Journal articles on the topic 'Decomposition by subpopulations'

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%.

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.

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.

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.

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.

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.

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.

ABSTRACT We present a photometric study of the field lenticular galaxy NGC 4546 using Gemini/GMOS imaging in g′r′i′z′. We perform a 2D image decomposition of the surface brightness distribution of the galaxy using galfit, finding that four components adequately describe it. The subtraction of this model from our images and the construction of a colour map allow us to examine in great detail the asymmetric dust structures around the galactic centre. In addition, we perform a detailed analysis of the globular cluster (GC) system of NGC 4546. Using a Gaussian Mixture Model algorithm in the colour–colour plane, we detected hints of multiple groups of GC candidates: the classic blue and red subpopulations, a group with intermediate colours that present a concentrated spatial distribution towards the galaxy, and an additional group towards the red end of the colour distribution. We estimate a total GC population for NGC 4546 of 390 ± 60 members and specific frequency SN = 3.3 ± 0.7, which is relatively high compared to the typical value for galaxies of similar masses and environment. We suggest that the unusual GC population substructures were possibly formed during the interaction that led to the formation of the young ultra-compact dwarf (NGC 4546-UCD1) found in this system. Finally, we estimate the distance modulus of NGC 4546 by analysing its luminosity function, resulting in (m − M) = 30.75 ± 0.12 mag (14.1 Mpc).

ObjectiveTeenage pregnancy has become a public health concern in Uganda because of its negative consequences to both the mother and child. The objective of this study was to examine the determinants of change in the inequality and associated predictors of teenage pregnancy in Uganda for the period 2006–2016.Study designA retrospective national cross-sectional study.SettingUganda.ParticipantsUganda Demographic and Health Survey secondary data of only female teenagers aged 15–19 years. The samples selected for analyses were 1936 in 2006; 2048 in 2011 and 4264 in 2016.Outcome measureThe primary outcome was teenage pregnancy. Analysis was performed using the logistic regression, equiplots, concentration curve, normalised concentration index, decomposition of the concentration index and Oaxaca-type decomposition.ResultsThe prevalence of teenage pregnancy has seemingly remained high and almost constant from 2006 to 2016 with the risk worsening to the disadvantage of the poor. Household wealth-index, teenagers’ years of education, early sexual debut and child marriage were the main key predictors and contributors of the large inequality in teenage pregnancy from 2006 to 2016.ConclusionTeenage pregnancy is disproportionately prevalent among different subpopulations of adolescent girls in Uganda. We therefore recommend policy actions to sensitise communities and enforcement of child rights and child protection laws to stop child marriages. There is also need to promote girl child education, improving household incomes, and intensifying mass media awareness on the risks of early pregnancies. Further, ensuring that villages have operational adolescent and youth friendly services as well as incorporating sex education and other different adolescent reproductive health programmes in school curriculum will be key measures in reducing the large inequality in teenage pregnancy.

The constraint-handling methods using multiobjective techniques in evolutionary algorithms have drawn increasing attention from researchers. This paper proposes an efficient conical area differential evolution (CADE) algorithm, which employs biased decomposition and dual populations for constrained optimization by borrowing the idea of cone decomposition for multiobjective optimization. In this approach, a conical subpopulation and a feasible subpopulation are designed to search for the global feasible optimum, along the Pareto front and the feasible segment, respectively, in a cooperative way. In particular, the conical subpopulation aims to efficiently construct and utilize the Pareto front through a biased cone decomposition strategy and conical area indicator. Neighbors in the conical subpopulation are fully exploited to assist each other to find the global feasible optimum. Afterwards, the feasible subpopulation is ranked and updated according to a tolerance-based rule to heighten its diversity in the early stage of evolution. Experimental results on 24 benchmark test cases reveal that CADE is capable of resolving the constrained optimization problems more efficiently as well as producing solutions that are significantly competitive with other popular approaches.

