Conflicted Emotions Following Trust-based Interaction

We investigated whether 20 emotional states, reported by 170 participants after participating in a Trust game, were experienced in a patterned way predicted by the “Recalibrational Model” or Valence Models. According to the Recalibrational Model, new information about trust-based interaction outcomes triggers specific sets of emotions. Unlike Valence Models that predict reports of large sets of either positive or negative emotional states, the Recalibrational Model predicts the possibility of conflicted (concurrent positive and negative) emotional states. Consistent with the Recalibrational Model, we observed reports of conflicted emotional states activated after interactions where trust was demonstrated but trustworthiness was not. We discuss the implications of having conflicted goals and conflicted emotional states for both scientific and well-being pursuits

Complete Genome Sequence of the N2-Fixing Broad Host Range Endophyte Klebsiella pneumoniae 342 and Virulence Predictions Verified in Mice

We report here the sequencing and analysis of the genome of the nitrogen-fixing endophyte, Klebsiella pneumoniae 342. Although K. pneumoniae 342 is a member of the enteric bacteria, it serves as a model for studies of endophytic, plant-bacterial associations due to its efficient colonization of plant tissues (including maize and wheat, two of the most important crops in the world), while maintaining a mutualistic relationship that encompasses supplying organic nitrogen to the host plant. Genomic analysis examined K. pneumoniae 342 for the presence of previously identified genes from other bacteria involved in colonization of, or growth in, plants. From this set, approximately one-third were identified in K. pneumoniae 342, suggesting additional factors most likely contribute to its endophytic lifestyle. Comparative genome analyses were used to provide new insights into this question. Results included the identification of metabolic pathways and other features devoted to processing plant-derived cellulosic and aromatic compounds, and a robust complement of transport genes (15.4%), one of the highest percentages in bacterial genomes sequenced. Although virulence and antibiotic resistance genes were predicted, experiments conducted using mouse models showed pathogenicity to be attenuated in this strain. Comparative genomic analyses with the presumed human pathogen K. pneumoniae MGH78578 revealed that MGH78578 apparently cannot fix nitrogen, and the distribution of genes essential to surface attachment, secretion, transport, and regulation and signaling varied between each genome, which may indicate critical divergences between the strains that influence their preferred host ranges and lifestyles (endophytic plant associations for K. pneumoniae 342 and presumably human pathogenesis for MGH78578). Little genome information is available concerning endophytic bacteria. The K. pneumoniae 342 genome will drive new research into this less-understood, but important category of bacterial-plant host relationships, which could ultimately enhance growth and nutrition of important agricultural crops and development of plant-derived products and biofuels

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

TaxCollector: Modifying Current 16S rRNA Databases for the Rapid Classification at Six Taxonomic Levels

The high level of conservation of 16S ribosomal RNA gene (16S rRNA) in all Prokaryotes makes this gene an ideal tool for the rapid identification and classification of these microorganisms. Databases such as the Ribosomal Database Project II (RDP-II) and the Greengenes Project offer access to sets of ribosomal RNA sequence databases useful in identification of microbes in a culture-independent analysis of microbial communities. However, these databases do not contain all of the taxonomic levels attached to the published names of the bacterial and archaeal sequences. TaxCollector is a set of scripts developed in Python language that attaches taxonomic information to all 16S rRNA sequences in the RDP-II and Greengenes databases. These modified databases are referred to as TaxCollector databases, which when used in conjunction with BLAST allow for rapid classification of sequences from any environmental or clinical source at six different taxonomic levels, from domain to species. The TaxCollector database prepared from the RDP-II database is an important component of a new 16S rRNA pipeline called PANGEA. The usefulness of TaxCollector databases is demonstrated with two very different datasets obtained using samples from a clinical setting and an agricultural soil. The six TaxCollector scripts are freely available on http://taxcollector.sourceforge.net and on http://www.microgator.org

Inhibition of hepatitis B virus replication in vivo using lipoplexes containing altritol-modified antiviral siRNAs

Chronic infection with the hepatitis B virus (HBV) occurs in approximately 6% of the world's population and carriers of the virus are at risk for complicating hepatocellular carcinoma. Current treatment options have limited efficacy and chronic HBV infection is likely to remain a significant global medical problem for many years to come. Silencing HBV gene expression by harnessing RNA interference (RNAi) presents an attractive option for development of novel and effective anti HBV agents. However, despite significant and rapid progress, further refinement of existing technologies is necessary before clinical application of RNAi-based HBV therapies is realized. Limiting off target effects, improvement of delivery efficiency, dose regulation and preventing reactivation of viral replication are some of the hurdles that need to be overcome. To address this, we assessed the usefulness of the recently described class of altritol-containing synthetic siRNAs (ANA siRNAs), which were administered as lipoplexes and tested in vivo in a stringent HBV transgenic mouse model. Our observations show that ANA siRNAs are capable of silencing of HBV replication in vivo. Importantly, non specific immunostimulation was observed with unmodified siRNAs and this undesirable effect was significantly attenuated by ANA modification. Inhibition of HBV replication of approximately 50% was achieved without evidence for induction of toxicity. These results augur well for future application of ANA siRNA therapeutic lipoplexes

PANGEA: pipeline for analysis of next generation amplicons

High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including preprocessing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the χ(2) step, are joined into one program called the ‘backbone’