Study of the integration of wind tunnel and computational methods for aerodynamic configurations

A study was conducted to determine the effectiveness of using a low-order panel code to estimate wind tunnel wall corrections. The corrections were found by two computations. The first computation included the test model and the surrounding wind tunnel walls, while in the second computation the wind tunnel walls were removed. The difference between the force and moment coefficients obtained by comparing these two cases allowed the determination of the wall corrections. The technique was verified by matching the test-section, wall-pressure signature from a wind tunnel test with the signature predicted by the panel code. To prove the viability of the technique, two cases were considered. The first was a two-dimensional high-lift wing with a flap that was tested in the 7- by 10-foot wind tunnel at NASA Ames Research Center. The second was a 1/32-scale model of the F/A-18 aircraft which was tested in the low-speed wind tunnel at San Diego State University. The panel code used was PMARC (Panel Method Ames Research Center). Results of this study indicate that the proposed wind tunnel wall correction method is comparable to other methods and that it also inherently includes the corrections due to model blockage and wing lift

Severe Facial Herpes Vegetans and Viremia in NFKB2-Deficient Common Variable Immunodeficiency

With the accessibility of next-generation sequencing modalities, an increasing number of primary immunodeficiency disorders (PIDDs) such as common variable immunodeficiency (CVID) have gained improved understanding of molecular pathogenesis and disease phenotype with the identification of a genetic etiology. We report a patient with early-onset CVID due to an autosomal dominant loss-of-function mutation in NFKB2 who developed a severe herpes vegetans cutaneous infection as well as concurrent herpes simplex virus viremia. The case highlights features of CVID, unique aspects of NF-κB2 deficiency including susceptibility to herpesvirus infections, the detection of neutralizing anticytokine antibodies, and the complexity of medical management of patients with a PIDD that can be aided by a known genetic diagnosis

Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide

Signal transducer and activator of transcription 1 (STAT1) gain-of-function mutations and disseminated coccidioidomycosis and histoplasmosis

Background: Impaired signaling in the IFN-g/IL-12 pathway causes susceptibility to severe disseminated infections with mycobacteria and dimorphic yeasts. Dominant gain-of-function mutations in signal transducer and activator of transcription 1 (STAT1) have been associated with chronic mucocutaneous candidiasis. Objective: We sought to identify the molecular defect in patients with disseminated dimorphic yeast infections. Methods: PBMCs, EBV-transformed B cells, and transfected U3A cell lines were studied for IFN-g/IL-12 pathway function. STAT1 was sequenced in probands and available relatives. Interferon-induced STAT1 phosphorylation, transcriptional responses, protein-protein interactions, target gene activation, and function were investigated. Results: We identified 5 patients with disseminated Coccidioides immitis or Histoplasma capsulatum with heterozygous missense mutations in the STAT1 coiled-coil or DNA-binding domains. These are dominant gain-of-function mutations causing enhanced STAT1 phosphorylation, delayed dephosphorylation, enhanced DNA binding and transactivation, and enhanced interaction with protein inhibitor of activated STAT1. The mutations caused enhanced IFN-g–induced gene expression, but we found impaired responses to IFN-g restimulation. Conclusion: Gain-of-function mutations in STAT1 predispose to invasive, severe, disseminated dimorphic yeast infections, likely through aberrant regulation of IFN-g–mediated inflammationFil: Sampaio, Elizabeth P.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Instituto Oswaldo Cruz. Laboratorio de Leprologia; BrasilFil: Hsu, Amy P.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Pechacek, Joseph. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Hannelore I.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Erasmus Medical Center. Department of Medical Microbiology and Infectious Disease; Países BajosFil: Dias, Dalton L.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Paulson, Michelle L.. Clinical Research Directorate/CMRP; Estados UnidosFil: Chandrasekaran, Prabha. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Rosen, Lindsey B.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Carvalho, Daniel S.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unidos. Instituto Oswaldo Cruz, Laboratorio de Leprologia; BrasilFil: Ding, Li. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Vinh, Donald C.. McGill University Health Centre. Division of Infectious Diseases; CanadáFil: Browne, Sarah K.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Datta, Shrimati. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Allergic Diseases. Allergic Inflammation Unit; Estados UnidosFil: Milner, Joshua D.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Allergic Diseases. Allergic Inflammation Unit; Estados UnidosFil: Kuhns, Douglas B.. Clinical Services Program; Estados UnidosFil: Long Priel, Debra A.. Clinical Services Program; Estados UnidosFil: Sadat, Mohammed A.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Host Defenses. Infectious Diseases Susceptibility Unit; Estados UnidosFil: Shiloh, Michael. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: De Marco, Brendan. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Alvares, Michael. University of Texas. Southwestern Medical Center. Division of Allergy and Immunology; Estados UnidosFil: Gillman, Jason W.. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Ramarathnam, Vivek. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: de la Morena, Maite. University of Texas. Southwestern Medical Center. Division of Allergy and Immunology; Estados UnidosFil: Bezrodnik, Liliana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutierrez"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Moreira, Ileana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutierrez"; ArgentinaFil: Uzel, Gulbu. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Johnson, Daniel. University of Chicago. Comer Children; Estados UnidosFil: Spalding, Christine. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Zerbe, Christa S.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados UnidosFil: Wiley, Henry. National Eye Institute. Clinical Trials Branch; Estados UnidosFil: Greenberg, David E.. University of Texas. Southwestern Medical Center. Division of Infectious Diseases; Estados UnidosFil: Hoover, Susan E.. University of Arizona. College of Medicine. Valley Fever Center for Excellence; Estados UnidosFil: Rosenzweig, Sergio D.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Host Defenses Infectious Diseases Susceptibility Unit; Estados Unidos. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Primary Immunodeficiency Clinic; Estados UnidosFil: Galgiani, John N.. University of Arizona. College of Medicine. Valley Fever Center for Excellence; Estados UnidosFil: Holland, Steven M.. National Institutes of Health. National Institute of Allergy and Infectious Diseases. Laboratory of Clinical Infectious Diseases. Immunopathogenesis Section; Estados Unido

