Resiliently evolving supply-demand networks

Peer reviewedPublisher PD

Structure and function in flow networks

Peer reviewedPublisher PD

Constraining Dark Matter Properties with Gamma-Rays from the Galactic Center with Fermi-LAT

We study the capabilities of the Fermi-LAT instrument on board of the Fermi mission to constrain particle dark matter properties, as annihilation cross section, mass and branching ratio into dominant annihilation channels, with gamma-ray observations from the galactic center. Besides the prompt gamma-ray flux, we also take into account the contribution from the electrons/positrons produced in dark matter annihilations to the gamma-ray signal via inverse Compton scattering off the interstellar photon background, which turns out to be crucial in the case of dark matter annihilations into mu+mu- and e+e- pairs. We study the signal dependence on different parameters like the region of observation, the density profile, the assumptions for the dark matter model and the uncertainties in the propagation model. We also show the effect of the inclusion of a 20% systematic uncertainty in the gamma-ray background. If Fermi-LAT is able to distinguish a possible dark matter signal from the large gamma-ray background, we show that for dark matter masses below ~200 GeV, Fermi-LAT will likely be able to determine dark matter properties with good accuracy.Comment: 38 pages, 13 figures, 4 tables; to match published versio

Trafficking Coordinate Description of Intracellular Transport Control of Signaling Networks

Many cellular networks rely on the regulated transport of their components to transduce extracellular information into precise intracellular signals. The dynamics of these networks is typically described in terms of compartmentalized chemical reactions. There are many important situations, however, in which the properties of the compartments change continuously in a way that cannot naturally be described by chemical reactions. Here, we develop an approach based on transport along a trafficking coordinate to precisely describe these processes and we apply it explicitly to the TGF-{\beta} signal transduction network, which plays a fundamental role in many diseases and cellular processes. The results of this newly introduced approach accurately capture for the first time the distinct TGF-{\beta} signaling dynamics of cells with and without cancerous backgrounds and provide an avenue to predict the effects of chemical perturbations in a way that closely recapitulates the observed cellular behavior.Comment: 17 pages, 5 figure

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Ischemic Tolerance Protects the Rat Retina from Glaucomatous Damage

Glaucoma is a leading cause of acquired blindness which may involve an ischemic-like insult to retinal ganglion cells and optic nerve head. We investigated the effect of a weekly application of brief ischemia pulses (ischemic conditioning) on the rat retinal damage induced by experimental glaucoma. Glaucoma was induced by weekly injections of chondroitin sulfate (CS) in the rat eye anterior chamber. Retinal ischemia was induced by increasing intraocular pressure to 120 mmHg for 5 min; this maneuver started after 6 weekly injections of vehicle or CS and was weekly repeated in one eye, while the contralateral eye was submitted to a sham procedure. Glaucoma was evaluated in terms of: i) intraocular pressure (IOP), ii) retinal function (electroretinogram (ERG)), iii) visual pathway function (visual evoked potentials, (VEPs)) iv) histology of the retina and optic nerve head. Retinal thiobarbituric acid substances levels were assessed as an index of lipid peroxidation. Ischemic conditioning significantly preserved ERG, VEPs, as well as retinal and optic nerve head structure from glaucomatous damage, without changes in IOP. Moreover, ischemia pulses abrogated the increase in lipid peroxidation induced by experimental glaucoma. These results indicate that induction of ischemic tolerance could constitute a fertile avenue for the development of new therapeutic strategies in glaucoma treatment

Effects of treated wastewater irrigation on the establishment of young grapevines

Irrigation with treated wastewater could produce excessive accumulations within the plant and soil, negatively affecting the yield and production quality. In addition, the presence of biological and chemical contaminants could harm the agricultural environment, as well as the health of farmers and consumers. During this work, the suitability of secondary and tertiary treated wastewater for use in young grapevines was evaluated by studying the effect of the wastewater irrigation on the soil-plant system, crop yield, fruit quality and the presence of inorganic chemical contamination (salts, elements and heavy metals), organic chemical contamination (polycyclic aromatic hydrocarbons) and microbial contamination (E. coli, total coliforms). The results show that tertiary treated wastewater had positive impact on plant growth and yield while secondary treated wastewater had negative impact on fruit safety in comparison with tap water. Sodium levels in soils irrigated with treated wastewater increased at the end of the irrigation period while decreased during the wet season. The total polycyclic aromatic hydrocarbon concentrations in the soils ranged from 363 μg/kg to 374 μg/kg at the end of the experiment for all irrigation treatments applied. The use of tertiary treated wastewater was recommended for the irrigation of young grapevines as an alternative water source secured protection of environment, plant health and fruit quality

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks