Relevant bibliographies by topics / Decomposition by subpopulations

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Academic literature on the topic 'Decomposition by subpopulations'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Decomposition by subpopulations"

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Rühl, Martin, Wolf-Dietrich Hardt, and Uwe Sauer. "Subpopulation-Specific Metabolic Pathway Usage in Mixed Cultures as Revealed by Reporter Protein-Based13C Analysis." Applied and Environmental Microbiology 77, no. 5 (January 7, 2011): 1816–21. http://dx.doi.org/10.1128/aem.02696-10.

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ABSTRACTMost large-scale biological processes, like global element cycling or decomposition of organic matter, are mediated by microbial consortia. Commonly, the different species in such consortia exhibit mutual metabolic dependencies that include the exchange of nutrients. Despite the global importance, surprisingly little is known about the metabolic interplay between species in particular subpopulations. To gain insight into the intracellular fluxes of subpopulations and their interplay within such mixed cultures, we developed here a13C flux analysis approach based on affinity purification of the recombinant fusion glutathioneS-transferase (GST) and green fluorescent protein (GFP) as a reporter protein. Instead of detecting the13C labeling patterns in the typically used amino acids from the total cellular protein, our method detects these13C patterns in amino acids from the reporter protein that has been expressed in only one species of the consortium. As a proof of principle, we validated our approach by mixed-culture experiments of anEscherichia coliwild type with two metabolic mutants. The reporter method quantitatively resolved the expected mutant-specific metabolic phenotypes down to subpopulation fractions of about 1%.
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Rahman, Pk Md Motiur, and S. Huda. "Decomposition of Income Inequality in Rural Bangladesh." Modern Asian Studies 26, no. 1 (February 1992): 83–93. http://dx.doi.org/10.1017/s0026749x0001595x.

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Until the recent past, the analysis of economic inequality in a country was essentially a macro level exercise. Currently, it has been well recognized that macro level inequality measure is inadequate for assessing a country's economic development and its distributional pattern. To have a clear understanding of the nature, structure and factors responsible for inequality decompositions of aggregate inequality into sectors, sources and determinants of income are essential. The concept of decompositions of inequality signifies that if the population of income recipients is partitioned into a number of subpopulations, the total inequality of the population can be expressed as sum of the inequality within the sub-populations and of the inequality between them.
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Zenga, Michele. "On the decomposition by subpopulations of the point and synthetic Zenga (2007) inequality indexes." METRON 74, no. 3 (May 11, 2016): 375–405. http://dx.doi.org/10.1007/s40300-016-0086-7.

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Taylor-King, Jake P., Asbjørn N. Riseth, Will Macnair, and Manfred Claassen. "Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming." PLOS Computational Biology 16, no. 1 (January 10, 2020): e1007491. http://dx.doi.org/10.1371/journal.pcbi.1007491.

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Słoczyński, Tymon. "Average Gaps and Oaxaca–Blinder Decompositions: A Cautionary Tale about Regression Estimates of Racial Differences in Labor Market Outcomes." ILR Review 73, no. 3 (September 13, 2019): 705–29. http://dx.doi.org/10.1177/0019793919874063.

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Using a recent result from the program evaluation literature, the author demonstrates that the interpretation of regression estimates of between-group differences in wages and other economic outcomes depends on the relative sizes of subpopulations under study. When the disadvantaged group is small, regression estimates are similar to the average loss for disadvantaged individuals. When this group is a numerical majority, regression estimates are similar to the average gain for advantaged individuals. The author analyzes racial test score gaps using ECLS-K data and racial wage gaps using CPS, NLSY79, and NSW data, and shows that the interpretation of regression estimates varies substantially across data sets. Methodologically, he develops a new version of the Oaxaca–Blinder decomposition, in which the unexplained component recovers a parameter referred to as the average outcome gap. Under additional assumptions, this estimand is equivalent to the average treatment effect. Finally, the author reinterprets the Reimers, Cotton, and Fortin decompositions in the context of the program evaluation literature, with attention to the limitations of these approaches.
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Pinheiro, Alusio, and Pranab Kumar Sen. "Quasi U-statistics of innite order and applications to the subgroup decomposition of some diversity measures." São Paulo Journal of Mathematical Sciences 8, no. 2 (December 12, 2014): 285. http://dx.doi.org/10.11606/issn.2316-9028.v8i2p285-309.

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In several applications, information is drawn from quali- tative variables. In such cases, measures of central tendency and dis- persion may be highly inappropriate. Variability for categorical data can be correctly quantied by the so-called diversity measures. These measures can be modied to quantify heterogeneity between groups (or subpopulations). Pinheiro et al. (2005) shows that Hamming distance can be employed in such way and the resulting estimator of hetero- geneity between populations will be asymptotically normal under mild regularity conditions. Pinheiro et al. (2009) proposes a class of weighted U-statistics based on degenerate kernels of degree 2, called quasi U-statistics, with the property of asymptotic normality under suitable conditions. This is generalized to kernels of degree m by Pinheiro et al. (2011). In this work we generalize this class to an innite order degenerate kernel. We then use this powerful tools and the reverse martingale nature of U-statistics to study the asymptotic behavior of a collection of trans- formed classic diversity measures. We are able to estimate them in a common framework instead of the usual individualized estimation procedures. MSC 2000: primary - 62G10; secondary - 62G20, 92D20.
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Nikitin, Stanislav Kolpakov, Arseniy Yashkin, Julia Kravchenko, and Igor Akushevich. "Causes of the Racial Disparities in the Risk of Alzheimer’s Disease." Innovation in Aging 5, Supplement_1 (December 1, 2021): 59. http://dx.doi.org/10.1093/geroni/igab046.227.

