Time-Shifting vs. Appointment Viewing: The Role of Fear of Missing Out Within TV Consumption Behaviors

The current study employed a national sample in order to investigate the phenomenon of fear-of-missing-out (FoMO), the apprehension associated with the fear that other people are having a pleasurable experience that one is not a part of. The current study investigated the role that FoMO plays in TV viewing habits, particularly binge-watching and the consumption of one-time megaevents. Results indicated that FoMO predicts the pace at which people choose to watch TV, social media use as it relates to TV, and whether they are likely to watch some one-time TV programs—such as sporting events like the Super Bowl

Comparing American soccer dialogues: social media commentary Surrounding the 2014 US men’s and 2015 US women’s World Cup teams

Mega sporting events such as the World Cup have been found to stimulate categorization of in-groups and out-groups among fans. While self-categorization correlates with gender, the sport of soccer also facilitates nationalistic categorization. The World Cup features nation vs. nation competition while making gender a non-variable as the men and women compete in separate tournaments in separate years. This study examined 33,529 tweets illustrating social media match commentary involving US teams and opponents on Twitter during the 2014 and 2015 World Cups. Results revealed US teams were more likely to be described in regard to attributions of success and failure, while opposition teams were more likely to receive personal and physical attributions. Conversely, no differences were found between US Men’s and Women’s teams in regard to characterizations of success and failure, but revealed the Women’s team was more likely to receive personal and physical characterizations

Loyalty as permission to forgive: sport, political, and religious identification as predictors of transgression diminishment

A national survey of 314 Americans was employed to determine whether core forms of group identification (sport, political, and religious) predict one's likelihood to forgive a leader within that group for an intentional/preventable transgression. Three forms of transgressions (assault and battery, sex with a minor, stealing money) were presented as possible scenarios for leaders of sport, political, and religious groups. Sports leaders were more likely to be forgiven overall, with each of the three scenarios shifting levels of forgiveness; sex with a minor was more likely to be forgiven for sports figures, while stealing money was less likely to be forgiven for religious leaders. Unaffiliated individuals were less likely to forgive transgressions, with no differences between identified groups

Genome sequence and description of the anaerobic lignin-degrading bacterium Tolumonas lignolytica sp. nov.

Tolumonas lignolytica BRL6-1T sp. nov. is the type strain of T. lignolytica sp. nov., a proposed novel species of the Tolumonas genus. This strain was isolated from tropical rainforest soils based on its ability to utilize lignin as a sole carbon source. Cells of Tolumonas lignolytica BRL6-1T are mesophilic, non-spore forming, Gram-negative rods that are oxidase and catalase negative. The genome for this isolate was sequenced and returned in seven unique contigs totaling 3.6Mbp, enabling the characterization of several putative pathways for lignin breakdown. Particularly, we found an extracellular peroxidase involved in lignin depolymerization, as well as several enzymes involved in β-aryl ether bond cleavage, which is the most abundant linkage between lignin monomers. We also found genes for enzymes involved in ferulic acid metabolism, which is a common product of lignin breakdown. By characterizing pathways and enzymes employed in the bacterial breakdown of lignin in anaerobic environments, this work should assist in the efficient engineering of biofuel production from lignocellulosic material

Lifespan extension and the doctrine of double effect

Recent developments in biogerontology—the study of the biology of ageing—suggest that it may eventually be possible to intervene in the human ageing process. This, in turn, offers the prospect of significantly postponing the onset of age-related diseases. The biogerontological project, however, has met with strong resistance, especially by deontologists. They consider the act of intervening in the ageing process impermissible on the grounds that it would (most probably) bring about an extended maximum lifespan—a state of affairs that they deem intrinsically bad. In a bid to convince their deontological opponents of the permissibility of this act, proponents of biogerontology invoke an argument which is grounded in the doctrine of double effect. Surprisingly, their argument, which we refer to as the ‘double effect argument’, has gone unnoticed. This article exposes and critically evaluates this ‘double effect argument’. To this end, we first review a series of excerpts from the ethical debate on biogerontology in order to substantiate the presence of double effect reasoning. Next, we attempt to determine the role that the ‘double effect argument’ is meant to fulfil within this debate. Finally, we assess whether the act of intervening in ageing actually can be justified using double effect reasoning

Hospital Readmission in General Medicine Patients: A Prediction Model

Background: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design: Prospective observational cohort study. Patients: Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements: We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results: Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions: Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience