Information processing on smartphones in public versus private

People increasingly turn to news on mobile devices, often while out and about, attending to daily tasks. Yet, we know little about whether attention to and learning from information on a mobile differs by the setting of use. This study builds on Multiple Resource Theory (Wickens, 1984) and the Resource Competition Framework (Oulasvirta et al., 2005) to compare visual attention to a dynamic newsfeed, varying only the setting: private or public. We use mobile eye-tracking to evaluate the effects of setting on attention and assess correspondent learning differences after exposure to the feed, which allows us to uncover a relationship between attention and learning. Findings indicate higher visual attention to mobile newsfeed posts in public, relative to a private setting. Moreover, scrolling through news on a smartphone in public attenuates some knowledge gain but is beneficial for other learning outcomes

Detection of gravitational-wave signals from binary neutron star mergers using machine learning

As two neutron stars merge, they emit gravitational waves that can potentially be detected by Earth-bound detectors. Matched-filtering-based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network-based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from nonspinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of ten per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 4 in sensitivity to signals with a signal-to-noise ratio between 8 and 15. However, this approach is not yet competitive with traditional matched-filtering-based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can be applied not only to machine-learning-based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches. © 2020 authors. Published by the American Physical Society

Frequencies, Drivers, and Solutions to News Non-Attendance: Investigating Differences Between Low News Usage and News (Topic) Avoidance with Conversational Agents

Low levels of news seeking can be problematic for an informed citizenry. Previous research has discussed different types of news non-attendance but conceptual ambiguities between low news usage, general news avoidance, and news topic avoidance still exist. By using a longitudinal design conducted with a chatbot survey among Dutch users (n = 189), this study provides first empirical evidence that helps clarify conceptual differences. First, it estimates the prevalence of these different types of news non-attendance. Second, it tests to what extend cognitive restrictions, quality assessments, and personal relevance are relevant predictors in explaining engagement in three types of non-attendance to news. Third, the study investigates how news usage behaviors (e.g., news curation, news snacking, and verification engagement) may serve as potential user-driven counter strategies against news avoidance. We find evidence for the conceptual differences. Only small shares of news non-attendance are explained by avoidance motivations. Especially news curation and verification engagement can mitigate common drivers of news avoidance, while news snacking reinforces them

L-selectin is essential for delivery of activated CD8+ T cells to virus-infected organs for protective immunity

Cytotoxic CD8+ T lymphocytes play a critical role in the host response to infection by viruses. The ability to secrete cytotoxic chemicals and cytokines is considered pivotal for eliminating virus. Of equal importance is how effector CD8+ T cells home to virus-infected tissues. L-selectin has not been considered important for effector T cell homing, because levels are low on activated T cells. We report here that, although L-selectin expression is downregulated following T cell priming in lymph nodes, L-selectin is re-expressed on activated CD8+ T cells entering the bloodstream, and recruitment of activated CD8+ T cells from the bloodstream into virus-infected tissues is L-selectin dependent. Furthermore, L-selectin on effector CD8+ T cells confers protective immunity to two evolutionally distinct viruses, vaccinia and influenza, which infect mucosal and visceral organs, respectively. These results connect homing and a function of virus-specific CD8+ T cells to a single molecule, L-selectin

Generational Gaps in Political Media Use and Civic Engagement

"This book investigates news use patterns among five different generations in a time where digital media create a multi-choice media environment. The book introduces a new model – The EPIG Model (Engagement-Participation-Information*Generation) – to study how different generational cohorts’ exposure to political information is related to their political engagement and participation. The authors build on a multi-method framework to determine direct and indirect media effects across generations. The unique dataset allows for comparison of effects between legacy and social media use and helps to disentangle the influence on citizens’ political involvement in nonelection as well as during political campaign times. Bringing the newly of-age Generation Z into the picture, the book presents an in-depth understanding of how a changing media environment presents different challenges and opportunities for political involvement of this, as well as older generations. Bringing the conversation around political engagement and the media up to date for the new generation, this book will be of key importance to scholars and students in the areas of media studies, communication studies, technology, political science and political communication.

