Chandra HETGS Multi-Phase Spectroscopy of the Young Magnetic O Star theta^1 Orionis C

We report on four Chandra grating observations of the oblique magnetic rotator theta^1 Ori C (O5.5 V) covering a wide range of viewing angles with respect to the star's 1060 G dipole magnetic field. We employ line-width and centroid analyses to study the dynamics of the X-ray emitting plasma in the circumstellar environment, as well as line-ratio diagnostics to constrain the spatial location, and global spectral modeling to constrain the temperature distribution and abundances of the very hot plasma. We investigate these diagnostics as a function of viewing angle and analyze them in conjunction with new MHD simulations of the magnetically channeled wind shock mechanism on theta^1 Ori C. This model fits all the data surprisingly well, predicting the temperature, luminosity, and occultation of the X-ray emitting plasma with rotation phase.Comment: 52 pages, 14 figures (1 color), 6 tables. To appear in the Astrophysical Journal, 1 August 2005, v628, issue 2. New version corrects e-mail address, figure and table formatting problem

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies

Background: Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. Methods: We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. Results: For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study

An electrostatic switch displaces phosphatidylinositol phosphate kinases from the membrane during phagocytosis

PIP5K is held at the membrane of forming phagosomes by a conserved, positively charged patch. During particle engulfment, the surface charge of the phagosome decreases, releasing PIP5K and enabling phagocytosis to proceed

Data and safety monitoring in social behavioral intervention trials: the REACH II experience

Background Psychosocial and behavioral interventions trials targeting a broad range of complex social and behavioral problems such as smoking, obesity and family caregiving have proliferated in the past 30 years. At the same time the use of Data and Safety Monitoring Boards (DSMBs) to monitor the progress and quality of intervention trials and the safety of study participants has increased substantially. Most of the existing literature and guidelines for safety monitoring and reporting of adverse events focuses on medical interventions. Consequently, there is little guidance for investigators conducting social and behavior trials. Purpose This paper summarizes how issues associated with safety monitoring and adverse event reporting were handled in the Resources for Enhancing Alzheimer\u27s Caregiver Health (REACH II) program, a multi-site randomized clinical trial, funded by the National Institutes on Aging (NIA) and the National Institutes of Nursing Research (NINR), that tested the efficacy of a multicomponent social/behavioral intervention for caregivers of persons with Alzheimer\u27s disease. Methods A task force was formed to define adverse events for the trial and protocols for reporting and resolving events that occurred. The task force conducted a review of existing polices and protocols for data and safety monitoring and adverse event reporting and identified potential risks particular to the study population. An informal survey regarding data and safety monitoring procedures with investigators on psychosocial intervention trials was also conducted. Results Two categories of events were defined for both caregivers and patients; adverse events and safety alerts. A distinction was also made between events detected at baseline assessment and those detected post-randomization. Standardized protocols were also developed for the reporting and resolution of events that occurred and training of study personnel. Results from the informal survey indicated wide variability in practices for data safety and monitoring across psychosocial intervention trials. Conclusions Overall, the REACH II experience demonstrates that existing guidelines regarding safety monitoring and adverse event reporting pose unique challenges for social/behavioral intervention trials. Challenges encountered in the REACH II program included defining and classifying adverse events, defining resolution of adverse events and attributing causes for events that occurred. These challenges are highlighted and recommendations for addressing them in future studies are discussed

Does self-monitoring reduce blood pressure? Meta-analysis with meta-regression of randomized controlled trials

Introduction. Self-monitoring of blood pressure (BP) is an increasingly common part of hypertension management. The objectives of this systematic review were to evaluate the systolic and diastolic BP reduction, and achievement of target BP, associated with self-monitoring. Methods. MEDLINE, Embase, Cochrane database of systematic reviews, database of abstracts of clinical effectiveness, the health technology assessment database, the NHS economic evaluation database, and the TRIP database were searched for studies where the intervention included self-monitoring of BP and the outcome was change in office/ambulatory BP or proportion with controlled BP. Two reviewers independently extracted data. Meta-analysis using a random effects model was combined with meta-regression to investigate heterogeneity in effect sizes. Results. A total of 25 eligible randomized controlled trials (RCTs) (27 comparisons) were identified. Office systolic BP (20 RCTs, 21 comparisons, 5,898 patients) and diastolic BP (23 RCTs, 25 comparisons, 6,038 patients) were significantly reduced in those who self-monitored compared to usual care (weighted mean difference (WMD) systolic −3.82 mmHg (95% confidence interval −5.61 to −2.03), diastolic −1.45 mmHg (−1.95 to −0.94)). Self-monitoring increased the chance of meeting office BP targets (12 RCTs, 13 comparisons, 2,260 patients, relative risk = 1.09 (1.02 to 1.16)). There was significant heterogeneity between studies for all three comparisons, which could be partially accounted for by the use of additional co-interventions. Conclusion. Self-monitoring reduces blood pressure by a small but significant amount. Meta-regression could only account for part of the observed heterogeneity

Bostonia: The Boston University Alumni Magazine. Volume 9

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Genome-Wide Association Study and Gene Expression Analysis Identifies CD84 as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10-8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10-11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry. © 2013 Cui et al

Rationalising "for" and "against" a policy of school-led careers guidance in STEM in the U.K. : a teacher perspective

This paper reports on teacher attitudes to changes in the provision of careers guidance in the U.K., particularly as it relates to Science, Technology, Engineering and Mathematics (STEM). It draws on survey data of n = 94 secondary-school teachers operating in STEM domains and their attitudes towards a U.K. and devolved policy of internalising careers guidance within schools. The survey presents a mixed message of teachers recognising the significance of their unique position in providing learners with careers guidance yet concern that their ‘relational proximity’ to students and ‘informational distance’ from higher education and STEM industry may produce bias and misinformation that is harmful to their educational and occupational futures