A very brief description of LOFAR - the Low Frequency Array

LOFAR (Low Frequency Array) is an innovative radio telescope optimized for the frequency range 30-240 MHz. The telescope is realized as a phased aperture array without any moving parts. Digital beam forming allows the telescope to point to any part of the sky within a second. Transient buffering makes retrospective imaging of explosive short-term events possible. The scientific focus of LOFAR will initially be on four key science projects (KSPs): 1) detection of the formation of the very first stars and galaxies in the universe during the so-called epoch of reionization by measuring the power spectrum of the neutral hydrogen 21-cm line (Shaver et al. 1999) on the ~5' scale; 2) low-frequency surveys of the sky with of order $10^8$ expected new sources; 3) all-sky monitoring and detection of transient radio sources such as gamma-ray bursts, x-ray binaries, and exo-planets (Farrell et al. 2004); and 4) radio detection of ultra-high energy cosmic rays and neutrinos (Falcke & Gorham 2003) allowing for the first time access to particles beyond 10^21 eV (Scholten et al. 2006). Apart from the KSPs open access for smaller projects is also planned. Here we give a brief description of the telescope.Comment: 2 pages, IAU GA 2006, Highlights of Astronomy, Volume 14, K.A. van der Hucht, e

Toepassing van anonieme onderzoeksgegevens over de SDQ binnen de JGZ: Bruikbaarheid en valkuilen

Application of anonymous research data of the sdq within the preventive child healthcare: usefulness and pitfalls. For the early detection of psychosocial problems within the preventive child healthcare (pch), for children in primary and secondary school, the strengths and difficulties questionnaire (sdq) is used during assessments. The early detection with the sdq is facilitated by the use of cut-off points. These cut-off points are in general extracted from validation studies within an anonymous setting. This setting is different from the confidential, non-anonymous, setting of the pch in which the sdq questionnaire is reviewed by the health professional. There is, therefore, a discrepancy between the settings in which the sdq is validated and in which it is implemented. In the present study it is investigated whether this discrepancy creates a difference in the sdq regarding reliability, mean scores, and cutoff points. For this study questionnaires were used from confidential pch assessments (n = 6.594) and the anonymous youthmonitor (n = 4.613), both administered in the second year of secondary school. The reliability of the sdq was equal in both settings; the mean scores and cut-off points differed however significantly. It is, therefore, not valid to use the cut-off values for the sdq derived from anonymous settings within confidential settings (e.g. Pch) nor to directly compare the mean scores of these different settings. Keywords: preventive child healthcare, sdq, early detection, cut-off points, psychosocial behavior

Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples (N≥ 1,000), using cutoff values for the lower

Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples (N≥ 1,000), using cutoff values for the lower

Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples (N≥ 1,000), using cutoff values for the lower

Identification of cetrimonium bromide and irinotecan as compounds with synthetic lethality against NDRG1 deficient prostate cancer cells

Experimentele farmacotherapi

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains

Background: Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains. Methods: In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: Global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping. Results: Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83-0.93). Conclusions: The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice