Hafnia alvei in stool specimens from patients with diarrhea and healthy controls

We found an epidemiological association of Hafnia alvei with diarrhea, because the organism was isolated from 12 of 77 (16%) adult Finnish tourists to Morocco who developed diarrhea and from 0 of 321 tourists without diarrhea (P < 0.001). From another group of 112 adult Finnish diarrheal patients, only 2 (2%) yielded H. alvei. In contrast to some Bangladeshi strains of H. alvei, the Finnish strains were negative for the attachment-effacement lesion by an in vitro fluorescent acting staining test and also did not show homology to the Escherichia coli attachment-effacement gene (eaeA) by PCR. These results suggest that a mechanism or mechanisms other than the attachment-effacement lesion may also be involved in the association of H. alvei with diarrhea

The Intentional Use of Service Recovery Strategies to Influence Consumer Emotion, Cognition and Behaviour

Service recovery strategies have been identified as a critical factor in the success of. service organizations. This study develops a conceptual frame work to investigate how specific service recovery strategies influence the emotional, cognitive and negative behavioural responses of . consumers., as well as how emotion and cognition influence negative behavior. Understanding the impact of specific service recovery strategies will allow service providers' to more deliberately and intentionally engage in strategies that result in positive organizational outcomes. This study was conducted using a 2 x 2 between-subjects quasi-experimental design. The results suggest that service recovery has a significant impact on emotion, cognition and negative behavior. Similarly, satisfaction, negative emotion and positive emotion all influence negative behavior but distributive justice has no effect

A population of highly energetic transient events in the centres of active galaxies

Recent all-sky surveys have led to the discovery of new types of transients. These include stars disrupted by the central supermassive black hole, and supernovae that are 10–100 times more energetic than typical ones. However, the nature of even more energetic transients that apparently occur in the innermost regions of their host galaxies is hotly debated1,2,3. Here we report the discovery of the most energetic of these to date: PS1-10adi, with a total radiated energy of ~2.3 × 1052 erg. The slow evolution of its light curve and persistently narrow spectral lines over ∼ 3 yr are inconsistent with known types of recurring black hole variability. The observed properties imply powering by shock interaction between expanding material and large quantities of surrounding dense matter. Plausible sources of this expanding material are a star that has been tidally disrupted by the central black hole, or a supernova. Both could satisfy the energy budget. For the former, we would be forced to invoke a new and hitherto unseen variant of a tidally disrupted star, while a supernova origin relies principally on environmental effects resulting from its nuclear location. Remarkably, we also discover that PS1-10adi is not an isolated case. We therefore surmise that this new population of transients has previously been overlooked due to incorrect association with underlying central black hole activity

A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors

IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their sources. High-energy muon neutrinos are observed via the secondary muons produced in charge current interactions with nuclei in the ice. Currently, the best performing muon track directional reconstruction is based on a maximum likelihood method using the arrival time distribution of Cherenkov photons registered by the experiment\u27s photomultipliers. A known systematic shortcoming of the prevailing method is to assume a continuous energy loss along the muon track. However at energies >1 TeV the light yield from muons is dominated by stochastic showers. This paper discusses a generalized ansatz where the expected arrival time distribution is parametrized by a stochastic muon energy loss pattern. This more realistic parametrization of the loss profile leads to an improvement of the muon angular resolution of up to 20% for through-going tracks and up to a factor 2 for starting tracks over existing algorithms. Additionally, the procedure to estimate the directional reconstruction uncertainty has been improved to be more robust against numerical errors

A2ML1 and otitis media: novel variants, differential expression, and relevant pathways

