Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

Imaging Mass Spectrometry Detection of Gangliosides Species in the Mouse Brain following Transient Focal Cerebral Ischemia and Long-Term Recovery

Gangliosides, a member of the glycosphingolipid family, are heterogeneously expressed in biological membranes and are particularly enriched within the central nervous system. Gangliosides consist of mono- or poly-sialylated oligosaccharide chains of variable lengths attached to a ceramide unit and are found to be intimately involved in brain disease development. The purpose of this study is to examine the spatial profile of ganglioside species using matrix-assisted laser desorption/ionization (MALDI) imaging (IMS) following middle cerebral artery occlusion (MCAO) reperfusion injury in the mouse. IMS is a powerful method to not only discriminate gangliosides by their oligosaccharide components, but also by their carbon length within their sphingosine base. Mice were subjected to a 30 min unilateral MCAO followed by long-term survival (up to 28 days of reperfusion). Brain sections were sprayed with the matrix 5-Chloro-2-mercaptobenzothiazole, scanned and analyzed for a series of ganglioside molecules using an Applied Biosystems 4800 MALDI TOF/TOF. Traditional histological and immunofluorescence techniques were performed to assess brain tissue damage and verification of the expression of gangliosides of interest. Results revealed a unique anatomical profile of GM1, GD1 and GT1b (d18∶1, d20∶1 as well as other members of the glycosphingolipid family). There was marked variability in the ratio of expression between ipsilateral and contralateral cortices for the various detected ganglioside species following MCAO-reperfusion injury. Most interestingly, MCAO resulted in the transient induction of both GM2 and GM3 signals within the ipsilateral hemisphere; at the border of the infarcted tissue. Taken together, the data suggest that brain region specific expression of gangliosides, particularly with respect to hydrocarbon length, may play a role in neuronal responses to injury

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels