Downregulation of Key Early Events in the Mobilization of Antigen-bearing Dendritic Cells by Leukocyte Immunoglobulin-like Receptor B4 in a Mouse Model of Allergic Pulmonary Inflammation

Leukocyte Immunoglobulin-like Receptor B4 (LILRB4) null mice have an exacerbated T helper cell type 2 (Th2) immune response and pulmonary inflammation compared with Lilrb4+/+ animals when sensitized intranasally with ovalbumin (OVA) and low-dose lipopolysaccharide (LPS) followed by challenge with OVA. Moreover, OVA-challenged Lilrb4−/− mice exhibit greater migration of antigen (Ag)-bearing dendritic cells (DCs) to lymph nodes and accumulation of interleukin 4- and interleukin 5-producing lymph node lymphocytes. The main objective of this study was to determine how the absence of LILRB4 leads to a greater number of DCs in the lymph nodes of Ag-challenged mice and increased lung Th2 inflammation. Mice were sensitized intranasally with PBS alone or containing OVA and LPS; additional cohorts were subsequently challenged with OVA. Expression of chemokine (C-C motif) ligand 21 (CCL21) in the lung was assessed immunohistologically. OVA ingestion and expression of LILRB4 and chemokine (C-C motif) receptor 7 (CCR7) were quantified by flow cytometry. Inhalation of OVA and LPS induced upregulation of LILRB4 selectively on lung Ag-bearing DCs. After sensitization and challenge, the lung lymphatic vessels of Lilrb4−/− mice expressed more CCL21, a chemokine that directs the migration of DCs from peripheral tissue to draining lymph nodes, compared with Lilrb4+/+ mice. In addition, lung DCs of challenged Lilrb4−/− mice expressed more CCR7, the CCL21 receptor. The lungs of challenged Lilrb4−/− mice also contained significantly greater numbers of CD4+ cells expressing interleukin-4 or interleukin-5, consistent with the greater number of Ag-bearing DCs and Th2 cells in lymph nodes and the attendant exacerbated Th2 lung pathology. Our data establish a new mechanism by which LILRB4 can downregulate the development of pathologic allergic inflammation: reduced upregulation of key molecules needed for DC migration leading to decreases in Th2 cells in lymph nodes and their target tissue

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Changes in trabecular bone, hematopoiesis and bone marrow vessels in aplastic anemia, primary osteoporosis, and old age

Retrospective histologic analyses of bone biopsies and of post mortem samples from normal persons of different age groups, and of bone biopsies of age- and sex-matched groups of patients with primary osteoporosis and aplastic anemia show characteristic age dependent as well as pathologic changes including atrophy of osseous trabeculae and of hematopoiesis, and changes in the sinusoidal and arterial capillary compartments. These results indicate the possible role of a microvascular defect in the pathogenesis of osteoporosis and aplastic anemia

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

Semiparametric regression methods for temporal processes subject to multiple sources of censoring

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155547/1/cjs11528.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155547/2/cjs11528_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155547/3/cjs11528-sup-0002-SuppInfo2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155547/4/cjs11528-sup-0001-SuppInfo1.pd

Are quantitative trait-dependent sampling designs cost-effective for analysis of rare and common variants?

Use of trait-dependent sampling designs in whole-genome association studies of sequence data can reduce total sequencing costs with modest losses of statistical efficiency. In a quantitative trait (QT) analysis of data from the Genetic Analysis Workshop 17 mini-exome for unrelated individuals in the Asian subpopulation, we investigate alternative designs that sequence only 50% of the entire cohort. In addition to a simple random sampling design, we consider extreme-phenotype designs that are of increasing interest in genetic association analysis of QTs, especially in studies concerned with the detection of rare genetic variants. We also evaluate a novel sampling design in which all individuals have a nonzero probability of being selected into the sample but in which individuals with extreme phenotypes have a proportionately larger probability. We take differential sampling of individuals with informative trait values into account by inverse probability weighting using standard survey methods which thus generalizes to the source population. In replicate 1 data, we applied the designs in association analysis of Q1 with both rare and common variants in the FLT1 gene, based on knowledge of the generating model. Using all 200 replicate data sets, we similarly analyzed Q1 and Q4 (which is known to be free of association with FLT1) to evaluate relative efficiency, type I error, and power. Simulation study results suggest that the QT-dependent selection designs generally yield greater than 50% relative efficiency compared to using the entire cohort, implying cost-effectiveness of 50% sample selection and worthwhile reduction of sequencing costs

Causal inference in paired two-arm experimental studies under non-compliance with application to prognosis of myocardial infarction

Motivated by a study about prompt coronary angiography in myocardial infarction, we propose a method to estimate the causal effect of a treatment in two-arm experimental studies with possible non-compliance in both treatment and control arms. The method is based on a causal model for repeated binary outcomes (before and after the treatment), which includes individual covariates and latent variables for the unobserved heterogeneity between subjects. Moreover, given the type of non-compliance, the model assumes the existence of three subpopulations of subjects: compliers, never-takers, and always-takers. The model is estimated by a two-step estimator: at the first step the probability that a subject belongs to one of the three subpopulations is estimated on the basis of the available covariates; at the second step the causal effects are estimated through a conditional logistic method, the implementation of which depends on the results from the first step. Standard errors for this estimator are computed on the basis of a sandwich formula. The application shows that prompt coronary angiography in patients with myocardial infarction may significantly decrease the risk of other events within the next two years, with a log-odds of about -2. Given that non-compliance is significant for patients being given the treatment because of high risk conditions, classical estimators fail to detect, or at least underestimate, this effect

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes