initiated a study to identify characteristics of patients and providers associated with achieving target BP levels in community-based practice. Methods: We conducted a practice- based, cross-sectional observational and mailed survey study. The setting was the practices of 27 family physicians and nurse practitioners in 3 eastern provinces in Canada. The participants were all patients with type 2 diabetes who could understand English, were able to give consent, and would be available for follow-up for more than one year. Data were collected from each patient’s medical record and from each patient and physician/nurse practitioner by mailed survey. Our main outcome measures were overall blood pressure at target (< 130/80), systolic blood pressure at target, and diastolic blood pressure at target. Analysis included initial descriptive statistics, logistic regression models, and multivariate regression using hierarchical nonlinear modeling (HNLM). Results: Fifty-four percent were at target for both systolic and diastolic pressures. Sixty-two percent were at systolic target, and 79% were at diastolic target. Patients who reported eating food low in salt had higher odds of reaching target blood pressure. Similarly, patients reporting low adherence to their medication regimen had lower odds of reaching target blood pressure. Conclusions: When primary care health professionals are dealing with blood pressures above target in a patient with type 2 diabetes, they should pay particular attention to two factors. They should inquire about dietary salt intake, strongly emphasize the importance of reduction, and refer for detailed counseling if necessary. Similarly, they should inquire about adherence to the medication regimen, and employ a variety of patient-oriented strategies to improve adherence

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods