1,233 research outputs found
A comparison of robust Mendelian randomization methods using summary data.
The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses
Cardiac magnetic resonance findings predict increased resource utilization in elective coronary artery bypass grafting
Morbidity following CABG (coronary artery bypass grafting) is difficult to predict and leads to increased healthcare costs. We hypothesized that pre-operative CMR (cardiac magnetic resonance) findings would predict resource utilization in elective CABG. Over a 12-month period, patients requiring elective CABG were invited to undergo CMR 1 day prior to CABG. Gadolinium-enhanced CMR was performed using a trueFISP inversion recovery sequence on a 1.5 tesla scanner (Sonata; Siemens). Clinical data were collected prospectively. Admission costs were quantified based on standardized actual cost/day. Admission cost greater than the median was defined as 'increased'. Of 458 elective CABG cases, 45 (10%) underwent pre-operative CMR. Pre-operative characteristics [mean (S.D.) age, 64 (9) years, mortality (1%) and median (interquartile range) admission duration, 7 (6â8) days] were similar in patients who did or did not undergo CMR. In the patients undergoing CMR, eight (18%) and 11 (24%) patients had reduced LV (left ventricular) systolic function by CMR [LVEF (LV ejection fraction) <55%] and echocardiography respectively. LE (late enhancement) with gadolinium was detected in 17 (38%) patients. The average cost/day was 19059 ($10891â157917). CMR LVEF {OR (odds ratio), 0.93 [95% CI (confidence interval), 0.87â0.99]; P=0.03} and SV (stroke volume) index [OR 1.07 (95% CI, 1.00â1.14); P=0.02] predicted increased admission cost. CMR LVEF (P=0.08) and EuroScore tended to predict actual admission cost (P=0.09), but SV by CMR (P=0.16) and LV function by echocardiography (P=0.95) did not. In conclusion, in this exploratory investigation, pre-operative CMR findings predicted admission duration and increased admission cost in elective CABG surgery. The cost-effectiveness of CMR in risk stratification in elective CABG surgery merits prospective assessment
A clustering method for graphical handwriting components and statistical writership analysis
Handwritten documents can be characterized by their content or by the shape of the written characters. We focus on the problem of comparing a person\u27s handwriting to a document of unknown provenance using the shape of the writing, as is done in forensic applications. To do so, we first propose a method for processing scanned handwritten documents to decompose the writing into small graphical structures, often corresponding to letters. We then introduce a measure of distance between two such structures that is inspired by the graph edit distance, and a measure of center for a collection of the graphs. These measurements are the basis for an outlier tolerant Kâmeans algorithm to cluster the graphs based on structural attributes, thus creating a template for sorting new documents. Finally, we present a Bayesian hierarchical model to capture the propensity of a writer for producing graphs that are assigned to certain clusters. We illustrate the methods using documents from the Computer Vision Lab dataset. We show results of the identification task under the cluster assignments and compare to the same modeling, but with a less flexible grouping method that is not tolerant of incidental strokes or outliers
Cyclic behaviour of stone and brick masonry under uniaxial compressive loading
An experimental research concerning the uniaxial compressive behaviour of stone and brick specimens, as well as masonry prisms, is presented. Local sandstone and clay brick materials were used in order to obtain results representative with respect to local constructions. Aiming at a comprehensive material description, a set of displacementcontrolled
experiments were carried out, both under monotonic and cyclic compressive loading. The procedure adopted for testing is described and the results are discussed, namely material brittleness, intrinsic variability, energy dissipation and stiffness degradation.Dans cet article une recherche expérimentale
Ă propos du comportement en compression uniaxial de spĂ©cimens de pierre et de la brique, aussi bien que prismes de maçonnerie, est prĂ©sentĂ©. GrĂšs et brique de lâargile locale ont Ă©tĂ© utilisĂ©s pour obtenir des rĂ©sultats reprĂ©sentatifs en ce qui concerne les constructions locales. Avec lâobjective de obtenir une description matĂ©rielle complĂšte, un ensemble de tests contrĂŽlĂ© par dĂ©placement a Ă©tĂ© emportĂ©, sous chargement de compression monotonic et cyclique. La procĂ©dure adoptĂ©e pour tester est dĂ©crite et les rĂ©sultats sont discutĂ©s, nommĂ©ment la fragilitĂ© matĂ©rielle, variabilitĂ© intrinsĂšque des matĂ©riaux, dissipation dâĂ©nergie et dĂ©chĂ©ance de la raideur