Abstract. The analysis of grain-size distributions has a long tradition in Quaternary Science and disciplines studying Earth surface and subsurface deposits. The decomposition of multi-modal grain-size distributions into inherent subpopulations, commonly termed end-member modelling analysis (EMMA), is increasingly recognised as a tool to infer the underlying sediment sources, transport and (post-)depositional processes. Most of the existing deterministic EMMA approaches are only able to deliver one out of many possible solutions, thereby shortcutting uncertainty in model parameters. Here, we provide user-friendly computational protocols that support deterministic as well as robust (i.e. explicitly accounting for incomplete knowledge about input parameters in a probabilistic approach) EMMA, in the free and open software framework of R. In addition, and going beyond previous validation tests, we compare the performance of available grain-size EMMA algorithms using four real-world sediment types, covering a wide range of grain-size distribution shapes (alluvial fan, dune, loess and floodplain deposits). These were randomly mixed in the lab to produce a synthetic data set. Across all algorithms, the original data set was modelled with mean R2 values of 0.868 to 0.995 and mean absolute deviation (MAD) values of 0.06 % vol to 0.34 % vol. The original grain-size distribution shapes were modelled as end-member loadings with mean R2 values of 0.89 to 0.99 and MAD of 0.04 % vol to 0.17 % vol. End-member scores reproduced the original mixing ratios in the synthetic data set with mean R2 values of 0.68 to 0.93 and MAD of 0.1 % vol to 1.6 % vol. Depending on the validation criteria, all models provided reliable estimates of the input data, and each of the models exhibits individual strengths and weaknesses. Only robust EMMA allowed uncertainties of the end-members to be objectively estimated and expert knowledge to be included in the end-member definition. Yet, end-member interpretation should carefully consider the geological and sedimentological meaningfulness in terms of sediment sources, transport and deposition as well as post-depositional alteration of grain sizes. EMMA might also be powerful in other geoscientific contexts where the goal is to unmix sources and processes from compositional data sets.

Multimodal multiobjective optimization problem (MMOP) is a special kind of multiobjective optimization problem (MOP) with multimodal characteristics, where multiple different Pareto optimal sets (PSs) map to the same Pareto optimal front (PF). To handle MMOPs, a decomposition-based harmony search algorithm (called MOEA/D-HSA) is devised. In MOEA/D-HSA, multiple individuals who are assigned to the same weight vector form a subpopulation for finding multiple different PSs. Then, an environmental selection method based on greedy selection is designed to dynamically adjust the subpopulation scale for keeping the population diversity. Finally, the modified harmony search algorithm and elite learning strategy are utilized to balance the diversity and convergence of the population. Experimental results on the CEC 2019 test suite reveal that MOEA/D-HSA has superior performance than a few state-of-the-art algorithms.

Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front.

This article proposes an evolutionary multiagent framework of the co-operative co-evolutionary multiobjective model (CCMO-EMAS), specifically for equipment layout optimization in engineering. In this framework, each agent is set in a multiobjective cooperative co-evolutionary mode along with the algorithms and corresponding settings. In each iteration, agents are executed in turn, and each agent optimizes a subpopulation from system decomposition. Additionally, the collaboration mechanism is addressed to build complete solutions and evaluate individuals in the co-operative co-evolutionary algorithm. Each subpopulation is optimized once, and the corresponding agent is evaluated based on the improvement of the system memory. Moreover, the agent team is also evolved through an elite genetic algorithm. Finally, the proposed CCMO-EMAS framework is verified in a multimodule satellite equipment layout problem.

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.

