Asc-Seurat: analytical single-cell Seurat-based web application.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat's capabilities by analyzing a peripheral blood mononuclear cell dataset. CONCLUSIONS: Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

The contribution of dominance to phenotype prediction in a pine breeding and simulated population.

Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.

Co-responding Police-Mental Health Programs: A Review.

Co-responding police-mental health programs are increasingly used to respond to 'Emotionally Disturbed Persons' in the community; however, there is limited understanding of program effectiveness and the mechanisms that promote program success. The academic and gray literature on co-responding police-mental health programs was reviewed. This review synthesized evidence of outcomes along seven dimensions, and the available evidence was further reviewed to identify potential mechanisms of program success. Co-responding police-mental health programs were found to have strong linkages with community services and reduce pressure on the justice system, but there is limited evidence on other impacts. The relevance of these findings for practitioners and the major challenges of this program model are discussed, and future research directions are identified.

Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression-best linear unbiased prediction (RR-BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR-BLUB B had higher predictive ability than RR-BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments.

• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53-112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.

Robust linear regression methods in association studies.

MOTIVATION: It is well known that data deficiencies, such as coding/rounding errors, outliers or missing values, may lead to misleading results for many statistical methods. Robust statistical methods are designed to accommodate certain types of those deficiencies, allowing for reliable results under various conditions. We analyze the case of statistical tests to detect associations between genomic individual variations (SNP) and quantitative traits when deviations from the normality assumption are observed. We consider the classical analysis of variance tests for the parameters of the appropriate linear model and a robust version of those tests based on M-regression. We then compare their empirical power and level using simulated data with several degrees of contamination. RESULTS: Data normality is nothing but a mathematical convenience. In practice, experiments usually yield data with non-conforming observations. In the presence of this type of data, classical least squares statistical methods perform poorly, giving biased estimates, raising the number of spurious associations and often failing to detect true ones. We show through a simulation study and a real data example, that the robust methodology can be more powerful and thus more adequate for association studies than the classical approach. AVAILABILITY: The code of the robustified version of function lmekin() from the R package kinship is provided as Supplementary Material.

Effects of a spaceflight environment on heritable changes in wheat gene expression.

Once it was established that the spaceflight environment was not a drastic impediment to plant growth, a remaining space biology question was whether long-term spaceflight exposure could cause changes in subsequent generations, even if they were returned to a normal Earth environment. In this study, we used a genomic approach to address this question. We tested whether changes in gene expression patterns occur in wheat plants that are several generations removed from growth in space, compared to wheat plants with no spaceflight exposure in their lineage. Wheat flown on Mir for 167 days in 1991 formed viable seeds back on Earth. These seeds were grown on the ground for three additional generations. Gene expression of fourth-generation Mir flight leaves was compared to that of the control leaves by using custom-made wheat microarrays. The data were evaluated using analysis of variance, and transcript abundance of each gene was contrasted among samples with t-tests. After corrections were made for multiple tests, none of the wheat genes represented on the microarrays showed a statistically significant difference in expression between wheat that has spaceflight exposure in their lineage and plants with no spaceflight exposure. This suggests that exposure to the spaceflight environment in low Earth orbit space stations does not cause significant, heritable changes in gene expression patterns in plants.

Genomics, domestication, and evolution of forest trees.

The forests of the world continue to be threatened by climate change, population growth, and loss to agriculture. Our ability to conserve natural forests and to meet the increasing demand for fuel, biomass, wood, and paper depends on our fundamental understanding of tree growth and adaptation (FAO 2001; Fenning and Gershenzon 2002; Campbell et al. 2003; Gray et al. 2006). Our knowledge of the unique biology of trees will be greatly advanced through the application of genomics. The purpose of this chapter is to describe this emergent genomic paradigm as it is being applied to trees.

Power of microsatellite markers for fingerprinting and parentage analysis in Eucalyptus grandis breeding populations.

We report the genetic analysis of 192 unrelated individuals of an elite breeding population of Eucalyptus grandis (Hill ex Maiden) with a selected set of six highly polymorphic microsatellite markers developed for species of the genus Eucalyptus. A full characterization of this set of six loci was carried out generating allele frequency distributions that were used to estimate parameters of genetic information content of these loci, including expected heterozygosity, polymorphism information content (PIC), power of exclusion, and probability of identity. The number of detected alleles per locus ranged from 6 to 33, with an average of 19.8 +/- 9.2. The average expected heterozygosity was 0.86 +/- 0.11 and the average PIC was 0.83 +/- 0.16. Using only three loci, it was possible to discriminate all 192 individuals. The overall probability of identity considering all six EMBRA microsatellite markers combined was lower than 1 in 2 billion. An analysis of the sample size necessary to estimate expected heterozygosity with minimum variance indicated that at least 64 individuals have to be genotyped to characterize this parameter with adequate accuracy for most microsatellites in Eucalyptus. The high degree of multiallelism and the clear and simple codominant Mendelian inheritance of the set of microsatellites used provide an extremely powerful system for the unique identification of Eucalyptus individuals for fingerprinting purposes and parentage testing.

Gene expression during formation of earlywood and latewood in loblolly pine: expression profiles of 350 genes.

The natural variability of wood formation in trees affords opportunities to correlate transcript profiles with the resulting wood properties. We have used cDNA microarrays to study transcript abundance in developing secondary xylem of loblolly pine (Pinus taeda) over a growing season. The cDNAs were selected from a collection of 75 000 ESTs that have been sequenced and annotated (http://web.ahc.umn.edu/biodata/nsfpine/). Cell wall thickness and climatic data were related to earlywood and latewood formation at different time points during the growing season. Seventy-one ESTs showed preferential expression in earlywood or latewood, including 23 genes with no significant similarity to genes in GenBank. Seven genes involved in lignin synthesis were preferentially expressed in latewood. The studies have provided initial insights into the variation of expression patterns of some of the genes related to the wood formation process.

Mobile patient simulator for resuscitation training with automatic external defibrillators.

Resuscitation training has to be performed under most realistic conditions. This includes both usual CPR measures (breathing and chest compressions) and advanced measures, e.g. the usage of an Automatic External Defibrillator (AED). Almost all currently available simulators for ECG signals used in such trainings have a rather limited variety of available ECG signals. The trainer also has to change between different rhythms manually, resulting in a less realistic training environment. The development will result in mobile ECG simulator which can automatically react to events in the resuscitation process according to pre-programmed scenarios. It also has potential to simulate other physiological parameters like thorax impedance in the future.

State services for children: an exploration of who benefits, who governs.

In recent years, equity, choice, and efficiency issues in the provision of education have received much attention and analysis. Yet, in the area of other state services for children (health, protective services, day care, etc.), there has been scant concern for equity, efficiency, or choice, despite the fact that out-of-school influences can be crucial in determining in-school performance. This paper reports on work in progress that reaches the following initial conclusions: 1. In the field of children's social services, data compilation is approximately 20 years behind the state of the art for education. Basic data on services provided is not collected for submission to state or federal authorities in any standard format on a recurring basis. 2. From the limited data available, the access of children to quantity and quality in social service programs varies enormously within states. The variations are much larger than those discovered in the public financing of education even before the recent school finance reform movement (1968-1978). 3. Federal allocations to states for social service programs comprise a substantial proportion of state Title XX budgets. For several reasons, the degree of accountability for these funds is less than that for federal education grants. 4. In all three states Title XX state allocations to localities are purported to be based on need. Closer examination of the formulas, and interviews with policy makers, revealed that Title XX allocations are determined primarily by political criteria.