What you wear does not affect the credibility of your treatment: A blinded randomized controlled study

© 2016 Elsevier Ireland Ltd Objective Professional appearance is easily modifiable, and might alter the effects of a clinical encounter. We aimed to determine whether professional attire influences a patient's perception of treatment credibility. Methods We performed a single-blind randomized controlled study on 128 patients with acute non-specific low back pain who were about to receive treatment in primary care. The treating clinician was randomly allocated to wear formal attire (experimental condition) or casual attire (control condition) to the consultation. Clinicians provided a standardized briefing on the rationale behind the patient's forthcoming treatment. Treatment credibility (Credibility and Expectancy Questionnaire) was assessed immediately after this briefing. Results All patients received the experimental or control condition as allocated and provided complete primary outcome data. Formal attire had no effect on perceived treatment credibility (Mean difference between groups 1.2 [95%CI-1.1 to 3.5]). Age was the only significant predictor of treatment credibility; older patients rated treatment credibility higher (Beta = 0.16 [95%CI 0.08 to 0.24]). Conclusion In a trial setting, whether or not a clinician is formally dressed has no effect on perceptions of treatment credibility in patients with acute low back pain. Practice implication Clinicians should dress comfortably without fear of losing credibility

The discovery, monitoring and environment of SGR J1935+2154

We report on the discovery of a new member of the magnetar class, SGR J1935+2154, and on its timing and spectral properties measured by an extensive observational campaign carried out between July 2014 and March 2015 with Chandra and XMM-Newton (11 pointings). We discovered the spin period of SGR J1935+2154 through the detection of coherent pulsations at a period of about 3.24s. The magnetar is slowing-down at a rate of 1.43(1)x10^{-11} s/s and with a decreasing trend due to a negative second period derivative of -3.5(7)x10^{-19} s/s^2. This implies a surface dipolar magnetic field strength of about 2.2x10^{14} G, a characteristic age of about 3.6kyr and, a spin-down luminosity L_{sd} of about 1.7x10^{34} erg/s. The source spectrum is well modelled by a blackbody with temperature of about 500eV plus a power-law component with photon index of about 2. The source showed a moderate long-term variability, with a flux decay of about 25\% during the first four months since its discovery, and a re-brightening of the same amount during the second four months. The X-ray data were also used to study the source environment. In particular, we discovered a diffuse emission extending on spatial scales from about 1" up to at least 1' around SGR J1935+2154 both in Chandra and XMM-Newton data. This component is constant in flux (at least within uncertainties) and its spectrum is well modelled by a power-law spectrum steeper than that of the pulsar. Though a scattering halo origin seems to be more probable we cannot exclude that part, or all, of the diffuse emission is due to a pulsar wind nebula.Comment: To appear in MNRAS; 10 pages, 3 color figures, 4 table

An epidemiologic study of early biologic effects of benzene in Chinese workers.

Benzene is a recognized hematotoxin and leukemogen, but its mechanisms of action in humans are still uncertain. To provide insight into these processes, we carried out a cross-sectional study of 44 healthy workers currently exposed to benzene (median 8-hr time-weighted average; 31 ppm), and unexposed controls in Shanghai, China. Here we provide an overview of the study results on peripheral blood cells levels and somatic cell mutation frequency measured by the glycophorin A (GPA) gene loss assay and report on peripheral cytokine levels. All peripheral blood cells levels (i.e., total white blood cells, absolute lymphocyte count, platelets, red blood cells, and hemoglobin) were decreased among exposed workers compared to controls, with the exception of the red blood cell mean corpuscular volume, which was higher among exposed subjects. In contrast, peripheral cytokine levels (interleukin-3, interleukin-6, erythropoietin, granulocyte colony-stimulating factor, tissue necrosis factor-alpha) in a subset of the most highly exposed workers (n = 11) were similar to values in controls (n = 11), suggesting that benzene does not affect these growth factor levels in peripheral blood. The GPA assay measures stem cell or precursor erythroid cell mutations expressed in peripheral red blood cells of MN heterozygous subjects, identifying NN variants, which result from loss of the GPA M allele and duplication of the N allele, and N phi variants, which arise from gene inactivation. The NN (but not N phi) GPA variant cell frequency was elevated in the exposed workers compared with controls (mean +/- SD, 13.9 +/- 8.4 mutants per million cells versus 7.4 +/- 5.2 per million cells, (respectively; p = 0.0002), suggesting that benzene produces gene-duplicating but not gene-inactivating mutations at the GPA locus in bone marrow cells of exposed humans. These findings, combined with ongoing analyses of benzene macromolecular adducts and chromosomal aberrations, will provide an opportunity to comprehensively evaluate a wide range of early biologic effects associated with benzene exposure in humans

