False Memory for Trauma-Related DRM Lists in Adolescents and Adults with Histories of Child Sexual Abuse

The purpose of the present research was to examine Deese-Roediger-McDermott (DRM) false memory for trauma-related and nontrauma-related lists in adolescents and adults with and without documented histories of child sexual abuse (CSA). Individual differences in psychopathology and adult attachment were also explored. Participants were administered free recall and recognition tests after hearing CSA, negative, neutral, and positive DRM lists. In free recall, CSA and negative lists produced the most false memory. In sharp contrast, for recognition, CSA lists enjoyed the highest d’ scores. CSA-group adolescents who evinced greater PTSD symptoms had higher rates of false memory compared to: 1) nonCSA-group adolescents with higher PTSD symptom scores (free recall), and 2) CSA-group adolescents with lower PTSD symptom scores (recognition). Regression analyses revealed that individuals with higher PTSD scores and greater fearful-avoidant attachment tendencies showed less proficient memory monitoring for CSA lists. Implications for trauma and memory development and for translational research are discussed

What's in an education? Implications of CEO education for bank performance

Exploiting a unique hand-built dataset, this paper finds that CEO educational attainment, both level and quality, matters for bank performance. We offer robust evidence that banks led by CEOs with MBAs outperform their peers. Such CEOs improve performance when compensation structures are geared towards greater risk-taking incentives, and when banks follow riskier or more innovative business models. Our findings suggest that management education delivers skills enabling CEOs to manage increasingly larger and complex banking firms and achieve successful performance outcomes

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations

MEMOTE for standardized genome-scale metabolic model testing

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-yReconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).info:eu-repo/semantics/publishedVersio

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

An amendment to this paper has been published and can be accessed via a link at the top of the paper.(undefined)info:eu-repo/semantics/publishedVersio

Phytotoxicity indexes and removal of color, COD, phenols and ISA from pulp and paper mill wastewater post-treated by UV/H2O2 and photo-Fenton

Pulp and paper mill wastewater (PPMWW) contains high concentrations of recalcitrant compounds that cause toxicity to organisms. Advanced oxidation processes (AOPs) have the ability to degrade these compounds and reduce overall toxicity. Physicochemical characterization and Lactuca sativa toxicity test were conducted to compare the effectiveness of two post-treatments: UV/H2O2 and photo-Fenton. A comparison of four phytotoxicity indexes was carried out. PPMWW from a Brazilian treatment plant was characterized by high values of phenols, color, integrated spectral area (ISA), and chemical oxygen demand (COD), and caused significant inhibition to seedling development. The use of both post-treatments allowed the removal of over 75% of phenols, color, ISA, and COD. Although UV/H2O2 was more effective in removing phenols and ISA, photo-Fenton better reduced phytotoxicity. The most sensitive phytotoxicity indexes were RGIC0.8 and GIC80%, whereas SGC0, REC-0.25 and REC-0.50 better showed the effectiveness of the post-treatments. We suggest the combined use of two phytotoxicity indexes: one that evaluates the effects on seed germination and, another, on root elongation, e.g., SGC0 and RGIC0.8. Additionally, we recommend the use of ISA for monitoring programs of wastewater treatments because it is a cost-effective approach that allows narrowing down the search and identification of compounds present in complex mixtures.Instituto de Microbiología y Zoología Agrícola (IMYZA)Fil: Carvalho Neves, Ludmila. Universidade Estadual do Centro Oeste do Paraná; BrasilFil: Beber de Souza, Jeanette. Universidade Estadual do Centro Oeste do Paraná; BrasilFil: de Souza Vidal, Carlos Magno. Universidade Estadual do Centro Oeste do Paraná; BrasilFil: Herbert, Lucila Thomsett. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente. Centro de Ecología Aplicada del Neuquén (CEAN); ArgentinaFil: de Souza Vidal, Kely Viviane. Universidade Estadual do Centro Oeste do Paraná; BrasilFil: Martins, Kely Geronazzo. Universidade Estadual do Centro Oeste do Paraná; BrasilFil: Young, Brian Jonathan. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Microbiología y Zoología Agrícola; Argentin

BASSOONarama 2009

https://dc.ewu.edu/music_performances/1725/thumbnail.jp