Bacteraemia due to carbapenemase-producing Enterobacteriaceae is an emerging medical problem. Management of this entity is complicated by the difficulty in Identifying resistance patterns and the ...limited therapeutic options. A cohort study was performed including all episodes of bloodstream infection due to OXA-48-producing Enterobacteriaceae (O48PE), occurring between July 2010 and April 2012. Data on predisposing factors, clinical presentation, therapy and outcome were collected from medical records. There were 40 cases of bacteraemia caused by O48PE, 35 Klebsiella pneumoniae and five Escherichia coli. Patients were elderly with significant comorbidities (57.5% underlying malignancy). Thirty-five cases (87.5%) were nosocomial, and five (12.5%) were healthcare-associated. Patients had frequently been exposed to antibiotics and to invasive procedures during hospitalization. The most common source of bacteraemia was the urinary tract followed by deep intra-abdominal surgical site infection. Clinical presentation was severe sepsis or shock in 18 cases (45%). Extended-spectrum β-lactamase production was detected in 92.5% of isolates. MIC90 for ertapenem, imipenem and meropenem were 32, 16 and 16 mg/L, respectively. Most frequently preserved antibiotics were amikacin, colistin, tigecycline and fosfomycin. These antibiotics combined are the basis of targeted therapies, including carbapenem in selected cases. Median delay in starting clinically adequate and microbiologically appropriate treatment was 3 days. Crude mortality during admission and within 30 days from bacteraemia was 65% and 50%, respectively. Bloodstream infections caused by O48PE have a poor prognosis. Delay in diagnosis and in initiation of optimal antimicrobial therapy is frequent. Suspicion and rapid identification could contribute to improving outcomes.
Traditional obesity prevention programs are time- and cost-intensive. Mobile phone technology has been successful in changing behaviors and managing weight; however, to our knowledge, its potential ...in young children has yet to be examined.
We assessed the effectiveness of a mobile health (mHealth) obesity prevention program on body fat, dietary habits, and physical activity in healthy Swedish children aged 4.5 y.
From 2014 to 2015, 315 children were randomly assigned to an intervention or control group. Parents in the intervention group received a 6-mo mHealth program. The primary outcome was fat mass index (FMI), whereas the secondary outcomes were intakes of fruits, vegetables, candy, and sweetened beverages and time spent sedentary and in moderate-to-vigorous physical activity. Composite scores for the primary and secondary outcomes were computed.
No statistically significant intervention effect was observed for FMI between the intervention and control group (mean ± SD: -0.23 ± 0.56 compared with -0.20 ± 0.49 kg/m
). However, the intervention group increased their mean composite score from baseline to follow-up, whereas the control group did not (+0.36 ± 1.47 compared with -0.06 ± 1.33 units;
= 0.021). This improvement was more pronounced among the children with an FMI above the median (4.11 kg/m
) (
= 0.019). The odds of increasing the composite score for the 6 dietary and physical activity behaviors were 99% higher for the intervention group than the control group (
= 0.008).
This mHealth obesity prevention study in preschool-aged children found no difference between the intervention and control group for FMI. However, the intervention group showed a considerably higher postintervention composite score (a secondary outcome) than the control group, especially in children with a higher FMI. Further studies targeting specific obesity classes within preschool-aged children are warranted. This trial was registered at clinicaltrials.gov as NCT02021786.
The authors' aim in this cross-sectional study was to characterize levels of objectively measured physical activity and sedentary time in adolescents from 9 European countries. The study comprised ...2,200 European adolescents (1,184 girls) participating in the HELENA cross-sectional study (2006-2008). Physical activity was measured by accelerometry and was expressed as average intensity (counts/minute) and amount of time (minutes/day) spent engaging in moderate- to vigorous-intensity physical activity (MVPA). Time spent in sedentary behaviors was also objectively measured. Cardiorespiratory fitness was measured by means of the 20-m shuttle run test. Level of maternal education was reported by the adolescents. A higher proportion of boys (56.8% of boys vs. 27.5% of girls) met the physical activity recommendations of at least 60 minutes/day of MVPA. Adolescents spent most of the registered time in sedentary behaviors (9 hours/day, or 71% of the registered time). Both average intensity and MVPA were higher in adolescents with high cardiorespiratory fitness, and sedentary time was lower in the high-fitness group. There were no physical activity or sedentary time differences between maternal education categories. These data provide an objective measure of physical activity and amount of time spent in sedentary behaviors in a relatively large number of European adolescents.
Common approaches to gene signature discovery in single‐cell RNA‐sequencing (scRNA‐seq) depend upon predefined structures like clusters or pseudo‐temporal order, require prior normalization, or do ...not account for the sparsity of single‐cell data. We present single‐cell hierarchical Poisson factorization (scHPF), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan et al, , Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, 326) for de novo discovery of both continuous and discrete expression patterns from scRNA‐seq. scHPF does not require prior normalization and captures statistical properties of single‐cell data better than other methods in benchmark datasets. Applied to scRNA‐seq of the core and margin of a high‐grade glioma, scHPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and regionally associated expression biases within glioma subpopulations. scHFP revealed an expression signature that was spatially biased toward the glioma‐infiltrated margins and associated with inferior survival in glioblastoma.
Synopsis
Single‐cell Hierarchical Poisson Factorization (scHPF) is a Bayesian factorization method for de novo discovery of both continuously varying and subpopulation‐specific expression patterns in single‐cell RNA‐sequencing data.
scHPF takes genome‐wide molecular counts as input, avoids prior normalization, captures the statistical structure of scRNA‐seq data better than alternative methods, and has fast, memory‐efficient inference on sparse scRNA‐seq data.
Applied to scRNA‐seq of a spatially sampled high‐grade glioma, scHPF reveals regional differences in lineage resemblance within glioma subpopulations.
One regionally biased gene signature enriched in astrocyte‐like glioma cells is associated with poor survival in glioblastoma.
Single‐cell Hierarchical Poisson Factorization (scHPF) is a Bayesian factorization method for de novo discovery of both continuously varying and subpopulation‐specific expression patterns in single‐cell RNA‐sequencing data.
This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the ...hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in ...parallel. The consumer’s utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and may be correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out-of-stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.
Infinite Factorial Unbounded-State Hidden Markov Model Valera, Isabel; Ruiz, Francisco J. R.; Perez-Cruz, Fernando
IEEE transactions on pattern analysis and machine intelligence,
2016-Sept.-1, 2016-09-00, 2016-9-1, 20160901, Volume:
38, Issue:
9
Journal Article
Peer reviewed
There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in ...accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.
This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multiobject posterior density of the map given a set of measurements. The landmarks in ...the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multiobject posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform a state-of-the-art method.
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