Microbial ligand costimulation drives neutrophilic steroid-refractory asthma

Funding: The authors thank the Wellcome Trust (102705) and the Universities of Aberdeen and Cape Town for funding. This research was also supported, in part, by National Institutes of Health GM53522 and GM083016 to DLW. KF and BNL are funded by the Fonds Wetenschappelijk Onderzoek, BNL is the recipient of an European Research Commission consolidator grant and participates in the European Union FP7 programs EUBIOPRED and MedALL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Simpson's paradox and calculation of number needed to treat from meta-analysis

BACKGROUND: Calculation of numbers needed to treat (NNT) is more complex from meta-analysis than from single trials. Treating the data as if it all came from one trial may lead to misleading results when the trial arms are imbalanced. DISCUSSION: An example is shown from a published Cochrane review in which the benefit of nursing intervention for smoking cessation is shown by formal meta-analysis of the individual trial results. However if these patients were added together as if they all came from one trial the direction of the effect appears to be reversed (due to Simpson's paradox). Whilst NNT from meta-analysis can be calculated from pooled Risk Differences, this is unlikely to be a stable method unless the event rates in the control groups are very similar. Since in practice event rates vary considerably, the use a relative measure, such as Odds Ratio or Relative Risk is advocated. These can be applied to different levels of baseline risk to generate a risk specific NNT for the treatment. SUMMARY: The method used to calculate NNT from meta-analysis should be clearly stated, and adding the patients from separate trials as if they all came from one trial should be avoided

Wet Granular Materials

Most studies on granular physics have focused on dry granular media, with no liquids between the grains. However, in geology and many real world applications (e.g., food processing, pharmaceuticals, ceramics, civil engineering, constructions, and many industrial applications), liquid is present between the grains. This produces inter-grain cohesion and drastically modifies the mechanical properties of the granular media (e.g., the surface angle can be larger than 90 degrees). Here we present a review of the mechanical properties of wet granular media, with particular emphasis on the effect of cohesion. We also list several open problems that might motivate future studies in this exciting but mostly unexplored field.Comment: review article, accepted for publication in Advances in Physics; tex-style change

Display of probability densities for data from a continuous distribution

Based on cumulative distribution functions, Fourier series expansion and Kolmogorov tests, we present a simple method to display probability densities for data drawn from a continuous distribution. It is often more efficient than using histograms.Comment: 5 pages, 4 figures, presented at Computer Simulation Studies XXIV, Athens, GA, 201

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

Patterns of Multimorbidity in the Aged Population. Results from the KORA-Age Study

Multimorbidity is a common problem in aged populations with a wide range of individual and societal consequences. The objective of the study was to explore patterns of comorbidity and multimorbidity in an elderly population using different analytical approaches. Data were gathered from the population-based KORA-Age project, which included 4,127 persons aged 65–94 years living in the city of Augsburg and its two surrounding counties in Southern Germany. Information on the presence of 13 chronic conditions was collected in a standardized telephone interview and a self-administered questionnaire. Patterns of comorbidity and multimorbidity were analyzed using prevalence figures, logistic regression models and exploratory tetrachoric factor analysis. The prevalence of multimorbidity (≥2 diseases) was 58.6% in the total sample. Hypertension and diabetes (Odds Ratio [OR] 2.95, 99.58% confidence interval [CI] [2.19–3.96]), as well as hypertension and stroke (OR 2.00, 99.58% CI [1.26–3.16]) most often occurred in combination. This association was independent of age, sex and the presence of other conditions. Using factor analysis, we identified four patterns of multimorbidity: the first pattern includes cardiovascular and metabolic diseases, the second includes joint, liver, lung and eye diseases, the third covers mental and neurologic diseases and the fourth pattern includes gastrointestinal diseases and cancer. 44% of the persons were assigned to at least one of the four multimorbidity patterns; 14% could be assigned to both the cardiovascular/metabolic and the joint/liver/lung/eye pattern. Further common pairs were the mental/neurologic pattern combined with the cardiovascular/metabolic pattern (7.2%) or the joint/liver/lung/eye pattern (5.3%), respectively. Our results confirmed the existence of co-occurrence of certain diseases in elderly persons, which is not caused by chance. Some of the identified patterns of multimorbidity and their overlap may indicate common underlying pathological mechanisms

Mother-infant interactions and regional brain volumes in infancy: an MRI study

Background: It is generally agreed that the human brain is responsive to environmental influences, and that the male brain may be particularly sensitive to early adversity. However, this is largely based on retrospective studies of older children and adolescents exposed to extreme environments in childhood. Less is understood about how normative variations in parent-child interactions are associated with the development of the infant brain in typical settings. Method: To address this, we used magnetic resonance imaging to investigate the relationship between observational measures of mother-infant interactions and regional brain volumes in a community sample of 3-6 month old infants (N=39). In addition, we examined whether this relationship differed in male and female infants. Results: We found that lower maternal sensitivity was correlated with smaller subcortical grey matter volumes in the whole sample, and that this was similar in both sexes. However, male infants who showed greater levels of positive communication and engagement during early interactions had smaller cerebellar volumes. Conclusion These preliminary findings suggest that variations in mother-infant interaction dimensions are associated with differences in infant brain development. Although the study is cross-sectional and causation cannot be inferred, the findings reveal a dynamic interaction between brain and environment that may be important when considering interventions to optimize infant outcomes

Allergen Uptake, Activation, and IL-23 Production by Pulmonary Myeloid DCs Drives Airway Hyperresponsiveness in Asthma-Susceptible Mice

Maladaptive, Th2-polarized inflammatory responses are integral to the pathogenesis of allergic asthma. As regulators of T cell activation, dendritic cells (DCs) are important mediators of allergic asthma, yet the precise signals which render endogenous DCs “pro-asthmatic”, and the extent to which these signals are regulated by the pulmonary environment and host genetics, remains unclear. Comparative phenotypic and functional analysis of pulmonary DC populations in mice susceptible (A/J), or resistant (C3H) to experimental asthma, revealed that susceptibility to airway hyperresponsiveness is associated with preferential myeloid DC (mDC) allergen uptake, and production of Th17-skewing cytokines (IL-6, IL-23), whereas resistance is associated with increased allergen uptake by plasmacytoid DCs. Surprisingly, adoptive transfer of syngeneic HDM-pulsed bone marrow derived mDCs (BMDCs) to the lungs of C3H mice markedly enhanced lung IL-17A production, and rendered them susceptible to allergen-driven airway hyperresponsiveness. Characterization of these BMDCs revealed levels of antigen uptake, and Th17 promoting cytokine production similar to that observed in pulmonary mDCs from susceptible A/J mice. Collectively these data demonstrate that the lung environment present in asthma-resistant mice promotes robust pDC allergen uptake, activation, and limits Th17-skewing cytokine production responsible for driving pathologic T cell responses central to the development of allergen-induced airway hyperresponsiveness

A genomic catalog of Earth’s microbiomes

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes