The Samurai Project: verifying the consistency of black-hole-binary waveforms for gravitational-wave detection

We quantify the consistency of numerical-relativity black-hole-binary waveforms for use in gravitational-wave (GW) searches with current and planned ground-based detectors. We compare previously published results for the $(\ell=2,| m | =2)$ mode of the gravitational waves from an equal-mass nonspinning binary, calculated by five numerical codes. We focus on the 1000M (about six orbits, or 12 GW cycles) before the peak of the GW amplitude and the subsequent ringdown. We find that the phase and amplitude agree within each code's uncertainty estimates. The mismatch between the $(\ell=2,| m| =2)$ modes is better than $10^{-3}$ for binary masses above $60 M_{\odot}$ with respect to the Enhanced LIGO detector noise curve, and for masses above $180 M_{\odot}$ with respect to Advanced LIGO, Virgo and Advanced Virgo. Between the waveforms with the best agreement, the mismatch is below $2 \times 10^{-4}$. We find that the waveforms would be indistinguishable in all ground-based detectors (and for the masses we consider) if detected with a signal-to-noise ratio of less than $\approx14$, or less than $\approx25$ in the best cases.Comment: 17 pages, 9 figures. Version accepted by PR

Impact of densitized lapse slicings on evolutions of a wobbling black hole

We present long-term stable and second-order convergent evolutions of an excised wobbling black hole. Our results clearly demonstrate that the use of a densitized lapse function extends the lifetime of simulations dramatically. We also show the improvement in the stability of single static black holes when an algebraic densitized lapse condition is applied. In addition, we introduce a computationally inexpensive approach for tracking the location of the singularity suitable for mildly distorted black holes. The method is based on investigating the fall-off behavior and asymmetry of appropriate grid variables. This simple tracking method allows one to adjust the location of the excision region to follow the coordinate motion of the singularity.Comment: 10 pages, 8 figure

Comparative pharmacokinetics of tacrolimus in stable pediatric allograft recipients converted from immediate-release tacrolimus to prolonged-release tacrolimus formulation.

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Evidence synthesis as the basis for decision analysis: a method of selecting the best agricultural practices for multiple ecosystem services

Agricultural management practices have impacts not only on crops and livestock, but also on soil, water, wildlife, and ecosystem services. Agricultural research provides evidence about these impacts, but it is unclear how this evidence should be used to make decisions. Two methods are widely used in decision making: evidence synthesis and decision analysis. However, a system of evidence-based decision making that integrates these two methods has not yet been established. Moreover, the standard methods of evidence synthesis have a narrow focus (e.g., the effects of one management practice), but the standard methods of decision analysis have a wide focus (e.g., the comparative effectiveness of multiple management practices). Thus, there is a mismatch between the outputs from evidence synthesis and the inputs that are needed for decision analysis. We show how evidence for a wide range of agricultural practices can be reviewed and summarized simultaneously (“subject-wide evidence synthesis”), and how this evidence can be assessed by experts and used for decision making (“multiple-criteria decision analysis”). We show how these methods could be used by The Nature Conservancy (TNC) in California to select the best management practices for multiple ecosystem services in Mediterranean-type farmland and rangeland, based on a subject-wide evidence synthesis that was published by Conservation Evidence (www.conservationevidence.com). This method of “evidence-based decision analysis” could be used at different scales, from the local scale (farmers deciding which practices to adopt) to the national or international scale (policy makers deciding which practices to support through agricultural subsidies or other payments for ecosystem services). We discuss the strengths and weaknesses of this method, and we suggest some general principles for improving evidence synthesis as the basis for multi-criteria decision analysis

Involvement in teaching improves learning in medical students: a randomized cross-over study

<p>Abstract</p> <p>Background</p> <p>Peer-assisted learning has many purported benefits including preparing students as educators, improving communication skills and reducing faculty teaching burden. But comparatively little is known about the effects of teaching on learning outcomes of peer educators in medical education.</p> <p>Methods</p> <p>One hundred and thirty-five first year medical students were randomly allocated to 11 small groups for the Gastroenterology/Hematology Course at the University of Calgary. For each of 22 sessions, two students were randomly selected from each group to be peer educators. Students were surveyed to estimate time spent preparing as peer educator versus group member. Students completed an end-of-course 94 question multiple choice exam. A paired t-test was used to compare performance on clinical presentations for which students were peer educators to those for which they were not.</p> <p>Results</p> <p>Preparation time increased from a mean (SD) of 36 (33) minutes baseline to 99 (60) minutes when peer educators (Cohen's <it>d </it>= 1.3; p < 0.001). The mean score (SD) for clinical presentations in which students were peer educators was 80.7% (11.8) compared to77.6% (6.9) for those which they were not (<it>d </it>= 0.33; <it>p </it>< 0.01).</p> <p>Conclusion</p> <p>Our results suggest that involvement in teaching small group sessions improves medical students' knowledge acquisition and retention.</p

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ

Isotropic 3D Nuclear Morphometry of Normal, Fibrocystic and Malignant Breast Epithelial Cells Reveals New Structural Alterations

Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome.METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants.RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving.CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.</p

Prospects for the development of probiotics and prebiotics for oral applications

There has been a paradigm shift towards an ecological and microbial community-based approach to understanding oral diseases. This has significant implications for approaches to therapy and has raised the possibility of developing novel strategies through manipulation of the resident oral microbiota and modulation of host immune responses. The increased popularity of using probiotic bacteria and/or prebiotic supplements to improve gastrointestinal health has prompted interest in the utility of this approach for oral applications. Evidence now suggests that probiotics may function not only by direct inhibition of, or enhanced competition with, pathogenic micro-organisms, but also by more subtle mechanisms including modulation of the mucosal immune system. Similarly, prebiotics could promote the growth of beneficial micro-organisms that comprise part of the resident microbiota. The evidence for the use of pro or prebiotics for the prevention of caries or periodontal diseases is reviewed, and issues that could arise from their use, as well as questions that still need to be answered, are raised. A complete understanding of the broad ecological changes induced in the mouth by probiotics or prebiotics will be essential to assess their long-term consequences for oral health and disease

Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage

Potential carbon mineralization (Cmin) is a commonly used indicator of soil health, with greater Cmin values interpreted as healthier soil. While Cmin values are typically greater in agricultural soils managed with minimal physical disturbance, the mechanisms driving the increases remain poorly understood. This study assessed bacterial and archaeal community structure and potential microbial drivers of Cmin in soils maintained under various degrees of physical disturbance. Potential carbon mineralization, 16S rRNA sequences, and soil characterization data were collected as part of the North American Project to Evaluate Soil Health Measurements (NAPESHM). Results showed that type of cropping system, intensity of physical disturbance, and soil pH influenced microbial sensitivity to physical disturbance. Furthermore, 28% of amplicon sequence variants (ASVs), which were important in modeling Cmin, were enriched under soils managed with minimal physical disturbance. Sequences identified as enriched under minimal disturbance and important for modeling Cmin, were linked to organisms which could produce extracellular polymeric substances and contained metabolic strategies suited for tolerating environmental stressors. Understanding how physical disturbance shapes microbial communities across climates and inherent soil properties and drives changes in Cmin provides the context necessary to evaluate management impacts on standardized measures of soil microbial activity