In this paper, we propose a decomposition-based evolutionary algorithm with boundary search and archive for constrained multi-objective optimization problems (CMOPs), named CM2M. It decomposes a CMOP into a number of optimization subproblems and optimizes them simultaneously. Moreover, a novel constraint handling scheme based on the boundary search and archive is proposed. Each subproblem has one archive, including a subpopulation and a temporary register. Those individuals with better objective values and lower constraint violations are recorded in the subpopulation, while the temporary register consists of those individuals ever found before. To improve the efficiency of the algorithm, the boundary search method is designed. This method makes the feasible individuals with a higher probability to perform genetic operator with the infeasible individuals. Especially, when the constraints are active at the Pareto solutions, it can play its leading role. Compared with two algorithms, i.e. CMOEA/D-DE-CDP and Gary’s algorithm, on 18 CMOPs, the results show the effectiveness of the proposed constraint handling scheme.

Abstract Objective. Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, (1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and (2) discharge characteristics of large MU populations are often difficult to visualize. Approach. In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main result. Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance. The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

Inoculated excised leaf disk technique allows decomposition of poplar partial resistance to Melampsora larici-populina leaf rust into key epidemiological components such as latent period (LP), uredinia number (UN), uredinia size (US), and spore production (SP) for a given M. larici-populina strain under controlled environmental conditions. Three hundred thirty-six genotypes from an interspecific Populus deltoides × P. trichocarpa F1 progeny segregating for complete resistance to M. larici-populina strain 93ID6 were inoculated with M. larici-populina strain 93CV1. This strain was able to infect the whole family, except few probable recombinants. LP, final UN, and final US after one infectious cycle proved to be relevant complementary descriptors of partial resistance. Area under the disease progress curve and other parameters of uredinia appearance dynamics did not yield additional information. Indirect assessment of SP by US scoring was reliable and easy to access compared with direct spore counting. UN was the only trait for which a doubling of the inoculum pressure level had a significant effect, leading to greater differentiation between genotypes. Consistent with previous studies is the clear relationship between presence of complete resistance against M. larici-populina strain 93ID6 and higher partial resistance to M. larici-populina strain 93CV1 (32% longer LP, 76% smaller UN, and 34% smaller US). In the subpopulation compatible with 93ID6, bimodal distribution of genotypic means for US suggested implication of a major gene inherited from the P. trichocarpa parent. Residual variation was noted for the three epidemiological components, suggesting that additional genes might condition these quantitative traits.

BackgroundBecause conventional single-cell strategies rely on dissociating tissues into suspensions that lose spatial context,1 we developed Slide-TCR-seq to sequence both whole transcriptomes and TCRs with 10µm-spatial resolution, & applied it to renal cell carcinoma (ccRCC) treated with immune checkpoint inhibitors (ICI).MethodsSlide-TCR-seq combines Slide-seqV22 3—a 10µm-resolution spatial approach utilizing mRNA capture and DNA-barcoded beads—with sensitive targeted capture of TCR sequences (rhTCRseq,4 previously developed by our group), thereby enabling amplification of segments extending from upstream of CDR3 to the 3’-end of the TCR transcript (figure 1A). We tested Slide-TCR-seq first on OT-I murine spleen and then applied this methodology to 3 patients‘ pre-αPD-1 ccRCC samples5 and a post-αPD-1 metastasis to investigate the spatial, functional, and clonotypic organization of T cells in relationship to tumor using RCTD,6 spatial enrichment, and spatial expression analyses.ResultsUsing Slide-TCR-seq, we first recapitulated native spatial structure of OT-I mouse spleen (figure 1B-G). TCRα/β CDR3 sequences were detected on 37.1% of beads with Trac/Trbc2 constant sequences—comparable to other scTCRseq methods. Because the clonal and spatial context of TILs have been increasingly implicated in immunotherapy resistance, we used Slide-TCR-seq to analyze a lung ccRCC metastasis following αPD-1 therapy. We employed unsupervised clustering to delineate the tumor, intervening boundary, and lung compartments, and RCTD analyses to spatially map individual cell types; together recapitulating the architecture observed in corresponding histology (figure 2). We identified 1,132 unique clonotypes, with distinct spatial distributions spanning the tissue compartments. Eight clonotypes were significantly enriched in tumor, whereas 5 were depleted (all p<0.05) (figure 3). We then analyzed the relationships between the T cells’ clonotype, gene expression, and tumor infiltration depth among clonotypes. Using a T-cell geneset associated with poor response to ICI,7 we dichotomized T-clonotype beads by geneset expression, and found spatial segregation of this geneset’s expression both within and across clonotypes (figure 4). TCR-4—the most significantly tumor-enriched clonotype—and TCR-2 displayed high expression of the poor ICI response geneset near the tumor’s edge, but low expression deeper in the tumor compartment; indicating that there are transcriptionally distinct subpopulations of these clonotypes, which depended on the extent of their tumor infiltration.Abstract 76 Figure 1Slide-TCR-seq spatially localizes T cell receptors and transcriptome information. a. Schematic of Slide-TCR-seq, in which tissue is placed onto an in situ barcoded bead array. cDNA libraries prepared with Slide-seqV2 are split prior to fragmentation with one portion used for targeted amplification via rhTCRseq optimized for use with Slide-seq libraries. Slide-TCR-seq provides gene expression, cell type, and clonotype information in space. b. Serial sections of the OT-1 mouse spleen with hematoxylin and eosin stain show characteristic architecture of red pulp and white pulp separation. c. Spatial reconstruction of Slide-TCR-seq array for a corresponding section of OT-I mouse spleen, with RCTD immune cell type assignment. NK = natural killer. d. Gene expression gaussian-filtered heatmap for visualizing the spatial distribution of gene markers for marginal zone (Marco), red blood cells (RBCs; Gypa), and CD8 T cells (Cd8a). e and f. Comparing the spatial distribution of constant (left) and variable (right) sequences for TCRα (e) and TCRβ (f), with superimposed density plot. g. The fraction of beads that capture CDR3 variable sequences (y-axis) when constant UMIs are captured (x-axis) for TCRα (left, light blue) and TCRβ (right, dark blue), with the number of corresponding beads along the top axis. All scale bars: 500 µm.Abstract 76 Figure 2Slide-TCR-seq identifies spatial differences between T cell clonotypes in renal cell carcinoma. (a) H&E stain of a ccRCC metastasis to the lung following PD-1 blockade therapy. (b) The compartment assignment of lung (green), immune cell boundary (orange), and tumor (blue) by applying K-nearest neighbors to cell types determined by unsupervised clustering from Slide-TCR-seq of a sequential tissue section. (c) Spatial reconstruction of cell type identifies using RCTD anaysis of the Slide-TCR-seq data. (d) Spatial localization of T cell clonotypes (n=447 clonotypes, colored