NGC 7789: An Open Cluster Case Study

We have obtained high-resolution spectra of 32 giants in the open cluster NGC 7789 using the Wisconsin-Indiana-Yale-NOAO Hydra spectrograph. We explore differences in atmospheric parameters and elemental abundances caused by the use of the linelist developed for the Gaia-ESO Survey (GES) compared to one based on Arcturus used in our previous work. [Fe/H] values decrease when using the GES linelist instead of the Arcturus-based linelist; these differences are probably driven by systematically lower (~ -0.1 dex) GES surface gravities. Using the GES linelist we determine abundances for 10 elements - Fe, Mg, Si, Ca, Ti, Na, Ni, Zr, Ba, and La. We find the cluster's average metallicity [Fe/H] = 0.03 +/- 0.07 dex, in good agreement with literature values, and a lower [Mg/Fe] abundance than has been reported before for this cluster (0.11 +/- 0.05 dex). We also find the neutron-capture element barium to be highly enhanced - [Ba/Fe] = +0.48 +/- 0.08 - and disparate from cluster measurements of neutron-capture elements La and Zr (-0.08 +/- 0.05 and 0.08 +/- 0.08, respectively). This is in accordance with recent discoveries of supersolar Ba enhancement in young clusters along with more modest enhancement of other neutron-capture elements formed in similar environments.Comment: 15 pages, 9 figures, Table 1 typo fixe

GCView: the genomic context viewer for protein homology searches

Genomic neighborhood can provide important insights into evolution and function of a protein or gene. When looking at operons, changes in operon structure and composition can only be revealed by looking at the operon as a whole. To facilitate the analysis of the genomic context of a query in multiple organisms we have developed Genomic Context Viewer (GCView). GCView accepts results from one or multiple protein homology searches such as BLASTp as input. For each hit, the neighboring protein-coding genes are extracted, the regions of homology are labeled for each input and the results are presented as a clear, interactive graphical output. It is also possible to add more searches to iteratively refine the output. GCView groups outputs by the hits for different proteins. This allows for easy comparison of different operon compositions and structures. The tool is embedded in the framework of the Bioinformatics Toolkit of the Max-Planck Institute for Developmental Biology (MPI Toolkit). Job results from the homology search tools inside the MPI Toolkit can be forwarded to GCView and results can be subsequently analyzed by sequence analysis tools. Results are stored online, allowing for later reinspection. GCView is freely available at http://toolkit.tuebingen.mpg.de/gcview

Does child abuse predict a population segment with large economic burden?

Objectives: The enormous societal and individual consequences of mental health disorders and detrimental health behaviours in the general population are of paramount concern. Many argue that ‘prevention is the best cure’, pushing for the implementation of early (preventive) interventions. Key questions regarding early interventions include which population segment to target for screenings and what information these screenings should focus on. In line with previous efforts, this study aimed to identify which population segment holds the majority (≥ 80 %) of different economically costly outcomes in society, and whether child abuse before the age of 16 years predicts being part of that population segment. Study design: Epidemiological cohort study. Methods: This study used the Netherlands Mental Health Survey and Incidence Study-2, a Dutch epidemiological cohort study including 6646 adults aged 18–64 years at baseline, spanning four timepoints from 2007 to 2018. Cumulative distributions were computed to identify high-cost population segments of economically costly outcomes in adulthood (i.e., mental and physical health [behaviours], unemployment and work absenteeism). Child abuse was examined as a potential predictor of these segments and the risk of multiple high-cost population segment membership was investigated by conducting Poisson regressions. Results: A 20 % population segment carried between 42 % and 100 % of economically costly outcomes. Being exposed to more child abuse predicted being in a high-cost population segment, albeit with small effect sizes. Being exposed to more child abuse also predicted belonging to multiple high-cost population segments across different economically costly outcomes. Conclusions: The study findings have implications for policy makers. Emphasis should be placed on prevention aimed at identifying potential members of multiple high-cost population segments.</p

Enabling comparative modeling of closely related genomes: Example genus Brucella

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as well as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.We thank Jean Jacques Letesson, Maite Iriarte, Stephan Kohler and David O'Callaghan for their input on improving specific annotations. This project has been funded by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN272200900040C, awarded to BW Sobral, and from the United States National Science Foundation under Grant MCB-1153357, awarded to CS Henry. J.P.F. acknowledges funding from [FRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) Ph.D. scholarship

Self-reported nature exposure and its association with well-being as measured with affect and cognition

