Exact reconstruction of gene regulatory networks using compressive sensing.

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness.ResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.ConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies

The Octonions

The octonions are the largest of the four normed division algebras. While somewhat neglected due to their nonassociativity, they stand at the crossroads of many interesting fields of mathematics. Here we describe them and their relation to Clifford algebras and spinors, Bott periodicity, projective and Lorentzian geometry, Jordan algebras, and the exceptional Lie groups. We also touch upon their applications in quantum logic, special relativity and supersymmetry.Comment: 56 pages LaTeX, 11 Postscript Figures, some small correction

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Abstract The gravitational waves (GW170817) produced during a binary neutron star inspiral, followed by a gamma-ray burst (GRB 170817A) and afterglows from X-ray to radio wavelength, were observed. By combining the distance obtained from gravitational waves with the red shift obtained from electromagnetic waves, even the Hubble constant was estimated. This indicates the start of new era of multimessenger astronomy. In addition to the masses of inspiralling neutron stars, the tidal deformability, which depends on the inner structures of neutron stars, has been estimated from gravitational waves. This confirms that even strong interactions can be tested by using gravitational waves. In this article, we review the effect of the tidal deformability of neutron stars on the gravitational waves produced during the inspiral process and discuss the implications of the detected tidal deformability for the neutron star's equations of state

Primer for Image Informatics in Personalized Medicine

AbstractImage informatics encompasses the concept of extracting and quantifying information contained in image data. Scenes, what an image contains, come from many imager devices such as consumer electronics, medical imaging systems, 3D laser scanners, microscopes, or satellites. There is a marked increase in image informatics applications as there have been simultaneous advances in imaging platforms, data availability due to social media, and big data analytics. An area ready to take advantage of these developments is personalized medicine, the concept where the goal is tailor healthcare to the individual. Patient health data is computationally profiled against a large of pool of feature-rich data from other patients to ideally optimize how a physician chooses care. One of the daunting challenges is how to effectively utilize medical image data in personalized medicine. Reliable data analytics products require as much automation as possible, which is a difficulty for data like histopathology and radiology images because we require highly trained expert physicians to interpret the information. This review targets biomedical scientists interested in getting started on tackling image analytics. We present high level discussions of sample preparation and image acquisition; data formats; storage and databases; image processing; computer vision and machine learning; and visualization and interactive programming. Examples will be covered using existing open-source software tools such as ImageJ, CellProfiler, and IPython Notebook. We discuss how difficult real-world challenges faced by image informatics and personalized medicine are being tackled with open-source biomedical data and software