Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed event arrivals to be naturally incorporated in a tractable manner, while the hidden Markov chain captures the effect of unobservable drivers of the data. In addition to increases in accuracy and interpretability, this method supplements analysis of the latent factors. Further, this procedure naturally incorporates data features such as over-dispersion and autocorrelation. Additional insights can be generated to assist analysis, including a procedure for iterative model improvement. Implementation difficulties are also addressed with a focus on dealing with large data sets, where latent models are especially advantageous due the large number of observations facilitating identification of hidden factors. Namely, computational issues such as numerical underflow and high processing cost arise in this context and in this paper, we produce procedures to overcome these problems. This modelling framework is demonstrated using a large insurance data set to illustrate theoretical, practical and computational contributions and an empirical comparison to other count models highlight the advantages of the proposed approach.Comment: For simulated data sets and code, please go to https://github.com/agi-lab/reserving-MMNP

Metabolic crosstalk: molecular links between glycogen and lipid metabolism in obesity.

Glycogen and lipids are major storage forms of energy that are tightly regulated by hormones and metabolic signals. We demonstrate that feeding mice a high-fat diet (HFD) increases hepatic glycogen due to increased expression of the glycogenic scaffolding protein PTG/R5. PTG promoter activity was increased and glycogen levels were augmented in mice and cells after activation of the mechanistic target of rapamycin complex 1 (mTORC1) and its downstream target SREBP1. Deletion of the PTG gene in mice prevented HFD-induced hepatic glycogen accumulation. Of note, PTG deletion also blocked hepatic steatosis in HFD-fed mice and reduced the expression of numerous lipogenic genes. Additionally, PTG deletion reduced fasting glucose and insulin levels in obese mice while improving insulin sensitivity, a result of reduced hepatic glucose output. This metabolic crosstalk was due to decreased mTORC1 and SREBP activity in PTG knockout mice or knockdown cells, suggesting a positive feedback loop in which once accumulated, glycogen stimulates the mTORC1/SREBP1 pathway to shift energy storage to lipogenesis. Together, these data reveal a previously unappreciated broad role for glycogen in the control of energy homeostasis

Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation

In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate object and part segmentation as an optimization problem and build a hierarchical feature representation including pixel, part, and object-level embeddings to solve it in a bottom-up clustering manner. Pixels are grouped into several clusters where the part-level embeddings serve as cluster centers. Afterwards, object masks are obtained by compositing the part proposals. This bottom-up interaction is shown to be effective in integrating information from lower semantic levels to higher semantic levels. Based on that, our novel approach Compositor produces part and object segmentation masks simultaneously while improving the mask quality. Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4.4% and 7.1% on part and object respectively. Code will be available at https://github.com/TACJu/Compositor

Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

BACKGROUND: Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series. RESULTS: For evenly sampled data, methods based on the classical Fourier periodogram are often used to detect periodically expressed gene. Recently, the Lomb-Scargle algorithm has been applied to unevenly sampled gene expression data for spectral estimation. However, since the Lomb-Scargle method assumes that there is a single stationary sinusoid wave with infinite support, it introduces spurious periodic components in the periodogram for data with a finite length. In this paper, we propose a new spectral estimation algorithm for unevenly sampled gene expression data. The new method is based on signal reconstruction in a shift-invariant signal space, where a direct spectral estimation procedure is developed using the B-spline basis. Experiments on simulated noisy gene expression profiles show that our algorithm is superior to the Lomb-Scargle algorithm and the classical Fourier periodogram based method in detecting periodically expressed genes. We have applied our algorithm to the Plasmodium falciparum and Yeast gene expression data and the results show that the algorithm is able to detect biologically meaningful periodically expressed genes. CONCLUSION: We have proposed an effective method for identifying periodic genes in unevenly sampled space of microarray time series gene expression data. The method can also be used as an effective tool for gene expression time series interpolation or resampling

FGF21 Increases Water Intake, Urine Output and Blood Pressure in Rats

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Fibroblast growth factor 21 (FGF21) is a hormone secreted by the liver in response to metabolic stress. In addition to its well-characterized effects on energy homeostasis, FGF21 has been shown to increase water intake in animals. In this study, we sought to further explore the effects of FGF21 on fluid homeostasis in rats. A single dose of a long-acting FGF21 analog, PF-05231023, significantly increased water consumption, which was accompanied by an elevation in urine output that appeared prior to a significant change in water intake. We observed that FGF21 rapidly and significantly increased heart rate and blood pressure in telemeter-implanted rats, before changes in urine output and water intake were observed. Our data suggest that sympathetic activation may contribute to the pathogenesis by which FGF21 increases blood pressure as the baroreceptor unloading induced reflex tachycardia was significantly elevated in FGF21-treated animals. However, FGF21 was still capable of causing hypertension in animals in which approximately 40% of the sympathetic post-ganglionic neurons were ablated. Our data suggest that FGF21-induced water intake is in fact secondary to diuresis, which we propose to be a compensatory mechanism engaged to alleviate the acute hypertension caused by FGF21

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Quality assurance for Chinese herbal formulae: standardization of IBS-20, a 20-herb preparation

<p>Abstract</p> <p>Background</p> <p>The employment of well characterized test samples prepared from authenticated, high quality medicinal plant materials is key to reproducible herbal research. The present study aims to demonstrate a quality assurance program covering the acquisition, botanical validation, chemical standardization and good manufacturing practices (GMP) production of IBS-20, a 20-herb Chinese herbal formula under study as a potential agent for the treatment of irritable bowel syndrome.</p> <p>Methods</p> <p>Purity and contaminant tests for the presence of toxic metals, pesticide residues, mycotoxins and microorganisms were performed. Qualitative chemical fingerprint analysis and quantitation of marker compounds of the herbs, as well as that of the IBS-20 formula was carried out with high-performance liquid chromatography (HPLC). Extraction and manufacture of the 20-herb formula were carried out under GMP. Chemical standardization was performed with liquid chromatography-mass spectrometry (LC-MS) analysis. Stability of the formula was monitored with HPLC in real time.</p> <p>Results</p> <p>Quality component herbs, purchased from a GMP supplier were botanically and chemically authenticated and quantitative HPLC profiles (fingerprints) of each component herb and of the composite formula were established. An aqueous extract of the mixture of the 20 herbs was prepared and formulated into IBS-20, which was chemically standardized by LC-MS, with 20 chemical compounds serving as reference markers. The stability of the formula was monitored and shown to be stable at room temperature.</p> <p>Conclusion</p> <p>A quality assurance program has been developed for the preparation of a standardized 20-herb formulation for use in the clinical studies for the treatment of irritable bowel syndrome (IBS). The procedures developed in the present study will serve as a protocol for other poly-herbal Chinese medicine studies.</p