Utilizing the publicly available MIDUS II Refresher datasets (Ryff et al., 2017) with hundreds of respondents across the United States, the authors attempted to (1) replicate and (2) extend their previous findings with the original MIDUS II data on the relationship between self-reported frequency of nature exposure and well-being, the latter holistically measured by emotional, physiological and cognitive variables (Craig, Menon, &amp; Klein, 2015; Craig, Neilson, &amp; Overbeek, 2016).In the original published research, Craig and colleagues (2015) first observed an association between a 3-pt scale in which middle-aged participants reported the frequency that they appreciated nature and other reported questionnaire measures. These measures included subscales of the Mood Affective Symptoms Questionnaire (MASQ), the Perceived Stress Scale, and scales measuring well-being constructs such as life satisfaction and gratitude. This was followed up with a second study, which found an observed relationship between reported nature exposure with a 7-pt scale and measured physiological variables relevant to emotion and cognition, specifically asymmetrical EEG and eye-blink startle response (Craig et al., 2016). However, the prior research was exploratory and correlational in nature, which many would argue necessitates replication.The original MIDUS II datasets used in the previous investigations (Ryff &amp; Davidson, 2010; Ryff, Seeman, &amp; Weinstein, 2010) were recollected by the original team with a new cohort (Ryff et al., 2017), allowing for a nearly direct replication of the previous analyses (Craig et al., 2015; 2016). Because positive effects associated with nature exposure may be a function of both exposure frequency and degree of appreciation, the first set of analyses replicating and extending the results of Craig and colleagues (2015) used an averaged composite score of two 3-pt scale questions measuring both frequency and degree of nature appreciation, instead of only frequency of nature appreciation as conducted in the original analysis.  The second set of analyses that attempted to replicate Craig and colleagues (2016) used the original 7-pt nature exposure scale.For the replication (goal 1), controlling for factors such as age, gender, exercise, and education, multiple regression analyses with the new datasets replicated the association between nature exposure and positive emotions, perceived stress, and metrics such as gratitude and perception of work value. However, there were mixed results for depressive affect, and the previously observed correspondence between nature exposure and emotional reactivity measures, such as eyeblink startle response and epinephrine, did not replicate.For extending the original research (goal 2), exploratory analyses were conducted to explore (1) previously unanalyzed variables related to well-being, and (2) previously unanalyzed cognitive variables. There was an observed and potentially beneficial relationship between self-reported nature exposure, sleep quality, self-control, and low-frequency (.04 - .15 Hz) heart-rate variability. A follow-up analysis focusing on cognitive test batteries including the CANTAB and BTACT mostly did not observe any associations between self-reported frequency of nature exposure and cognitive performance. However, a tentative relationship was noted between nature exposure and category fluency, which should be tested with future research.To clearly demonstrate the effects of nature on general well-being, an exploratory principle components analysis was conducted on 18 measures presently observed to be significantly associated with nature exposure, with a varimax rotation and the extraction based on the Kaiser criterion. Of five identified factors, one appeared to capture a construct akin to well-being (e.g., positive affect, reduced stress, gratitude, cognitive control, anger management, sleep score, work value). Therefore, a single well-being composite variable was computed (regression-weighted) based on the observed factor loadings after standardizing the component variables. A regression of the well-being composite score on the standardized nature exposure composite score (n = 788) was found to be significant, R2 = .095, F(1,786) = 82.81, p &lt; .001.One of the limitations of this study is nature exposure was measured with a single variable, and the type of exposure (nature trails, window scenery) and type of nature (green vs. blue nature) was not explored. Also, the current analysis looked at a large set of survey data and produced relatively small effect sizes, which is understandable given the large number of potentially intervening variables and the imprecision of the survey measurements. Further, the findings here are correlational and precise design recommendations are not warranted, but there may be several avenues to implement nature in and around built spaces. With careful design, even urban scenery designed with components akin to nature could be helpful in improving well-being. Future research could assess whether the amount of time in nature may lead to greater improvement

Development and validation of the Religious Collective Self-Esteem scale for children

This study aimed to validate a Religious Collective Self-Esteem Scale (RCSES) that assesses children’s evaluations and judgments about their belonging to a religious group. The RCSES includes 3 subscales: Private Religious Self-Esteem (PrRSE), Public Religious Self-Esteem (PuRSE), and Importance to Religious Identity (RI). Data were gathered from students in 39 primary schools (9 Reformed Protestant, 9 Islamic, 3 Hindu and 18 public schools) across five regions in the Netherlands. Students were asked to complete an anonymous questionnaire containing measures of variables of interest. Subjects were 1,437 6th graders (Mage = 11.72, SD = 0.61; 51.7% girls. 680 Students identified themselves as Muslim (47.3%), 442 (30.8%) as Christian, 278 (19.3%) as Hindu, and 37 (2.6%) children had another religion. Results indicated sufficient internal consistency of RCSES (α = .80), PrRSE (α = .77), PuRSE (α = .73), and RI (α = .60), moderate to high correlations between the subscales and moderate to large test–retest reliability across 1 year (r = .57). Three-factor model fitted the best. Overall, findings support partial measurement and structural invariance across religious groups. Convergent validity was supported by small to moderate correlations with other scales (Individual Self-Esteem Scale, r = .29; Private Ethnic Self-Esteem Scale (PESES), r = .40). Divergent validity was supported by positive small significant correlations with school well-being (r = .18) and social school motivation (r = .19). RCSES and its subscales significantly predicted, over and above PESES, school well-being and school motivation scores. Findings support the reliability and validity of the RCSES for assessing religious collective self-esteem. (PsycINFO Database Record (c) 2017 APA, all rights reserved

