Prospective surveillance of multivariate spatial disease data

Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented

CD71+ erythroid cells from neonates born to women with preterm labor regulate cytokine and cellular responses

Neonatal CD71+ erythroid cells are thought to have immunosuppressive functions. Recently, we demonstrated that CD71+ erythroid cells from neonates born to women who underwent spontaneous preterm labor (PTL) are reduced to levels similar to those of term neonates; yet, their functional properties are unknown. Herein, we investigated the functionality of CD71+ erythroid cells from neonates born to women who underwent spontaneous preterm or term labor. CD71+ erythroid cells from neonates born to women who underwent PTL displayed a similar mRNA profile to that of those from term neonates. The direct contact between preterm or term neonatal CD71+ erythroid cells and maternal mononuclear immune cells, but not soluble products from these cells, induced the release of proinflammatory cytokines and a reduction in the release of TGFâ β. Moreover, PTLâ derived neonatal CD71+ erythroid cells (1) modestly altered CD8+ T cell activation; (2) inhibited conventional CD4+ and CD8+ Tâ cell expansion; (3) suppressed the expansion of CD8+ regulatory T cells; (4) regulated cytokine responses mounted by myeloid cells in the presence of a microbial product; and (5) indirectly modulated Tâ cell cytokine responses. In conclusion, neonatal CD71+ erythroid cells regulate neonatal Tâ cell and myeloid responses and their direct contact with maternal mononuclear cells induces a proinflammatory response. These findings provide insight into the biology of neonatal CD71+ erythroid cells during the physiologic and pathologic processes of labor.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142950/1/jlb10051_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142950/2/jlb10051-sup-0003-TableS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142950/3/jlb10051-sup-0002-TableS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142950/4/jlb10051-sup-0001-Figures.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142950/5/jlb10051.pd

Sparsest factor analysis for clustering variables: a matrix decomposition approach

We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA

Biological measurement beyond the quantum limit

Quantum noise places a fundamental limit on the per photon sensitivity attainable in optical measurements. This limit is of particular importance in biological measurements, where the optical power must be constrained to avoid damage to the specimen. By using non-classically correlated light, we demonstrated that the quantum limit can be surpassed in biological measurements. Quantum enhanced microrheology was performed within yeast cells by tracking naturally occurring lipid granules with sensitivity 2.4 dB beyond the quantum noise limit. The viscoelastic properties of the cytoplasm could thereby be determined with a 64% improved measurement rate. This demonstration paves the way to apply quantum resources broadly in a biological context

Single particle tracking in systems showing anomalous diffusion: the role of weak ergodicity breaking

Anomalous diffusion has been widely observed by single particle tracking microscopy in complex systems such as biological cells. The resulting time series are usually evaluated in terms of time averages. Often anomalous diffusion is connected with non-ergodic behaviour. In such cases the time averages remain random variables and hence irreproducible. Here we present a detailed analysis of the time averaged mean squared displacement for systems governed by anomalous diffusion, considering both unconfined and restricted (corralled) motion. We discuss the behaviour of the time averaged mean squared displacement for two prominent stochastic processes, namely, continuous time random walks and fractional Brownian motion. We also study the distribution of the time averaged mean squared displacement around its ensemble mean, and show that this distribution preserves typical process characteristic even for short time series. Recently, velocity correlation functions were suggested to distinguish between these processes. We here present analytucal expressions for the velocity correlation functions. Knowledge of the results presented here are expected to be relevant for the correct interpretation of single particle trajectory data in complex systems.Comment: 15 pages, 15 figures; References adde

Semi-sparse PCA

It is well-known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition. We adopt a new approach to the EFA estimation and achieve a new characterization of the factor indeterminacy problem. A new alternative model is proposed, which gives determinate factors and can be seen as a semi-sparse principal component analysis (PCA). An alternating algorithm is developed, where in each step a Procrustes problem is solved. It is demonstrated that the new model/algorithm can act as a specific sparse PCA and as a low-rank-plus-sparse matrix decomposition. Numerical examples with several large data sets illustrate the versatility of the new model, and the performance and behaviour of its algorithmic implementation

Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace

Characteristics of clinical trials in rare vs. common diseases : A register-based Latvian study

Publisher Copyright: © 2018 Logviss et al. This is an open ccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and eproduction in any medium, provided the original author and source are credited.Background Conducting clinical studies in small populations may be very challenging; therefore quality of clinical evidence may differ between rare and non-rare disease therapies. Objective This register-based study aims to evaluate the characteristics of clinical trials in rare diseases conducted in Latvia and compare them with clinical trials in more common conditions. Methods The EU Clinical Trials Register (clinicaltrialsregister.eu) was used to identify interventional clinical trials related to rare diseases (n = 51) and to compose a control group of clinical trials in non-rare diseases (n = 102) for further comparison of the trial characteristics. Results We found no significant difference in the use of overall survival as a primary endpoint in clinical trials between rare and non-rare diseases (9.8% vs. 13.7%, respectively). However, clinical trials in rare diseases were less likely to be randomized controlled trials (62.7% vs. 83.3%). Rare and non-rare disease clinical trials varied in masking, with rare disease trials less likely to be double blind (45.1% vs. 63.7%). Active comparators were less frequently used in rare disease trials (36.4% vs. 58.8% of controlled trials). Clinical trials in rare diseases enrolled fewer participants than those in non-rare diseases: In Latvia (mean 18.3 vs. 40.2 subjects, respectively), in the European Economic Area (mean 181.0 vs. 626.9 subjects), and in the whole clinical trial (mean 335.8 vs. 1406.3 subjects). Although, we found no significant difference in trial duration between the groups (mean 38.3 vs. 36.4 months). Conclusions The current study confirms that clinical trials in rare diseases vary from those in non-rare conditions, with notable differences in enrollment, randomization, masking, and the use of active comparators. However, we found no significant difference in trial duration and the use of overall survival as a primary endpoint.publishersversionPeer reviewe

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time

A Role for the Inflammasome in Spontaneous Labor at Term

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143689/1/aji12440.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143689/2/aji12440_am.pd