Purity of transferred CD8+ T cells is crucial for safety and efficacy of combinatorial tumor immunotherapy in the absence of SHP-1

Adoptive transfer of tumor-specific cytotoxic T cells is a promising advance in cancer therapy. Similarly, checkpoint inhibition has shown striking clinical results in some patients. Here we combine adoptive cell transfer with ablation of the checkpoint protein Src homology 2-domain-containing phosphatase 1 (SHP-1, Ptpn6). Naturally occurring motheaten mice lack SHP-1 and do not survive weaning due to extensive immunopathology. To circumvent this limitation, we created a novel SHP-1(null) mouse that is viable up to 12 weeks of age by knocking out IL1r1. Using this model, we demonstrate that the absence of SHP-1 augments the ability of adoptively transferred CD8(+) T cells to control tumor growth. This therapeutic effect was only observed in situations where T-cell numbers were limited, analogous to clinical settings. However, adoptive transfer of non-CD8(+) SHP-1(null) hematopoietic cells resulted in lethal motheaten-like pathology, indicating that systemic inhibition of SHP-1 could have serious adverse effects. Despite this caveat, our findings support the development of SHP-1 inhibition strategies in human T cells to complement adoptive transfer therapies in the clinic

Ethylene- and pathogen-inducible Arabidopsis acyl-CoA-binding protein 4 interacts with an ethylene-responsive element binding protein

Six genes encode proteins with acyl-CoA-binding domains in Arabidopsis thaliana. They are the small 10-kDa cytosolic acyl-CoA-binding protein (ACBP), membrane-associated ACBP1 and ACBP2, extracellularly-targeted ACBP3, and kelch-motif containing ACBP4 and ACBP5. Here, the interaction of ACBP4 with an A. thaliana ethylene-responsive element binding protein (AtEBP), identified in a yeast two-hybrid screen, was confirmed by co-immunoprecipitation. The subcellular localization of ACBP4 and AtEBP, was addressed using an ACBP4:DsRed red fluorescent protein fusion and a green fluorescent protein (GFP):AtEBP fusion. Transient expression of these autofluoresence-tagged proteins in agroinfiltrated tobacco leaves, followed by confocal laser scanning microscopy, indicated their co-localization predominantly at the cytosol which was confirmed by FRET analysis. Immuno-electron microscopy on Arabidopsis sections not only localized ACBP4 to the cytosol but also to the periphery of the nucleus upon closer examination, perhaps as a result of its interaction with AtEBP. Furthermore, the expression of ACBP4 and AtEBP in Northern blot analyses was induced by the ethylene precursor 1-aminocyclopropane-1-carboxylic acid, methyl jasmonate treatments, and Botrytis cinerea infection, suggesting that the interaction of ACBP4 and AtEBP may be related to AtEBP-mediated defence possibly via ethylene and/or jasmonate signalling

Satellite university campuses and economic development in peripheral regions

Satellite university campuses – whereby established universities decentralise part of their activities, often to areas previously lacking a university – contribute to the diversification of university systems. While satellite campuses, due to their small scale and limited resources, might perform some activities less efficiently than their larger parent universities, we argue that they are uniquely placed to serve the needs of their localities. Based on the case of a satellite campus in North-West Italy, we show that: (i) the campus’ main contribution lies in widening access to higher education to residents who would not attend university in the absence of local provision; (ii) the campus contributes to local development also through research and business and community engagement, and by stimulating local demand for knowledge-intensive services; (iii) research and engagement are more effective for local development where local firms possess relevant absorptive capacity and where there is a favourable institutional framework

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.Comment: 25 pages, 6 figures, 4 tables, additional material available at https://github.com/gwastro/ml-mock-data-challenge-