Multivariate Time Series Imputation: An Approach Based on Dictionary Learning.

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods.

Continuous human uterine NK cell differentiation in response to endometrial regeneration and pregnancy.

Immune cell differentiation is critical for adequate tissue-specific immune responses to occur. Here, we studied differentiation of human uterine natural killer cells (uNK cells). These cells reside in a tissue undergoing constant regeneration and represent the major leukocyte population at the maternal-fetal interface. However, their physiological response during the menstrual cycle and in pregnancy remains elusive. By surface proteome and transcriptome analysis as well as using humanized mice, we identify a differentiation pathway of uNK cells in vitro and in vivo with sequential acquisition of killer cell immunoglobulin-like receptors and CD39. uNK cell differentiation occurred continuously in response to the endometrial regeneration and was driven by interleukin-15. Differentiated uNK cells displayed reduced proliferative capacity and immunomodulatory function including enhanced angiogenic capacity. By studying human uterus transplantation and monozygotic twins, we found that the uNK cell niche could be replenished from circulation and that it was under genetic control. Together, our study uncovers a continuous differentiation pathway of human NK cells in the uterus that is coupled to profound functional changes in response to local tissue regeneration and pregnancy.

Human endometrial MAIT cells are transiently tissue resident and respond to Neisseria gonorrhoeae.

Mucosa-associated invariant T (MAIT) cells are non-classical T cells important in the mucosal defense against microbes. Despite an increasing interest in the immunobiology of the endometrial mucosa, little is known regarding human MAIT cells in this compartment. The potential role of MAIT cells as a tissue-resident local defense against microbes in the endometrium is largely unexplored. Here, we performed a high-dimensional flow cytometry characterization of MAIT cells in endometrium from pre- and postmenopausal women, and in decidua from first-trimester pregnancies. Furthermore, we assessed MAIT cell function by stimulation with Neisseria gonorrhoeae (N. gonorrhoeae). Endometrial MAIT (eMAIT) cells represented a stable endometrial immune cell population as limited dynamic changes were observed during the menstrual cycle, post menopause, or in response to pregnancy. Furthermore, eMAIT cells exhibited an activated tissue-resident phenotype. Despite expressing CD69 and CD103, eMAIT cells were replenished over time by circulating MAIT cells, as assessed using human uterus transplantation as a model. Finally, functional experiments revealed the capability of MAIT cells to respond to the sexually transmitted and tissue-relevant pathogen, N. gonorrhoeae. In conclusion, our study provides novel insight into human MAIT cell dynamics and anti-microbial properties in the human uterus.

Maximum Entropy Expectation-Maximization Algorithm for Fitting Latent-Variable Graphical Models to Multivariate Time Series.

This work is focused on latent-variable graphical models for multivariate time series. We show how an algorithm which was originally used for finding zeros in the inverse of the covariance matrix can be generalized such that to identify the sparsity pattern of the inverse of spectral density matrix. When applied to a given time series, the algorithm produces a set of candidate models. Various information theoretic (IT) criteria are employed for deciding the winner. A novel IT criterion, which is tailored to our model selection problem, is introduced. Some options for reducing the computational burden are proposed and tested via numerical examples. We conduct an empirical study in which the algorithm is compared with the state-of-the-art. The results are good, and the major advantage is that the subjective choices made by the user are less important than in the case of other methods.

[Bed rest when the water breaks early--in accordance with the evidence or old habit?]. / Sängläge vid tidig vattenavgång - enligt evidens eller gammal vana?

Bed rest is often prescribed as part of the treatment plan for women with preterm premature rupture of membranes in the hope of avoid-ing premature birth. The purpose of this study was to determine the routine care of these patients in Swedish hospitals.A survey was sent to all hospitals in Sweden with an obstetrics department (n=45). Out of these, 31 answered our survey (69 %). The women were separated into three groups depending on gestation length (22+0-26+6, 27+0-32+6 and 33+0-36+6).Most hospitals chose to admit the women and to recommend total or partial bed rest. The earlier in the pregnancy the rupture occurred, the more likely the patient was to be confined to total bed rest.The study shows that this topic is controversial. Most hospitals use bed rest even though there is no evidence to support that it is of benefit for the patient. More studies in this area are required in order to find an optimal treatment for this patient group.