by clonotype) from the the Slide-TCR-seq data.Abstract 76 Figure 3Top: y-axis Significance of clonotype spatial distributions compared against all other clonotypes with at least ten beads per array from the ccRCC lung metastasis plotted against an x-axis of magnitude of tumor enrichment or depletion (data from n=3 replicate arrays, two one-tailed K-S tests). Bottom: Visualization of selected significant clonotypes, ordered by tumor enrichment, in tissue compartments for a single array (T cells within the tumor compartment are displayed as opaque, T cells within other compartments are displayed as translucent).Abstract 76 Figure 4Spatial and molecular heterogeneity in clonotype gene expression and tumor infiltration. a. The three axes — spatial localization, gene expression, and T cell clonotype — that Slide-TCR-seq can relate. b. Top: distribution of poor response to immune checkpoint inhibitor treatment (’PRI’) geneset7 expression across all clonotypes in the tumor region of the same post-PD1 inhibitor RCC lung metastasis from figures 2–3 (from a single replicate) with kernel density estimation. Yellow = clonotypes with lower than median PRI expression; purple = clonotypes with PRI expression greater than or equal to the median value. Bottom: localization of low (yellow) and high (purple) PRI geneset expression clonotypes within the tumor region (light blue) from the Slide-TCR-seq array shows their distinct spatial separation (light blue = tumor region, orange = boundary region, green = lung region). Scale bar: 500 µm. c. Smoothed histograms comparing the distance infiltrated into tumor by two-tailed K-S test comparing low (yellow) and high (purple) expression clonotypes, as dichotomized by median expression of PRI. d. Comparing distance infiltrated into tumor by two-tailed K-S test between low and high PRI expression T cells across those clonotypes with at least 20 beads (n=7 clonotypes).ConclusionsSlide-TCR-seq effectively integrates spatial transcriptomics with TCR detection at 10µm resolution, thereby relating T cells’ clonality and gene expression to their spatial organization in tumors. Our findings suggest that a clonotype’s T cells may exhibit mixed responses to ICI depending on their spatial localization. The heterogeneity among clonotypes, in both gene expression and organization, underscores the importance of studying the TCR repertoire with spatial resolution.AcknowledgementsWe are grateful to Irving A. Barrera-Lopez, Zoe N. Garcia, and Aziz Al’Khafaji for technical assistance.ReferencesGohil S, Iorgulescu JB, Braun D, Keskin D, Livak K. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol 2021; 18:244–256.Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Di Bella DJ, Arlotta P, Macosko EZ, Chen F. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol 2021 Mar;39(3):313–319.Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F, Macosko EZ. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019 Mar 29;363(6434):1463–1467.Li S, Sun J, Allesøe R, Datta K, Bao Y, Oliveira G, Forman J, Jin R, Olsen LR, Keskin DB, Shukla SA, Wu CJ, Livak KJ. RNase H-dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis. Nat Protoc 2019 Aug;14(8):2571–2594.Braun DA, Street K, Burke KP, Cookmeyer DL, Denize T, Pedersen CB, Gohil SH, Schindler N, Pomerance L, Hirsch L, Bakouny Z, Hou Y, Forman J, Huang T, Li S, Cui A, Keskin DB, Steinharter J, Bouchard G, Sun M, Pimenta EM, Xu W, Mahoney KM, McGregor BA, Hirsch MS, Chang SL, Livak KJ, McDermott DF, Shukla SA, Olsen LR, Signoretti S, Sharpe AH, Irizarry RA, Choueiri TK, Wu CJ. Progressive immune dysfunction with advancing disease stage in renal cell carcinoma. Cancer Cell 2021 May 10;39(5):632–648.Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, Chen F, Irizarry RA. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 2021 Feb 18. doi: 10.1038/s41587-021-00830-w. Epub ahead of print. PMID: 33603203.Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG, Jenkins RW, Lieb DJ, Chen JH, Frederick DT, Barzily-Rokni M, Freeman SS, Reuben A, Hoover PJ, Villani AC, Ivanova E, Portell A, Lizotte PH, Aref AR, Eliane JP, Hammond MR, Vitzthum H, Blackmon SM, Li B, Gopalakrishnan V, Reddy SM, Cooper ZA, Paweletz CP, Barbie DA, Stemmer-Rachamimov A, Flaherty KT, Wargo JA, Boland GM, Sullivan RJ, Getz G, Hacohen N. Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma. Cell 2018 Nov 1;175(4):998–1013Ethics ApprovalThis study was approved by MGB/DFCI/Broad institution’s Ethics Board; approval number 2019P000017.

AbstractIn this paper two innovative procedures for the decomposition of the Pietra index are proposed. The first one allows the decomposition by sources, while the second one provides the decomposition by subpopulations. As special case of the latter procedure, the “classical” decomposition in two components (within and between) can be easily obtained. A remarkable feature of both the proposed procedures is that they permit the assessment of the contribution to the Pietra index at the smallest possible level: each source for the first one and each subpopulation for the second one. To highlight the usefulness of these procedures, two applications are provided regarding Italian professional football (soccer) teams.

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted. Results We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process, and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data. Availability ASURAT is published on Bioconductor (DOI: 10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) or figshare (https://doi.org/10.6084/m9.figshare.19200254.v3). Supplementary information Supplementary data are available at Bioinformatics online.

Apostichopus japonicas is an economically important species with high nutritional value. However, our knowledge of its genetic diversity and the genetic changes that occurred during its domestication or trait selection is quite limited. In this study, the whole genomes of 254 A. japonicas samples were resequenced. Analyses of the population genetic structure revealed that the genetic diversity of A. japonicas in the north of China is generally high, there was no difference in the population structure among the six cultured populations, and they were divided into two subpopulations together with AY-1 (new species). The results also showed that the genetic diversity of the AY-1 population was relatively low, the degree of linkage of alleles was high, and this population had been subjected to more positive selection. Based on Tajima’s D, FST analysis, and ROD analysis, the selected intervals and genes of the AY-1 population were identified, with some of the candidate intervals being related to an economically important trait and breeding target, namely, the number of parapodia. Gene Ontology analysis of the candidate genes revealed that the two subpopulations differed in their immune function, protein synthesis, decomposition, and transport, among others. Using GWAS, we identified 39 candidate genes for four economically important traits of A. japonicas, and we verified that those genes contained non-synonymous SNPs. Through this verification, BSL78_00022 and BSL78_00023 were found to be key genes for the number of parapodia in A. japonicas. Of these two genes, BSL78_00022 encodes a protein related to cell differentiation and proliferation, so it was assumed that three non-synonymous substitutions (Ser-Phe, Glu-Asp, and Ala-Val) in this gene are related to the changes in the number of parapodia. Meanwhile, the pleiotropic gene BSL78_04631, which is related to body weight and body wall weight, and promotes protein synthesis and cell growth, has a non-synonymous substitution (IlE-Val), which is assumed to be the reason for the difference in body weight and body wall weight of A. japonicas. These results provide a new perspective for explaining the genetic structure characteristics of A. japonicas and analyzing the selection and economically important traits in the breeding of new species.

AbstractNigeria is a high burden country for stunting. Stunting reduction has been slow and characterized by unequal progress across the 36 states and federal capital territory of the country. This study aimed to assess the changes in prevalence of stunting and growth determinants from 2003 to 2018, identify factors that predicted the change in stunting, and project future stunting prevalence if these predicted determinants improve. Trend and linear decomposition analyses of growth outcomes and determinants were conducted using 2003, 2008, 2013, and 2018 Nigeria Demographic and Health Survey data. Pooled data included 57,507 children 0 to 59 months old. Findings show that stunting and severe stunting significantly reduced from 43 to 37% and 23% to 17%, respectively (p < 0.001), between 2003 and 2018. Disturbingly, height-for-age z-scores at birth significantly decreased, indicating risks of potential future stunting increase. Improvements in nine stunting determinants (maternal body mass index, maternal height, ≥ 4 antenatal care visits, health facility delivery, reduced child illnesses, asset index, maternal education, paternal education, and preceding birth interval) predicted stunting reductions in children 0–59 months. Few of these nine determinants improved in subpopulations with limited stunting progress. Intra-sectoral and multisectoral coordination were potentially inadequate; 12% of children had received all of three selected health sector interventions along a continuum of care and 6% had received all of six selected multisector interventions. Forward looking projections suggest that increased efforts to improve the nine predictors of stunting change can reduce under-five stunting in Nigeria to ≤ 27% in the short term.

