80 research outputs found

    Pattern Count on Multiply Restricted Permutations

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    Previous work has studied the pattern count on singly restricted permutations. In this work, we focus on patterns of length 3 in multiply restricted permutations, especially for double and triple pattern-avoiding permutations. We derive explicit formulae or generating functions for various occurrences of length 3 patterns on multiply restricted permutations, as well as some combinatorial interpretations for non-trivial pattern relationships.Comment: 23 pages, 2 figure

    Mixed Statistics on 01-Fillings of Moon Polyominoes

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    We establish a stronger symmetry between the numbers of northeast and southeast chains in the context of 01-fillings of moon polyominoes. Let \M be a moon polyomino with nn rows and mm columns. Consider all the 01-fillings of \M in which every row has at most one 1. We introduce four mixed statistics with respect to a bipartition of rows or columns of \M. More precisely, let S⊆{1,2,...,n}S \subseteq \{1,2,..., n\} and R(S)\mathcal{R}(S) be the union of rows whose indices are in SS. For any filling MM, the top-mixed (resp. bottom-mixed) statistic α(S;M)\alpha(S; M) (resp. ÎČ(S;M)\beta(S; M)) is the sum of the number of northeast chains whose top (resp. bottom) cell is in R(S)\mathcal{R}(S), together with the number of southeast chains whose top (resp. bottom) cell is in the complement of R(S)\mathcal{R}(S). Similarly, we define the left-mixed and right-mixed statistics Îł(T;M)\gamma(T; M) and ÎŽ(T;M)\delta(T; M), where TT is a subset of the column index set {1,2,...,m}\{1,2,..., m\}. Let λ(A;M)\lambda(A; M) be any of these four statistics α(S;M)\alpha(S; M), ÎČ(S;M)\beta(S; M), Îł(T;M)\gamma(T; M) and ÎŽ(T;M)\delta(T; M), we show that the joint distribution of the pair (λ(A;M),λ(Aˉ;M))(\lambda(A; M), \lambda(\bar A; M)) is symmetric and independent of the subsets S,TS, T. In particular, the pair of statistics (λ(A;M),λ(Aˉ;M))(\lambda(A;M), \lambda(\bar A; M)) is equidistributed with (\se(M),\ne(M)), where \se(M) and ≠(M)\ne(M) are the numbers of southeast chains and northeast chains of MM, respectively.Comment: 20 pages, 6 figure

    Mixed Statistics on 01-Fillings of Moon Polyominoes

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    Comparison of evolutionary algorithms in gene regulatory network model inference

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    Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very di±cult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insu±cient. Results: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and oŸer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identi¯ed and a platform for development of appropriate model formalisms is established

    Guanosine effect on cholesterol efflux and apolipoprotein E expression in astrocytes

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    The main source of cholesterol in the central nervous system (CNS) is represented by glial cells, mainly astrocytes, which also synthesise and secrete apolipoproteins, in particular apolipoprotein E (ApoE), the major apolipoprotein in the brain, thus generating cholesterol-rich high density lipoproteins (HDLs). This cholesterol trafficking, even though still poorly known, is considered to play a key role in different aspects of neuronal plasticity and in the stabilisation of synaptic transmission. Moreover, cell cholesterol depletion has recently been linked to a reduction in amyloid beta formation. Here we demonstrate that guanosine, which we previously reported to exert several neuroprotective effects, was able to increase cholesterol efflux from astrocytes and C6 rat glioma cells in the absence of exogenously added acceptors. In this effect the phosphoinositide 3 kinase/extracellular signal-regulated kinase 1/2 (PI3K/ERK1/2) pathway seems to play a pivotal role. Guanosine was also able to increase the expression of ApoE in astrocytes, whereas it did not modify the levels of ATP-binding cassette protein A1 (ABCA1), considered the main cholesterol transporter in the CNS. Given the emerging role of cholesterol balance in neuronal repair, these effects provide evidence for a role of guanosine as a potential pharmacological tool in the modulation of cholesterol homeostasis in the brain

    Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe

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    We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median z∌0.03z\sim 0.03). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between z∌0.6z\sim 0.6 and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July
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