An international cross-disciplinary student collaboration: a retrospective eight years

A successful construction endeavour invariably obliges a successful collaborative effort among its many multi-disciplinary stakeholders. Teachers of construction education today are increasingly aware of the need to teach their students skills to enable them to work collaboratively with their peers from other related disciplines. In the present day context of an increasingly globalized construction industry amidst a current rapid advancement in communication technology, an ability to work collaboratively with peers across a geographical divide within an online environment is a valuable skill to have. This paper presents the collective experiences of two distant universities where students from two related disciplines – architectural science (with a construction project management major) and civil engineering - collaborate on a joint student assignment across a time and geographical divide. It presents a description of the project and its intent, teaching pedagogy, students’ feedback and the challenges of establishing the framework

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

<p>Abstract</p> <p>Background</p> <p>Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.</p> <p>Methods</p> <p>Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.</p> <p>Results</p> <p>Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.</p> <p>Conclusions</p> <p>The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.</p

Expression of testicular genes in haematological malignancies

The gene expression of a new group of tumour antigens known as cancer/testis (CT) antigens is now well-recognized in some solid tumours. However, their expression in haematological malignancies remained unclear. In this study, we have used reverse transcription polymerase chain reaction and Southern blot analysis to examine the presence of transcripts for the three CT antigens, NY-ESO-1, SSX2 and SCP1 in haematological malignant cells. We found that transcripts for SCP1 could be detected in 10% of myeloma, 5.7% of acute myeloid leukaemia and 23% of chronic myeloid leukaemia. In contrast, NY-ESO-1 and SSX2 were not detected in any of the 107 tumour samples. © 1999 Cancer Research Campaig

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p

Quantitative High-Throughput Screen Identifies Inhibitors of the Schistosoma mansoni Redox Cascade

Schistosomiasis is a tropical disease associated with high morbidity and mortality, currently affecting over 200 million people worldwide. Praziquantel is the only drug used to treat the disease, and with its increased use the probability of developing drug resistance has grown significantly. The Schistosoma parasites can survive for up to decades in the human host due in part to a unique set of antioxidant enzymes that continuously degrade the reactive oxygen species produced by the host's innate immune response. Two principal components of this defense system have been recently identified in S. mansoni as thioredoxin/glutathione reductase (TGR) and peroxiredoxin (Prx) and as such these enzymes present attractive new targets for anti-schistosomiasis drug development. Inhibition of TGR/Prx activity was screened in a dual-enzyme format with reducing equivalents being transferred from NADPH to glutathione via a TGR-catalyzed reaction and then to hydrogen peroxide via a Prx-catalyzed step. A fully automated quantitative high-throughput (qHTS) experiment was performed against a collection of 71,028 compounds tested as 7- to 15-point concentration series at 5 µL reaction volume in 1536-well plate format. In order to generate a robust data set and to minimize the effect of compound autofluorescence, apparent reaction rates derived from a kinetic read were utilized instead of end-point measurements. Actives identified from the screen, along with previously untested analogues, were subjected to confirmatory experiments using the screening assay and subsequently against the individual targets in secondary assays. Several novel active series were identified which inhibited TGR at a range of potencies, with IC50s ranging from micromolar to the assay response limit (∼25 nM). This is, to our knowledge, the first report of a large-scale HTS to identify lead compounds for a helminthic disease, and provides a paradigm that can be used to jump-start development of novel therapeutics for other neglected tropical diseases

Zoonotic disease research in East Africa

Abstract Background The East African region is endemic with multiple zoonotic diseases and is one of the hotspots for emerging infectious zoonotic diseases with reported multiple outbreaks of epidemic diseases such as Ebola, Marburg and Rift Valley Fever. Here we present a systematic assessment of published research on zoonotic diseases in the region and thesis research in Kenya to understand the regional research focus and trends in publications, and estimate proportion of theses research transitioning to peer-reviewed journal publications. Methods We searched PubMed, Google Scholar and African Journals Online databases for publications on 36 zoonotic diseases identified to have occurred in the East Africa countries of Burundi, Ethiopia, Kenya, Tanzania, Rwanda and Uganda, for the period between 1920 and 2017. We searched libraries and queried online repositories for masters and PhD theses on these diseases produced between 1970 and 2016 in five universities and two research institutions in Kenya. Results We identified 771 journal articles on 22, and 168 theses on 21 of the 36 zoonotic diseases investigated. Research on zoonotic diseases increased exponentially with the last 10 years of our study period contributing more than half of all publications 460 (60%) and theses 102 (61%) retrieved. Endemic diseases were the most studied accounting for 656 (85%) and 150 (89%) of the publication and theses studies respectively, with publications on epidemic diseases associated with outbreaks reported in the region or elsewhere. Epidemiological studies were the most common study types but limited to cross-sectional studies while socio-economics were the least studied. Only 11% of the theses research transitioned to peer-review publications, taking an average of 2.5 years from theses production to manuscript publication. Conclusion Our findings demonstrate increased attention to zoonotic diseases in East Africa but reveal the need to expand the scope, focus and quality of studies to adequately address the public health, social and economic threats posed by zoonoses

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Frequency and predictors of miliary tuberculosis in patients with miliary pulmonary nodules in South Korea: A retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Miliary pulmonary nodules are commonly caused by various infections and cancers. We sought to identify the relative frequencies of various aetiologies and the clinical and radiographic predictors of miliary tuberculosis (TB) in patients with miliary pulmonary nodules.</p> <p>Methods</p> <p>We performed a retrospective cohort study of patients who presented with micronodules occupying more than two-thirds of the lung volume, based on computed tomography (CT) of the chest, between November 2001 and April 2007, in a tertiary referral hospital in South Korea.</p> <p>Results</p> <p>We analyzed 76 patients with miliary pulmonary nodules. Their median age was 52 years and 38 (50%) were males; 18 patients (24%) had a previous or current malignancy and five (7%) had a history of TB. The most common diagnoses of miliary nodules were miliary TB (41 patients, 54%) and miliary metastasis of malignancies (20 patients, 26%). Multivariate analysis revealed that age ≤30 years, HIV infection, corticosteroid use, bronchogenic spread of lesions, and ground-glass opacities occupying >25% of total lung volume increased the probability of miliary TB. However, a history of malignancy decreased the probability of miliary TB.</p> <p>Conclusion</p> <p>Miliary TB accounted for approximately half of all causes of miliary pulmonary nodules. Young age, an immune-compromised state, and several clinical and radiographic characteristics increased the probability of miliary TB.</p

Hospital-level associations with 30-day patient mortality after cardiac surgery: a tutorial on the application and interpretation of marginal and multilevel logistic regression

Background: Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery. However, it is not widely understood how the interpretation of hospital-level effects differs between these methods. Methods. The Australasian Society of Cardiac and Thoracic Surgeons (ASCTS) registry provided data on 32,354 patients undergoing cardiac surgery in 18 hospitals from 2001 to 2009. The logistic regression methods related 30-day mortality after surgery to hospital characteristics with concurrent adjustment for patient characteristics. Results: Hospital-level mortality rates varied from 1.0% to 4.1% of patients. Ordinary, marginal and multilevel regression methods differed with regard to point estimates and conclusions on statistical significance for hospital-level risk factors; ordinary logistic regression giving inappropriately narrow confidence intervals. The median odds ratio, MOR, from the multilevel model was 1.2 whereas ORs for most patient-level characteristics were of greater magnitude suggesting that unexplained between-hospital variation was not as relevant as patient-level characteristics for understanding mortality rates. For hospital-level characteristics in the multilevel model, 80% interval ORs, IOR-80%, supplemented the usual ORs from the logistic regression. The IOR-80% was (0.8 to 1.8) for academic affiliation and (0.6 to 1.3) for the median annual number of cardiac surgery procedures. The width of these intervals reflected the unexplained variation between hospitals in mortality rates; the inclusion of one in each interval suggested an inability to add meaningfully to explaining variation in mortality rates. Conclusions: Marginal and multilevel models take different approaches to account for correlation between patients within hospitals and they lead to different interpretations for hospital-level odds ratios. © 2012 Sanagou et al; licensee BioMed Central Ltd