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Abstract The risk of Alzheimer’s disease (AD) is not uniform across race-specific subpopulations: Blacks face approximately 50% higher risk of AD onset compared to Whites(Hazard Ratio=1.50; 95%CI:1.46-1.54). We used Blinder-Oaxaca decomposition, modified for censored data, to explain the disparities in the risk of AD between these races in Medicare beneficiaries aged 65+. This approach measures the contributions to the total difference in AD risks from the differences in the prevalence and the difference in magnitude of the effects of each potential explanatory variable. We used hypertension, diabetes mellitus, depression, cerebrovascular and renal diseases as the potential causes of the racial disparities in AD risk. We found that the greatest contribution was due to the impact of arterial hypertension, of which 24% of the effect was due to differences in prevalence and 76% due to the differences in effect magnitude. Unexpectedly, the contributions of other studied diseases explained only a small part of the racial disparity in AD risk. The remaining incidence rates, which could not be explained by the contributions of hypertension and other included diseases in the age-specific analysis, were lower for the Black population, although initially, the total age-specific incidence rates of AD were higher for the Blacks when compared to the Whites. Therefore, our results suggest that targeted interventions in the Black subpopulation are urgently needed to mitigate the adverse health effects of hypertension, independent of the possible causes, such as access to hypertension care, or race-related differences in adherence to antihypertensive treatment.
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Porro, Francesco, and Michele Zenga. "Decomposition by subpopulations of the Zenga-84 inequality curve and the related index $$\zeta $$ζ: an application to 2014 Bank of Italy survey." Statistical Methods & Applications 29, no. 1 (April 10, 2019): 187–207. http://dx.doi.org/10.1007/s10260-019-00459-9.

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Kiang, Mathew V., Nancy Krieger, Caroline O. Buckee, Jukka Pekka Onnela, and Jarvis T. Chen. "Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study." BMJ Open 9, no. 11 (November 2019): e029373. http://dx.doi.org/10.1136/bmjopen-2019-029373.

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ObjectiveDecompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy.SettingAll 50 US states and the District of Columbia, 2010 to 2015.ParticipantsA total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents.Primary and secondary outcome measuresThe race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups.ResultsThere is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income.ConclusionUnderstanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.
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Bond, Kamila, Lee Curtin, Andrea Hawkins-Daarud, Javier Urcuyo, Gustavo De Leon, Christopher Sereduk, Kyle Singleton, et al. "TMIC-58. PATTERNS OF CELLULAR SUBPOPULATION COHABITATION DEFINE GLIOBLASTOMA STATES." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii284. http://dx.doi.org/10.1093/neuonc/noac209.1102.

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Abstract Characterizing intra- and inter-tumoral heterogeneity of glioblastoma has historically relied on discrete classifications of malignant cell populations leaving immune and other cell populations, known to exist admixed with the malignant tumor cells, relatively neglected. Manifold learning algorithms can manage deconvolving multiple cell populations and are often used to track cell state transitions in single cell transcriptomics. We applied a manifold learning approach to TCGA microarray data (Nf525) and bulk transcriptomics of 134 image localized biopsies across 30 patients with primary and 9 with recurrent glioblastoma to further elucidate how to organize biopsies across a spectrum of possible tissue states. The algorithm revealed a low-dimensional manifold graph for which each biopsy lives across 3 polarizing tissue states - one that is associated with diffusely invaded brain, one that is enriched in mesenchymal genes, and one that is enriched in classical proliferative tumor signatures. We deconvolved the bulk transcriptomics of the image-localized biopsies to reveal the relative abundance of 18 malignant, immune, and other cell subpopulations in each biopsy. Overlaying the cellular decomposition onto the manifold graph visualizing the tissue state distributions revealed that transitions between states correlate with changes in cellular cohabitation composition. The tumor cellular cohabitation ecologies have the lowest diversity, as inferred by ecological measures such as Shannon entropy and evenness, at the distal poles of the graph when compared to the transitional arms. Further, we found that the relationship between imaging appearance of contrast enhancement on T1-weighted MRI and the biopsy cellular composition varies with sex and primary vs recurrent biopsy status. The limited spectrum of possible tissue states revealed by the manifold learning is suggestive of a limited continuum along which tumor and non-tumoral cell populations can cohabitate. Such a limited low-dimensional biological space may constrain the dynamics of tumor biology in a predictable manner.
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Dissertations / Theses on the topic "Decomposition by subpopulations"

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VALLI, IGOR. "Scomposizione per sottopopolazioni dell'indice di Bonferroni." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/114249.

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In this thesis it is suggested a decomposition by subpopulation of the Bonferoni index. This purpose is attained at first suggesting a definition of the Bonferroni index for data expressed in terms of frequency distribution and then, using the approach proposed by Zenga (Zenga, 2012) for the decomposition of his index, it follow a decomposition by subpopulation of the Bonferroni index in two steps. The Bonferroni and Zenga indexes, as well as the Gini index (Gini, 1914), are implemented in two steps: in the first step, point measures are made, whereas in the second steps, a synthetic index is obtained averaging the point measures. Until now the proposals suggested for the decompositions of the Gini and Bonferroni indexes were based on the decomposition of the synthetic indexes; in this works, the decomposition by subpopulation is based instead on the decompositions of the point measures. Following this approach, in the first part of the suggested procedure, an additive decomposition is obtained and the contributions of each subpopulation to the point inequality measure, evaluated on the whole population, is computed. From this decomposition, other two decomposition terms are deduced: a within term, that is a measure of inequality evaluated within a subpopulation, and a between term that informs on the inequality derived from the comparison between different subpopulation. In the second part of this work, exploiting these decomposition, an additive decomposition is obtained for the synthetic index. Finally, the methodology proposed is applied on data on the net disposable households income provided by Banca d’Italia in 2014 and comparison among inequality indexes are made.
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