Promoter Sequences Prediction Using Relational Association Rule Mining
In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal
Scalelength of disc galaxies
We have derived disk scale lengths for 30374 non-interacting disk galaxies in
all five SDSS bands. Virtual Observatory methods and tools were used to define,
retrieve, and analyse the images for this unprecedentedly large sample
classified as disk/spiral galaxies in the LEDA catalogue. Cross correlation of
the SDSS sample with the LEDA catalogue allowed us to investigate the variation
of the scale lengths for different types of disk/spiral galaxies. We further
investigat asymmetry, concentration, and central velocity dispersion as
indicators of morphological type, and are able to assess how the scale length
varies with respect to galaxy type. We note however, that the concentration and
asymmetry parameters have to be used with caution when investigating type
dependence of structural parameters in galaxies. Here, we present the scale
length derivation method and numerous tests that we have carried out to
investigate the reliability of our results. The average r-band disk scale
length is 3.79 kpc, with an RMS dispersion of 2.05 kpc, and this is a typical
value irrespective of passband and galaxy morphology, concentration, and
asymmetry. The derived scale lengths presented here are representative for a
typical galaxy mass of , and the RMS dispersion
is larger for more massive galaxies. Distributions and typical trends of scale
lengths have also been derived in all the other SDSS bands with linear
relations that indicate the relation that connect scale lengths in one passband
to another. Such transformations could be used to test the results of
forthcoming cosmological simulations of galaxy formation and evolution of the
Hubble sequence.Comment: Accepter for publication in MNRAS (15 pages, 15 figures, and 3
tables
Coherent streamflow variability in Monsoon Asia over the past eight centuriesâlinks to oceanic drivers
Ten of the world's biggest rivers are located entirely within the Asian Monsoon region. They provide water, energy, and food for 1.7 billion people. To manage these critical resources, we need a better understanding of river dischargeâhow does it change over a long time? Are there common variation patterns among rivers? To answer these questions, we use information derived from tree rings to reconstruct average annual river discharge history at 62 gauges in 16 Asian countries. Our reconstruction reveals the riparian footprint of megadroughts and large volcanic eruptions over the past eight centuries. We show that simultaneous droughts and pluvials have often occurred at adjacent river basins in the past, because Asian rivers share common influences from the Pacific, Indian, and Atlantic Oceans. We also show how these oceanic teleconnections change over space and time. Our findings can inform big decisions made on water-dependent infrastructure, thus benefiting the riparian people of the Asian Monsoon region
Assembly of the Red Sequence in Infrared-Selected Galaxy Clusters from the IRAC Shallow Cluster Survey
We present results for the assembly and star formation histories of massive
(~L*) red sequence galaxies in 11 spectroscopically confirmed,
infrared-selected galaxy clusters at 1.0 < z < 1.5, the precursors to
present-day massive clusters with M ~ 10^15 M_sun. Using rest-frame optical
photometry, we investigate evolution in the color and scatter of the red
sequence galaxy population, comparing with models of possible star formation
histories. In contrast to studies of central cluster galaxies at lower redshift
(z < 1), these data are clearly inconsistent with the continued evolution of
stars formed and assembled primarily at a single, much-earlier time.
Specifically, we find that the colors of massive cluster galaxies at z = 1.5
imply that the bulk of star formation occurred at z ~ 3, whereas by z = 1 their
colors imply formation at z ~ 2; therefore these galaxies exhibit approximately
the same luminosity-weighted stellar age at 1 < z < 1.5. This likely reflects
star formation that occurs over an extended period, the effects of significant
progenitor bias, or both. Our results generally indicate that massive cluster
galaxy populations began forming a significant mass of stars at z >~ 4,
contained some red spheroids by z ~ 1.5, and were actively assembling much of
their final mass during 1 < z < 2 in the form of younger stars. Qualitatively,
the slopes of the cluster color-magnitude relations are consistent with no
significant evolution relative to local clusters.Comment: 24 pages, 9 figures, accepted to Ap
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