Adjunctive rifampicin for Staphylococcus aureus bacteraemia (ARREST): a multicentre, randomised, double-blind, placebo-controlled trial.

BACKGROUND: Staphylococcus aureus bacteraemia is a common cause of severe community-acquired and hospital-acquired infection worldwide. We tested the hypothesis that adjunctive rifampicin would reduce bacteriologically confirmed treatment failure or disease recurrence, or death, by enhancing early S aureus killing, sterilising infected foci and blood faster, and reducing risks of dissemination and metastatic infection. METHODS: In this multicentre, randomised, double-blind, placebo-controlled trial, adults (≥18 years) with S aureus bacteraemia who had received ≤96 h of active antibiotic therapy were recruited from 29 UK hospitals. Patients were randomly assigned (1:1) via a computer-generated sequential randomisation list to receive 2 weeks of adjunctive rifampicin (600 mg or 900 mg per day according to weight, oral or intravenous) versus identical placebo, together with standard antibiotic therapy. Randomisation was stratified by centre. Patients, investigators, and those caring for the patients were masked to group allocation. The primary outcome was time to bacteriologically confirmed treatment failure or disease recurrence, or death (all-cause), from randomisation to 12 weeks, adjudicated by an independent review committee masked to the treatment. Analysis was intention to treat. This trial was registered, number ISRCTN37666216, and is closed to new participants. FINDINGS: Between Dec 10, 2012, and Oct 25, 2016, 758 eligible participants were randomly assigned: 370 to rifampicin and 388 to placebo. 485 (64%) participants had community-acquired S aureus infections, and 132 (17%) had nosocomial S aureus infections. 47 (6%) had meticillin-resistant infections. 301 (40%) participants had an initial deep infection focus. Standard antibiotics were given for 29 (IQR 18-45) days; 619 (82%) participants received flucloxacillin. By week 12, 62 (17%) of participants who received rifampicin versus 71 (18%) who received placebo experienced treatment failure or disease recurrence, or died (absolute risk difference -1·4%, 95% CI -7·0 to 4·3; hazard ratio 0·96, 0·68-1·35, p=0·81). From randomisation to 12 weeks, no evidence of differences in serious (p=0·17) or grade 3-4 (p=0·36) adverse events were observed; however, 63 (17%) participants in the rifampicin group versus 39 (10%) in the placebo group had antibiotic or trial drug-modifying adverse events (p=0·004), and 24 (6%) versus six (2%) had drug interactions (p=0·0005). INTERPRETATION: Adjunctive rifampicin provided no overall benefit over standard antibiotic therapy in adults with S aureus bacteraemia. FUNDING: UK National Institute for Health Research Health Technology Assessment

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Accommodating Measurements Below a Limit of Detection: A Novel Application of Cox Regression

In environmental epidemiology, measurements of exposure biomarkers often fall below the assay's limit of detection. Existing methods for handling this problem, including deletion, substitution, parametric regression, and multiple imputation, can perform poorly if the proportion of “nondetects” is high or parametric models are misspecified. We propose an approach that treats the measured analyte as the modeled outcome, implying a role reversal when the analyte is a putative cause of a health outcome. Following a scale reversal as well, our approach uses Cox regression to model the analyte, with confounder adjustment. The method makes full use of quantifiable analyte measures, while appropriately treating nondetects as censored. Under the proportional hazards assumption, the hazard ratio for a binary health outcome is interpretable as an adjusted odds ratio: the odds for the outcome at any particular analyte concentration divided by the odds given a lower concentration. Our approach is broadly applicable to cohort studies, case-control studies (frequency matched or not), and cross-sectional studies conducted to identify determinants of exposure. We illustrate the method with cross-sectional survey data to assess sex as a determinant of 2,3,7,8-tetrachlorodibenzo-p-dioxin concentration and with prospective cohort data to assess the association between 2,4,4′-trichlorobiphenyl exposure and psychomotor development