A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools

An Infinite Number of Closed FLRW Universes for Any Value of the Spatial Curvature

The Friedman-Lemaitre-Robertson-Walker (FLRW) cosmological models are based on the assumptions of large-scale homogeneity and isotropy of the distribution of matter and energy. They are usually taken to have spatial sections that are simply connected; they have finite volume in the positive curvature case, and infinite volume in the null and negative curvature ones. I want to call the attention to the existence of an infinite number of models, which are based on these same metrics, but have compact, finite volume, multiply connected spatial sections. Some observational implications are briefly mentioned.Comment: 4 pages. Contribution to the 5th International Workshop on Astronomy and Relativistic Astrophysics (Joao Pessoa, PB, Brazil, October 10-12, 2011) and to the 1o. Simposio Jayme Tiomno (Brasilia, DF, Brazil, October 27-28, 2011). In version 2: a few minor corrections; two new references added. In this version: title correction in Ref. 3; dedication paragraph at the en

Lessons From the Latest Data on U.S. Productivity

La croissance de la productivité est examinée par les macro-économistes car elle joue des rôles clés dans la compréhension de l’épargne dans le secteur privé, les sources des chocs macroéconomiques, l’évolution de la compétitivité internationale et la solvabilité des régimes de retraite publics. Toutefois, les estimations des taux de croissance de la productivité anticipées et conjoncturelles souffrent de deux problèmes potentiels : (i) les estimations des tendances récentes sont imprécises, et (ii) les données récemment publiées subissent souvent d’importantes révisions. Cette étude met en évidence la (non-) fiabilité de plusieurs mesures de croissance de la productivité agrégée aux États-Unis en examinant la mesure dans laquelle elles sont révisées au fil du temps. Nous examinons également dans quelle mesure ces révisions contribuent aux erreurs dans les prévisions de croissance de la productivité des États-Unis. Nous constatons que les révisions de données provoquent généralement des changements appréciables des estimations des taux de croissance de la productivité publiés à travers une gamme de différentes mesures de la productivité. D'importantes révisions surviennent souvent des années après la première publication des données, ce qui contribue significativement à l'incertitude générale à laquelle nos décideurs politiques doivent faire face. Cela souligne le besoin de moyens pour réduire l'incertitude à laquelle sont confrontés les décideurs politiques et les politiques robustes à l'incertitude sur les conditions économiques actuelles. La croissance de la productivité est examinée par les macro-économistes car elle joue des rôles clés dans la compréhension de l’épargne dans le secteur privé, les sources des chocs macroéconomiques, l’évolution de la compétitivité internationale et la solvabilité des régimes de retraite publics. Toutefois, les estimations des taux de croissance de la productivité anticipées et conjoncturelles souffrent de deux problèmes potentiels : (i) les estimations des tendances récentes sont imprécises, et (ii) les données récemment publiées subissent souvent d’importantes révisions. Cette étude met en évidence la (non-) fiabilité de plusieurs mesures de croissance de la productivité agrégée aux États-Unis en examinant la mesure dans laquelle elles sont révisées au fil du temps. Nous examinons également dans quelle mesure ces révisions contribuent aux erreurs dans les prévisions de croissance de la productivité des États-Unis. Nous constatons que les révisions de données provoquent généralement des changements appréciables des estimations des taux de croissance de la productivité publiés à travers une gamme de différentes mesures de la productivité. D'importantes révisions surviennent souvent des années après la première publication des données, ce qui contribue significativement à l'incertitude générale à laquelle nos décideurs politiques doivent faire face. Cela souligne le besoin de moyens pour réduire l'incertitude à laquelle sont confrontés les décideurs politiques et les politiques robustes à l'incertitude sur les conditions économiques actuelles.Productivité, analyses en temps réel, révisions de données, projections Greenbook projections , Productivité, analyses en temps réel, révisions de données, projections Greenbook projections

Extending invariant complex structures

We study the problem of extending a complex structure to a given Lie algebra g, which is firstly defined on an ideal h of g. We consider the next situations: h is either complex or it is totally real. The next question is to equip g with an additional structure, such as a (non)-definite metric or a symplectic structure and to ask either h is non-degenerate, isotropic, etc. with respect to this structure, by imposing a compatibility assumption. We show that this implies certain constraints on the algebraic structure of g. Constructive examples illustrating this situation are shown, in particular computations in dimension six are given.Comment: 22 pages, plus an Addendu

Inhibitory neuron migration and IPL formation in the developing zebrafish retina.

The mature vertebrate retina is a highly ordered neuronal network of cell bodies and synaptic neuropils arranged in distinct layers. Little, however, is known about the emergence of this spatial arrangement. Here, we investigate how the three main types of retinal inhibitory neuron (RIN)--horizontal cells (HCs), inner nuclear layer amacrine cells (iACs) and displaced amacrine cells (dACs)--reach their specific laminar positions during development. Using in vivo time-lapse imaging of zebrafish retinas, we show that RINs undergo distinct phases of migration. The first phase, common to all RINs, is bipolar migration directed towards the apicobasal centre of the retina. All RINs then transition to a less directionally persistent multipolar phase of migration. Finally, HCs, iACs and dACs each undergo cell type-specific migration. In contrast to current hypotheses, we find that most dACs send processes into the forming inner plexiform layer (IPL) before migrating through it and inverting their polarity. By imaging and quantifying the dynamics of HCs, iACs and dACs from birth to final position, this study thus provides evidence for distinct and new migration patterns during retinal lamination and insights into the initiation of IPL formation.This work was supported by Wellcome Trust Senior Investigator Award 100329/Z/12/Z to WH and a UK Commonwealth Scholarship to RC.This is the final version of the article. It first appeared from the Company of Biologists via http://dx.doi.org/10.1242/dev.12247

Can GDP measurement be further improved? Data revision and reconciliation

Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. Our new measure, GDP++, fits the data better than GDP+, the GDP growth measure of Aruoba et al. (2016) published by the Federal Reserve Bank of Philadephia. Historical decompositions show that GDE releases are more informative than GDI, while the use of multiple data releases is particularly important in the quarters leading up to the Great Recession

Can GDP Measurement Be Further Improved? Data Revision and Reconciliation

Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. We find that (a) the data prefer averaging across multiple releases instead of discarding early releases in favor of later ones, and (b) that initial estimates of GDI are quite informative. Our new measure, GDP(++), undergoes smaller revisions and tracks expenditure measures of GDP growth more closely than either the simple average of the expenditure and income measures published by the BEA or the GDP growth measure of Aruoba et al. published by the Federal Reserve Bank of Philadelphia