84 research outputs found
Bayesian uncertainty quantification in linear models for diffusion MRI
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue
microstructure. By fitting a model to the dMRI signal it is possible to derive
various quantitative features. Several of the most popular dMRI signal models
are expansions in an appropriately chosen basis, where the coefficients are
determined using some variation of least-squares. However, such approaches lack
any notion of uncertainty, which could be valuable in e.g. group analyses. In
this work, we use a probabilistic interpretation of linear least-squares
methods to recast popular dMRI models as Bayesian ones. This makes it possible
to quantify the uncertainty of any derived quantity. In particular, for
quantities that are affine functions of the coefficients, the posterior
distribution can be expressed in closed-form. We simulated measurements from
single- and double-tensor models where the correct values of several quantities
are known, to validate that the theoretically derived quantiles agree with
those observed empirically. We included results from residual bootstrap for
comparison and found good agreement. The validation employed several different
models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI)
and Constrained Spherical Deconvolution (CSD). We also used in vivo data to
visualize maps of quantitative features and corresponding uncertainties, and to
show how our approach can be used in a group analysis to downweight subjects
with high uncertainty. In summary, we convert successful linear models for dMRI
signal estimation to probabilistic models, capable of accurate uncertainty
quantification.Comment: Added results from a group analysis and a comparison with residual
bootstra
Variational Elliptical Processes
We present elliptical processes, a family of non-parametric probabilistic
models that subsume Gaussian processes and Student's t processes. This
generalization includes a range of new heavy-tailed behaviors while retaining
computational tractability. Elliptical processes are based on a representation
of elliptical distributions as a continuous mixture of Gaussian distributions.
We parameterize this mixture distribution as a spline normalizing flow, which
we train using variational inference. The proposed form of the variational
posterior enables a sparse variational elliptical process applicable to
large-scale problems. We highlight advantages compared to Gaussian processes
through regression and classification experiments. Elliptical processes can
supersede Gaussian processes in several settings, including cases where the
likelihood is non-Gaussian or when accurate tail modeling is essential.Comment: 14 pages, 15 figures, appendix 9 page
Short-Term Antibiotic Treatment Has Differing Long-Term Impacts on the Human Throat and Gut Microbiome
Antibiotic administration is the standard treatment for the bacterium Helicobacter pylori, the main causative agent of peptic ulcer disease and gastric cancer. However, the long-term consequences of this treatment on the human indigenous microbiota are relatively unexplored. Here we studied short- and long-term effects of clarithromycin and metronidazole treatment, a commonly used therapy regimen against H. pylori, on the indigenous microbiota in the throat and in the lower intestine. The bacterial compositions in samples collected over a four-year period were monitored by analyzing the 16S rRNA gene using 454-based pyrosequencing and terminal-restriction fragment length polymorphism (T-RFLP). While the microbial communities of untreated control subjects were relatively stable over time, dramatic shifts were observed one week after antibiotic treatment with reduced bacterial diversity in all treated subjects in both locations. While the microbiota of the different subjects responded uniquely to the antibiotic treatment some general trends could be observed; such as a dramatic decline in Actinobacteria in both throat and feces immediately after treatment. Although the diversity of the microbiota subsequently recovered to resemble the pre treatment states, the microbiota remained perturbed in some cases for up to four years post treatment. In addition, four years after treatment high levels of the macrolide resistance gene erm(B) were found, indicating that antibiotic resistance, once selected for, can persist for longer periods of time than previously recognized. This highlights the importance of a restrictive antibiotic usage in order to prevent subsequent treatment failure and potential spread of antibiotic resistance
Dissemination of Multidrug-Resistant Bacteria into the Arctic
We show that Escherichia coli isolates originating from Arctic birds carry antimicrobial drug resistance determinants. This finding implies that dissemination of drug-resistant bacteria is worldwide. Resistance genes can be found even in a region where no selection pressure for resistance development exists
Persistence of Resistant Staphylococcus epidermidis after Single Course of Clarithromycin
Short course of antimicrobial therapy can select resistant bacteria that persist for 4 years or longer
Pilaantuneen maa-alueen kunnostushankkeen tilaaminen
Tässä ohjeistuksessa esitellään pilaantuneen tai pilaantuneeksi epäillyn alueen tutkimus-, suunnittelu- ja kunnostustoimia. Samalla kuvataan kunnostushankkeen tavanomainen eteneminen, siihen sisältyviä vaiheita sekä eri osapuolten rooleja ja tehtäviä. Tavoitteena on tukea pilaantuneisuusselvitysten ja kunnostustoimien tilaajaa tekemään oikea-aikaisia ja tarkoituksenmukaisia päätöksiä
Plasmid-mediated Quinolone Resistance among Non-TyphiSalmonella enterica Isolates, USA
We determined the prevalence of plasmid-mediated quinolone resistance mechanisms among non-Typhi Salmonella spp. isolated from humans, food animals, and retail meat in the United States in 2007. Six isolates collected from humans harbored aac(6′)Ib-cr or a qnr gene. Most prevalent was qnrS1. No animal or retail meat isolates harbored a plasmid-mediated mechanism
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