6,710 research outputs found

    Levered and unlevered Beta

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    We prove that in a world without leverage cost the relationship between the levered beta ( L) and the unlevered beta ( u) is the No-costs-of-leverage formula: L = u + ( u - d) D (1 - T) / E. We also analyze 6 alternative valuation theories proposed in the literature to estimate the relationship between the levered beta and the unlevered beta (Harris and Pringle (1985), Modigliani and Miller (1963), Damodaran (1994), Myers (1974), Miles and Ezzell (1980), and practitioners) and prove that all provide inconsistent results.unleveredbeta; levered beta; asset beta; value of tax shields; required return to equity; leverage cost;

    Comments on "A reconsideration of tax shield valuation" by Enrique R. Arzac and Lawrence R. Glosten

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    While Arzac and Glosten (2005) affirm that "the value of tax shields depends upon the nature of the equity stochastic process, which, in turn, depends upon the free cash flow process," I prove that the value of tax shields depends only upon the nature of the stochastic process of the net increase of debt. Arzac and Glosten (2005) formulate the constant leverage ratio assumption as Dt = L•Et. The assumption of Fernández (2004) is E{Dt}= L•E{Et}, where E{•} is the expected value operator, D the value of debt, E the equity value, and L a constant. The Arzac and Glosten (2005) assumption requires continuous debt rebalancing, while mine does not. Under both financial policies, the expected leverage ratio is constant, but the Arzac and Glosten (2005) assumption is too extreme.Value of tax shields; required return to equity; cost of capital; net increase of debt;

    Ionella compressa Boyko & Williams & Shields 2017

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    Ionella compressa (Shiino 1964) n. comb. Pseudione compressa Shiino, 1964b: 240 –242, fig.2 (Honohoshi, Amamiooshima, Japan, infesting Neocallichirus jousseaumei (Nobili, 1904)).— Bourdon, 1968: 150, 172 (mention).— Danforth, 1970a: 29 (mention).— Page, 1985: 198 (mention).— Saito et al., 2000: 37 (list).— Markham, 2001: 198, 200 (list).— Itani, 2004b: 37 –38, table 3 (Japan, infesting Paratrypaea bouvieri (Nobili, 1904)). Not Pseudione compressa — Shiino, 1972: 7 (Japan, infesting Heterocarpus sibogae de Man, 1917) (= Pseudione magna Shiino, 1951 fide Markham, 2010: 158). Material examined. None. Distribution. Japan. Host. Neocallichirus jousseaumei (Nobili, 1904) (type host) and Paratrypaea bouvieri (Nobili, 1904). Remarks. The males and females of this species appear very similar to those of Ionella murchisoni Danforth, 1970 and Danforth (1970a) actually compared and contrasted the two species although he did not transfer P. compressa to Ionella. Based on Shiino’s (1964b) description and figures, we herein transfer P. compressa to Ionella. Examination of specimens, ideally from the vicinity of the type locality, is needed to confirm details of the morphology and to determine the relationship of I. compressa to other species of Ionella.Published as part of Boyko, Christopher B., Williams, Jason D. & Shields, Jeffrey D., 2017, Parasites (Isopoda: Epicaridea and Nematoda) from ghost and mud shrimp (Decapoda: Axiidea and Gebiidea) with descriptions of a new genus and a new species of bopyrid isopod and clarification of Pseudione Kossmann, 1881, pp. 251-301 in Zootaxa 4365 (3) on page 266, DOI: 10.11646/zootaxa.4365.3.1, http://zenodo.org/record/111798

    Tandem repeat copy-number variation in protein-coding regions of human genes

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    BACKGROUND: Tandem repeat variation in protein-coding regions will alter protein length and may introduce frameshifts. Tandem repeat variants are associated with variation in pathogenicity in bacteria and with human disease. We characterized tandem repeat polymorphism in human proteins, using the UniGene database, and tested whether these were associated with host defense roles. RESULTS: Protein-coding tandem repeat copy-number polymorphisms were detected in 249 tandem repeats found in 218 UniGene clusters; observed length differences ranged from 2 to 144 nucleotides, with unit copy lengths ranging from 2 to 57. This corresponded to 1.59% (218/13,749) of proteins investigated carrying detectable polymorphisms in the copy-number of protein-coding tandem repeats. We found no evidence that tandem repeat copy-number polymorphism was significantly elevated in defense-response proteins (p = 0.882). An association with the Gene Ontology term 'protein-binding' remained significant after covariate adjustment and correction for multiple testing. Combining this analysis with previous experimental evaluations of tandem repeat polymorphism, we estimate the approximate mean frequency of tandem repeat polymorphisms in human proteins to be 6%. Because 13.9% of the polymorphisms were not a multiple of three nucleotides, up to 1% of proteins may contain frameshifting tandem repeat polymorphisms. CONCLUSION: Around 1 in 20 human proteins are likely to contain tandem repeat copy-number polymorphisms within coding regions. Such polymorphisms are not more frequent among defense-response proteins; their prevalence among protein-binding proteins may reflect lower selective constraints on their structural modification. The impact of frameshifting and longer copy-number variants on protein function and disease merits further investigation

    Shields-Darcy pipingmodel. Verschilanalyse met Sellmeijer en D-GeoFlow.

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    Hydraulic Structures and Flood Ris

    [Shields]

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    [No annotations]Two three-dimensional plaques displaying two shields each. The shields on the upper plaque have a star (left) and a star and cross (right). The lower plaque has fleurs-de-lis (left) and crosses (right). Print, collotype

    Computational identification and analysis of protein short linear motifs

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    Short linear motifs (SLiMs) in proteins can act as targets for proteolytic cleavage, sites of post-translational modification, determinants of sub-cellular localization, and mediators of protein-protein interactions. Computational discovery of SLiMs involves assembling a group of proteins postulated to share a potential motif, masking out residues less likely to contain such a motif, down-weighting shared motifs arising through common evolutionary descent, and calculation of statistical probabilities allowing for the multiple testing of all possible motifs. Much of the challenge for motif discovery lies in the assembly and masking of datasets of proteins likely to share motifs, since the motifs are typically short (between 3 and 10 amino acids in length), so that potential signals can be easily swamped by the noise of stochastically recurring motifs. Focusing on disordered regions of proteins, where SLiMs are predominantly found, and masking out non-conserved residues can reduce the level of noise but more work is required to improve the quality of high-throughput experimental datasets (e.g. of physical protein interactions) as input for computational discovery
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