4-Methylumbelliferone improves the thermogenic capacity of brown adipose tissue.

Therapeutic increase of brown adipose tissue (BAT) thermogenesis is of great interest as BAT activation counteracts obesity and insulin resistance. Hyaluronan (HA) is a glycosaminoglycan, found in the extracellular matrix, which is synthesized by HA synthases (Has1/Has2/Has3) from sugar precursors and accumulates in diabetic conditions. Its synthesis can be inhibited by the small molecule 4-methylumbelliferone (4-MU). Here, we show that the inhibition of HA-synthesis by 4-MU or genetic deletion of Has2/Has3 improves BAT`s thermogenic capacity, reduces body weight gain, and improves glucose homeostasis independently from adrenergic stimulation in mice on diabetogenic diet, as shown by a magnetic resonance T2 mapping approach. Inhibition of HA synthesis increases glycolysis, BAT respiration and uncoupling protein 1 expression. In addition, we show that 4-MU increases BAT capacity without inducing chronic stimulation and propose that 4-MU, a clinically approved prescription-free drug, could be repurposed to treat obesity and diabetes

On Optimal Segmentation of Sequential Data

We present an algorithm that eciently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations { also on new data sets { even more eciently

Tracking and Visualization of Changes in High-Dimensional Non-Parametric Distributions

Most real-world systems exhibit a non-stationary behavior, e.g., slow drifts due to wear or fast changes due to external influences. Extracting and quantifying these phenomena is often di#cult due to the lack of a precise mathematical model of the underlying system. We here propose to model such high-level changes of a dynamical system solely on the basis of the observed measurements rather than by modeling the underlying system itself. In particular, we present a method to track and visualize changes in general data distributions. We approach the problem of how to represent continuous changes in high-dimensional non-parametric distributions by identifying anchor distributions and we model the transitions between those anchor distributions by defining a suitable similarity measure. Applications to a high-dimensional chaotic system and to a sleep-onset detection task in EEG demonstrate the e#ciency of this approach

TRACKING AND VISUALIZATION OF CHANGES IN HIGH-DIMENSIONAL NON-PARAMETRIC DISTRIBUTIONS

Abstract. Most real-world systems exhibit a non-stationary behavior, e.g., slow drifts due to wear or fast changes due to external influences. Extracting and quantifying these phenomena is often difficult due to the lack of a precise mathematical model of the underlying system. We here propose to model such high-level changes of a dynamical system solely on the basis of the observed measurements rather than by modeling the underlying system itself. In particular, we present a method to track and visualize changes in general data distributions. We approach the problem of how to represent continuous changes in high-dimensional non-parametric distributions by identifying anchor distributions and we model the transitions between those anchor distributions by defining a suitable similarity measure. Applications to a high-dimensional chaotic system and to a sleep-onset detection task in EEG demonstrate the efficiency of this approach

An On-Line Method For Segmentation And Identification Of Non-Stationary Time Series

. We present a method for the analysis of non-stationary time series from dynamical systems that switch between multiple operating modes. In contrast to other approaches, our method processes the data incrementally and without any training of internal parameters. It straightaway performs an unsupervised segmentation and classification of the data on-the-fly. In many cases it even allows to process incoming data in real-time. The main idea of the approach is to track and segment changes of the probability density of the data in a sliding window on the incoming data stream. An application to a switching dynamical system demonstrates the potential usefulness of the algorithm in a broad range of applications

A Dynamic HMM for On-line Segmentation of Sequential Data

We propose a novel method for the analysis of sequential data that exhibits an inherent mode switching. In particular, the data might be a non-stationary time series from a dynamical system that switches between multiple operating modes. Unlike other approaches, our method processes the data incrementally and without any training of internal parameters. We use an HMM with a dynamically changing number of states and an on-line variant of the Viterbi algorithm that performs an unsupervised segmentation and classification of the data on-the-fly, i.e. the method is able to process incoming data in real-time. The main idea of the approach is to track and segment changes of the probability density of the data in a sliding window on the incoming data stream. The usefulness of the algorithm is demonstrated by an application to a switching dynamical system

Data Set A is a pattern matching problem

Several data sets have been proposed for benchmarking in time series prediction. A popular one is Data Set A from the Santa Fe Competition. This data set was the subject of analysis in many papers. In this note, it is shown that predicting the continuation of Data Set A is nothing else than a pattern matching problem. Looking at studies of this data set, it is remarkable that most of the very good forecasts of Data Set A used upsampled training data. We explain why upsampling is crucial for this data set. Finally, it is demonstrated that simple pattern matching performs as good as sophisticated prediction methods on Data Set A