A genetic basis for otitis media is established, however, the role of rare variants in disease etiology is largely unknown. Previously a duplication variant within A2ML1 was identified as a significant risk factor for otitis media in an indigenous Filipino population and in US children. In this report exome and Sanger sequencing was performed using DNA samples from the indigenous Filipino population, Filipino cochlear implantees, US probands, Finnish, and Pakistani families with otitis media. Sixteen novel, damaging A2ML1 variants identified in otitis media patients were rare or low-frequency in population-matched controls. In the indigenous population, both gingivitis and A2ML1 variants including the known duplication variant and the novel splice variant c.4061 + 1 G>C were independently associated with otitis media. Sequencing of salivary RNA samples from indigenous Filipinos demonstrated lower A2ML1 expression according to the carriage of A2ML1 variants. Sequencing of additional salivary RNA samples from US patients with otitis media revealed differentially expressed genes that are highly correlated with A2ML1 expression levels. In particular, RND3 is upregulated in both A2ML1 variant carriers and high-A2ML1 expressors. These findings support a role for A2ML1 in keratinocyte differentiation within the middle ear as part of otitis media pathology and the potential application of ROCK inhibition in otitis media

Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for over-represented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer (GOTO), and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification

FUT2 Variants Confer Susceptibility to Familial Otitis Media

Non-secretor status due to homozygosity for the common FUT2 variant c.461G>A (p.Trp154∗) is associated with either risk for autoimmune diseases or protection against viral diarrhea and HIV. We determined the role of FUT2 in otitis media susceptibility by obtaining DNA samples from 609 multi-ethnic families and simplex case subjects with otitis media. Exome and Sanger sequencing, linkage analysis, and Fisher exact and transmission disequilibrium tests (TDT) were performed. The common FUT2 c.604C>T (p.Arg202∗) variant co-segregates with otitis media in a Filipino pedigree (LOD = 4.0). Additionally, a rare variant, c.412C>T (p.Arg138Cys), is associated with recurrent/chronic otitis media in European-American children (p = 1.2 × 10−5) and US trios (TDT p = 0.01). The c.461G>A (p.Trp154∗) variant was also over-transmitted in US trios (TDT p = 0.01) and was associated with shifts in middle ear microbiota composition (PERMANOVA p 20 were combined, FUT2 variants were over-transmitted in trios (TDT p = 0.001). Fut2 is transiently upregulated in mouse middle ear after inoculation with non-typeable Haemophilus influenzae. Four FUT2 variants—namely p.Ala104Val, p.Arg138Cys, p.Trp154∗, and p.Arg202∗—reduced A antigen in mutant-transfected COS-7 cells, while the nonsense variants also reduced FUT2 protein levels. Common and rare FUT2 variants confer susceptibility to otitis media, likely by modifying the middle ear microbiome through regulation of A antigen levels in epithelial cells. Our families demonstrate marked intra-familial genetic heterogeneity, suggesting that multiple combinations of common and rare variants plus environmental factors influence the individual otitis media phenotype as a complex trait

Processing GOTO data with the Rubin Observatory LSST Science Pipelines II: forced photometry and light curves

We have adapted the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines to process data from the Gravitational-Wave Optical Transient Observer (GOTO) prototype. In this paper, we describe how we used the Rubin Observatory LSST Science Pipelines to conduct forced photometry measurements on nightly GOTO data. By comparing the photometry measurements of sources taken on multiple nights, we find that the precision of our photometry is typically better than 20 mmag for sources brighter than 16 mag. We also compare our photometry measurements against colour-corrected PanSTARRS photometry, and find that the two agree to within 10 mmag (1σ) for bright (i.e., ∼ 14th mag) sources to 200 mmag for faint (i.e., ∼ 18th mag) sources. Additionally, we compare our results to those obtained by GOTO’s own in-house pipeline, gotophoto, and obtain similar results. Based on repeatability measurements, we measure a 5σ L-band survey depth of between 19 and 20 magnitudes, depending on observing conditions. We assess, using repeated observations of non-varying standard SDSS stars, the accuracy of our uncertainties, which we find are typically overestimated by roughly a factor of two for bright sources (i.e., 17th mag). Finally, we present lightcurves for a selection of variable sources, and compare them to those obtained with the Zwicky Transient Factory and GAIA. Despite the Rubin Observatory LSST Science Pipelines still undergoing active development, our results show that they are already delivering robust forced photometry measurements from GOTO data

Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream

Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritise human vetting efforts and inform future model optimisation via active learning. To fully realise the potential of this architecture, we present a fully-automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1%) compared against classifiers trained with fully human-labelled datasets, whilst being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community