Abstract Background The healthy immigrant paradox refers to the unexpected health advantages of immigrant groups settled in host countries. In this population-based study we analyze immigrant advantages in birthweight decomposing differences between infants born to immigrant mothers from specific origins. Method Using publicly available data from Spanish Vital Statistics for the period 2007–2017, differential birthweights among several groups of immigrants were estimated with an ordinary least squares regression. The Oaxaca–Blinder regression-based decomposition method was then applied to identify the extent to which differences in birthweight between groups corresponded to compositional disparities or to other factors. Results Our analysis of singleton live births to migrant mothers in Spain between 2007 and 2017 (N = 542,137) confirmed the healthy immigrant paradox for certain immigrant populations settled in Spain. Compared with infants born to mothers from high-income countries, the adjusted birthweight was higher for infants born to mothers from non-high- income European countries (33.2 g, 95% CI: 28.3–38.1, P < 0.01), mothers from African countries (52.2 g, 95% CI: 46.9–57.5, P < 0.01), and mothers from Latin American countries (57.4 g, 95% CI: 52.9–61.3, P < 0.01), but lower for infants born to mothers from Asian non-high-income countries (− 31.4 g, 95% CI: − 38.4 to − 24.3, P < 0.01). Decomposition analysis showed that when compared with infants born to mothers from high-income countries, compositional heterogeneity accounts for a substantial proportion of the difference in birthweights. For example, it accounts for 53.5% (95% CI: 24.0–29.7, P < 0.01) of the difference in birthweights for infants born to mothers from non-high-income European countries, 70.9% (95% CI: 60–66.7, P < 0.01) for those born to mothers from African countries, and 38.5% (95% CI: 26.1–29.3, P < 0.01) for those born to mothers from Latin American countries. Conclusions Our results provide strong population-based evidence for the healthy immigrant paradox in birthweight among certain migrant groups in Spain. However, birth outcomes vary significantly depending on the origins of migrant subpopulations, meaning that not all immigrant groups are unexpectedly healthier. A significant portion of the perinatal health advantage of certain immigrant groups is only a by-product of their group composition (by age, parity, marital status, socioeconomic status, and citizenship of mother, age and migratory status of father and type of delivery) and does not necessarily correspond to other medical, environmental, or behavioral factors.

ABSTRACT Flavobacterium psychrophilum affects salmonid health worldwide and causes economic losses. The genetic diversity of the pathogen must be considered to develop control methods. However, previous studies have reported both high and low levels of genetic diversity. The present longitudinal study aimed at assessing the genetic diversity of F. psychrophilum at a small temporal and geographic scale. Four farms located on the same watershed in France were studied. Rainbow trout (Oncorhynchus mykiss) batches were monitored, and apparently healthy individuals were sampled over 1 year. A total of 288 isolates were recovered from fish organs (gills and spleen) and eggs. Pulsed field gel electrophoresis revealed high genetic diversity. Multilocus sequence typing performed on a selection of 31 isolates provided congruent results, as follows: 18 sequence types (STs) were found, of which 13 were novel. The mean gene diversity (H = 0.8413) was much higher than that previously reported for this host species, although the sampling was restricted to a single watershed and 1 year. Seven isolates out of 31 were assigned to clonal complex ST10 (CC-ST10), which is the predominant clonal complex in the main salmonid production areas. A split decomposition tree reflected a panmictic population. This finding is important for aquaculture veterinarians in their diagnostic procedure, as the choice of adequate antibiotic treatment is conditioned by the correct identification of the causative agent. Furthermore, this study expands our knowledge on genetic diversity required for the development of an effective vaccine against F. psychrophilum. IMPORTANCE The bacterium Flavobacterium psychrophilum is a serious pathogen in many fish species, especially salmonids, that is responsible for considerable economic losses worldwide. In order to treat infections and to develop vaccines, the genetic diversity of this bacterium needs to be known. We assessed the genetic diversity of F. psychrophilum isolates from apparently healthy rainbow trout raised in several fish farms in the same watershed in France. Two different genotyping methods revealed high diversity. The majority of isolates were unrelated to clonal complex sequence type 10 (CC-ST10), the clonal complex that is predominant worldwide and associated with disease in rainbow trout. In addition, we found 13 novel sequence types. These results suggest that a diverse subpopulation of F. psychrophilum may be harbored by rainbow trout.