Reconciling gene expression data with regulatory network models

The reconstruction of genome-scale metabolic models from genome annotations has become a routine practice in Systems Biology research. The potential of metabolic models for predictive biology is widely accepted by the scientific community, but these same models still lack the capability to account for the effect of gene regulation on metabolic activity. Our focus organism, Bacillus subtilis is most commonly found in soil, being subject to a wide variety of external environmental conditions. This reinforces the importance of the regulatory mechanisms that allow the bacteria to survive and adapt to such conditions. We introduce a manually curated regulatory network for Bacillus subtilis, tapping into the notable resources for B. subtilis regulation. We propose the concept of Atomic Regulon, as a set of genes that share the same ON and OFF gene expression profile across multiple samples of experimental data. Atomic regulon inference uses prior knowledge from curated SEED subsystems, in addition to expression data to infer regulatory interactions. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand/ validate the knowledge of the regulatory networks and gain insights into novel biology

Reconciling gene expression data with regulatory network models – a stimulon-based approach for integrated metabolic and regulatory modeling of Bacillus subtilis

The reconstruction of genome-scale metabolic models from genome annotations has become a routine practice in Systems Biology research. The potential of metabolic models for predictive biology is widely accepted by the scientific community, but these same models still lack the capability to account for the effect of gene regulation on metabolic activity. Our focus organism, Bacillus subtilis is most commonly found in soil, being subject to a wide variety of external environmental conditions. This reinforces the importance of the regulatory mechanisms that allow the bacteria to survive and adapt to such conditions. Existing integrated metabolic regulatory models are currently available for only a small number of well-known organisms (e.g E. coli and B. subtilis). The E. coli integrated model was proposed by Covert et al in 2004 and has slowly improved over the years. Goelzer et al. introduced the B. subtilis integrated model in 2008, covering only the central metabolic pathways. Different strategies were used in the two modeling efforts. The E. coli model is defined by a set of Boolean rules (turning genes ON and OFF) accounting mostly for transcription factors, gene interactions, involved metabolites, and some external conditions such as heat shock. The B. subtilis model introduces a set of more complex rules and also incorporates sigma factor activity into the modeling abstraction. Here we propose a genome-scale model for the regulatory network of B. subtilis, using a new stimulon-based approach. A stimulon is defined as the set of genes (that can be a part of the same operon(s) and regulon(s)) that respond in the same set of stimuli. The proposed stimulon-based approach allows for the inclusion of more types of regulation in the model. This methodology also abstracts away much of the complexity of regulatory mechanisms by directly connecting the activity of genes to the presence or absence of associated stimuli, a necessity in the many cases where details of regulatory mechanisms are poorly understood. Our model integrates regulatory network data from the Goelzer et al model, in addition to other available literature data. We then reconciled our model against a large set of high-quality gene expression data (tiled microarrays for 104 different conditions). The stimulons in our model were split or extended to improve consistency with our expression data, and the stimuli in our model were adjusted to improve consistency with the conditions of our expression experiments. The reconciliation with gene expression data revealed a significant number of exact or nearly exact matches between the manually curated regulons/stimulons and pure correlation-based regulons. Our reconciliation analysis of the 2011 SubtiWiki regulon release suggested many gene candidates for regulon extension that were subsequently included in the 2013 SubtiWiki update. Our enhanced model also includes an improved coverage of a wide range of different stress conditions. We then integrated our regulatory model with the latest metabolic reconstruction for B. subtilis, the iBsu1103V2 model (Tanaka et al. 2012). We applied this integrated metabolic regulatory model to the simulation of all growth phenotype data currently available for B. subtilis, demonstrating how the addition of regulatory constraints improved consistency of model predictions with experimentally observed phenotype data. This analysis of growth phenotype data unveiled phenotypes that could only be characterized with the addition of regulatory network constraints. All tools applied in the reconstruction, simulation, and curation of our new regulatory model are now publicly available as a part of the KBase framework. These tools permit the direct simulation of gene expression data using the regulon model alone, as well as the simulation of phenotypes and growth conditions using an integrated metabolic and regulatory model. We will highlight these new tools in the context of our reconstruction and analysis of the B. subtilis regulatory model