Composition of the Adult Digestive Tract Bacterial Microbiome Based on Seven Mouth Surfaces, Tonsils, Throat and Stool Samples

Background: To understand the relationship between our bacterial microbiome and health, it is essential to define the microbiome in the absence of disease. The digestive tract includes diverse habitats and hosts the human body's greatest bacterial density. We describe the bacterial community composition of ten digestive tract sites from more than 200 normal adults enrolled in the Human Microbiome Project, and metagenomically determined metabolic potentials of four representative sites. Results: The microbiota of these diverse habitats formed four groups based on similar community compositions: buccal mucosa, keratinized gingiva, hard palate; saliva, tongue, tonsils, throat; sub- and supra-gingival plaques; and stool. Phyla initially identified from environmental samples were detected throughout this population, primarily TM7, SR1, and Synergistetes. Genera with pathogenic members were well-represented among this disease-free cohort. Tooth-associated communities were distinct, but not entirely dissimilar, from other oral surfaces. The Porphyromonadaceae, Veillonellaceae and Lachnospiraceae families were common to all sites, but the distributions of their genera varied significantly. Most metabolic processes were distributed widely throughout the digestive tract microbiota, with variations in metagenomic abundance between body habitats. These included shifts in sugar transporter types between the supragingival plaque, other oral surfaces, and stool; hydrogen and hydrogen sulfide production were also differentially distributed. Conclusions: The microbiomes of ten digestive tract sites separated into four types based on composition. A core set of metabolic pathways was present across these diverse digestive tract habitats. These data provide a critical baseline for future studies investigating local and systemic diseases affecting human health

Offspring ADHD as a risk factor for parental marital problems: Controls for genetic and environmental confounds

Background: Previous studies have found that child attention-deficit/hyperactivity disorder (ADHD) is associated with more parental marital problems. However, the reasons for this association are unclear. The association might be due to genetic or environmental confounds that contribute to both marital problems and ADHD. Method: Data were drawn from the Australian Twin Registry, including 1,296 individual twins, their spouses, and offspring. We studied adult twins who were discordant for offspring ADHD. Using a discordant twin pairs design, we examined the extent to which genetic and environmental confounds, as well as measured parental and offspring characteristics, explain the ADHD-marital problems association. Results: Offspring ADHD predicted parental divorce and marital conflict. The associations were also robust when comparing differentially exposed identical twins to control for unmeasured genetic and environmental factors, when controlling for measured maternal and paternal psychopathology, when restricting the sample based on timing of parental divorce and ADHD onset, and when controlling for other forms of offspring psychopathology. Each of these controls rules out alternative explanations for the association. Conclusion: The results of the current study converge with those of prior research in suggesting that factors directly associated with offspring ADHD increase parental marital problems

OSSMETER: Automated measurement and analysis of open source software

International audienceDeciding whether an open source software (OSS) meets the requiredstandards for adoption in terms of quality, maturity, activity of development anduser support is not a straightforward process. It involves analysing various sourcesof information, including the project’s source code repositories, communicationchannels, and bug tracking systems. OSSMETER extends state-of-the-art techniquesin the field of automated analysis and measurement of open-source software(OSS), and develops a platform that supports decision makers in the processof discovering, comparing, assessing and monitoring the health, quality, impactand activity of opensource software. To achieve this, OSSMETER computestrustworthy quality indicators by performing advanced analysis and integrationof information from diverse sources including the project metadata, source coderepositories, communication channels and bug tracking systems of OSS projects

Mental defeat and suicidality in chronic pain : a prospective analysis

Living with chronic pain has been identified as a significant risk factor for suicide. Qualitative and cross-sectional studies have reported an association between mental defeat and suicidal thoughts and behavior in patients with chronic pain. In this prospective cohort study, we hypothesized that higher levels of mental defeat would be associated with increased suicide risk at a 6-month follow-up. A total of 524 patients with chronic pain completed online questionnaires measuring variables related to suicide risk, mental defeat, sociodemographic, psychological, pain, activity, and health variables. At 6 months, 70.8% (n=371) of respondents completed the questionnaires again. Weighted univariate and multivariable regression models were run to predict suicide risk at 6 months. The clinical suicide risk cutoff was met by 38.55% of participants at baseline and 36.66% at 6 months. Multivariable modeling revealed that mental defeat, depression, perceived stress, head pain, and active smoking status significantly increased the odds of reporting higher suicide risk, while older age reduced the odds. Receiver operating characteristic (ROC) analysis showed that assessment of mental defeat, perceived stress, and depression is effective in discriminating between ‘low’ and ‘high’ suicide risk. Awareness of the prospective links from mental defeat, depression, perceived stress, head pain, and active smoking status to increased suicide risk in patients with chronic pain may offer a novel avenue for assessment and preventative intervention

Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used

An Experimental Analysis Of the Demand For Payday Loans

The payday loan industry is one of the fastest growing segments of the consumer financial services market in the United States. We design an environment similar to the one that payday loan customers face and then conduct a laboratory experiment to examine what effect, if any, the existence of payday loans has on individuals\u27 abilities to manage and to survive financial setbacks. Our primary objective is to examine whether access to payday loans improves or worsens the likelihood of financial survival in our experiment. We also test the degree to which people\u27s use of payday loans affects their ability to survive financially. We find that payday loans help the subjects to absorb expenditure shocks and therefore survive financially. However, subjects whose demand for payday loans exceeds a certain threshold level are at a greater risk than a corresponding subject in the treatment in which payday loans do not exist

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)