Streaming Algorithms for Submodular Function Maximization

We consider the problem of maximizing a nonnegative submodular set function $f:2^{\mathcal{N}} \rightarrow \mathbb{R}^+$ subject to a $p$-matchoid constraint in the single-pass streaming setting. Previous work in this context has considered streaming algorithms for modular functions and monotone submodular functions. The main result is for submodular functions that are {\em non-monotone}. We describe deterministic and randomized algorithms that obtain a $\Omega(\frac{1}{p})$-approximation using $O(k \log k)$-space, where $k$ is an upper bound on the cardinality of the desired set. The model assumes value oracle access to $f$ and membership oracles for the matroids defining the $p$-matchoid constraint.Comment: 29 pages, 7 figures, extended abstract to appear in ICALP 201

Testing matter effects in propagation of atmospheric and long-baseline neutrinos

We quantify our current knowledge of the size and flavor structure of the matter effects in the evolution of atmospheric and long-baseline neutrinos based solely on the analysis of the corresponding neutrino data. To this aim we generalize the matter potential of the Standard Model by rescaling its strength, rotating it away from the e-e sector, and rephasing it with respect to the vacuum term. This phenomenological parametrization can be easily translated in terms of non-standard neutrino interactions in matter. We show that in the most general case, the strength of the potential cannot be determined solely by atmospheric and long-baseline data. However its flavor composition is very much constrained and the present determination of the neutrino masses and mixing is robust under its presence. We also present an update of the constraints arising from this analysis in the particular case in which no potential is present in the e-mu and e-tau sectors. Finally we quantify to what degree in this scenario it is possible to alleviate the tension between the oscillation results for neutrinos and antineutrinos in the MINOS experiment and show the relevance of the high energy part of the spectrum measured at MINOS.Comment: PDFLaTeX file using JHEP3 class, 25 pages, 7 figures included. Accepted for publication in JHE

Is EC class predictable from reaction mechanism?

We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Publisher PDFPeer reviewe

Interaction between Streptococcus pneumoniae and Staphylococcus aureus in paediatric patients suffering from an underlying chronic disease

Little is known about the interaction between Streptococcus pneumoniae and Staphylococcus aureus in school-age children and adolescents suffering from an underlying chronic disease. To increase our knowledge in this regard, an oropharyngeal swab was obtained from school-age children and adolescents suffering from asthma (n = 423), cystic fibrosis (CF) (n = 212) and type 1 diabetes mellitus (DM1) (n = 296). S. pneumoniae detection and serotyping were performed using a real-time polymerase chain reaction, and S. aureus detection was performed using the RIDAGENE MRSA system. Among asthmatic, CF and DM1 patients, both pathogens were identified in 65/423 (15.4%), 21/212 (9.9%) and 62/296 (20.9%) children, respectively; S. pneumoniae alone was identified in 127/434 (30.0%), 21/212 (9.9%) and 86/296 (29.1%), respectively; S. aureus alone was identified in 58/434 (13.7%), 78/212 (36.8%) and 49/296 (16.6%), respectively. S. pneumoniae colonisation rates were higher in younger children and declined with age, whereas the frequency of S. aureus colonisation was quite similar in the different age groups. Among asthmatic and CF patients aged 6-9 years, S. aureus carriage was significantly higher in children who were positive for S. pneumoniae (P <0.05). No significant association emerged between S. aureus carriage and carriage of S. pneumoniae serotypes included in the pneumococcal conjugate vaccines (PCVs). This study shows for the first time that school-age children and adolescents with asthma, CF and DM1 are frequently colonised by S. pneumoniae and S. aureus and that no negative relationship seems to exist between these pathogens. Moreover, the supposed protection offered by PCV administration against S. aureus colonisation was not demonstrated

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks