Clustering Spectrum of scale-free networks

Real-world networks often have power-law degrees and scale-free properties such as ultra-small distances and ultra-fast information spreading. In this paper, we study a third universal property: three-point correlations that suppress the creation of triangles and signal the presence of hierarchy. We quantify this property in terms of $\bar c(k)$, the probability that two neighbors of a degree-$k$ node are neighbors themselves. We investigate how the clustering spectrum $k\mapsto\bar c(k)$ scales with $k$ in the hidden variable model and show that $c(k)$ follows a {\it universal curve} that consists of three $k$-ranges where $\bar c(k)$ remains flat, starts declining, and eventually settles on a power law $\bar c(k)\sim k^{-\alpha}$ with $\alpha$ depending on the power law of the degree distribution. We test these results against ten contemporary real-world networks and explain analytically why the universal curve properties only reveal themselves in large networks

Parameter estimators of random intersection graphs with thinned communities

This paper studies a statistical network model generated by a large number of randomly sized overlapping communities, where any pair of nodes sharing a community is linked with probability $q$ via the community. In the special case with $q=1$ the model reduces to a random intersection graph which is known to generate high levels of transitivity also in the sparse context. The parameter $q$ adds a degree of freedom and leads to a parsimonious and analytically tractable network model with tunable density, transitivity, and degree fluctuations. We prove that the parameters of this model can be consistently estimated in the large and sparse limiting regime using moment estimators based on partially observed densities of links, 2-stars, and triangles.Comment: 15 page

Determinants of the impact factor of publications: A panel model for journals indexed in scopus 2017

This article has the purpose of establishing which are the variables that allow explaining the behavior of the SJR between 2014 and 2016, for the journals indexed in Scopus. To do this, journals that had a SJR value greater than eight in 2016 were selected, that is, 103 of the 22,231. For the analysis, a model of standard errors corrected for panel was used, for which a coefficient of determination of 81% was obtained, and a model of feasible generalized least squares. From these it was possible to establish that variables such as open access, the number of areas in which the publication is registered and the language of publication, are not significant to explain the impact of a publication. On the contrary, variables such as belonging to health sciences or social sciences

Citescore of publications indexed in Scopus: an implementation of panel data

This article is intended to establish the variables that explain the behavior of the CiteScore metrics from 2014 to 2016, for journals indexed in Scopus in 2017. With this purpose, journals with a CiteScore value greater than 11 were selected in any of the periods, that is to say, 133 journals. For the data analysis, a model of standard corrected errors for panel was used, from which a coefficient of determination of 77% was obtained. From the results, it was possible to state that journals of arts and humanities; business; administration and accounting; economics, econometrics, and finance; immunology and microbiology; medicine and social sciences, have the greatest impact.Corporación Universitaria Minuto de Dios, Fundación Universitaria Konrad Lorenz, Universidad de La Habana, Universidad de la Costa

Compact solid-state CMOS single-photon detector array for in vivo NIR fluorescence lifetime oncology measurements

In near infrared fluorescence-guided surgical oncology, it is challenging to distinguish healthy from cancerous tissue. One promising research avenue consists in the analysis of the exogenous fluorophores’ lifetime, which are however in the (sub-)nanosecond range. We have integrated a single-photon pixel array, based on standard CMOS SPADs (single-photon avalanche diodes), in a compact, time-gated measurement system, named FluoCam. In vivo measurements were carried out with indocyanine green (ICG)-modified derivatives targeting the avb3 integrin, initially on a genetically engineered mouse model of melanoma injected with ICG conjugated with tetrameric cyclic pentapeptide (ICG􀀀E[c(RGDfK)4]), then on mice carrying tumour xenografts of U87-MG (a human primary glioblastoma cell line) injected with monomeric ICG􀀀c(RGDfK). Measurements on tumor, muscle and tail locations allowed us to demonstrate the feasibility of in vivo lifetime measurements with the FluoCam, to determine the characteristic lifetimes (around 500 ps) and subtle lifetime differences between bound and unbound ICG-modified fluorophores (10% level), as well as to estimate the available photon fluxes under realistic conditions

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

Early Mendeley readers correlate with later citation counts

This is an accepted manuscript of an article published by Springer in Scientometrics on 26/03/2018, available online: https://doi.org/10.1007/s11192-018-2715-9 The accepted version of the publication may differ from the final published version.Counts of the number of readers registered in the social reference manager Mendeley have been proposed as an early impact indicator for journal articles. Although previous research has shown that Mendeley reader counts for articles tend to have a strong positive correlation with synchronous citation counts after a few years, no previous studies have compared early Mendeley reader counts with later citation counts. In response, this first diachronic analysis compares reader counts within a month of publication with citation counts after 20 months for ten fields. There were moderate or strong correlations in eight out of ten fields, with the two exceptions being the smallest categories (n=18, 36) with wide confidence intervals. The correlations are higher than the correlations between later citations and early citations, showing that Mendeley reader counts are more useful early impact indicators than citation counts

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

Analysis of observations indicates that there was a rapid increase in summer (June-August, JJA) mean surface air temperature (SAT) since the mid-1990s over Western Europe. Accompanying this rapid warming are significant increases in summer mean daily maximum temperature, daily minimum temperature, annual hottest day temperature and warmest night temperature, and an increase in frequency of summer days and tropical nights, while the change in the diurnal temperature range (DTR) is small. This study focuses on understanding causes of the rapid summer warming and associated temperature extreme changes. A set of experiments using the atmospheric component of the state-of-the-art HadGEM3 global climate model have been carried out to quantify relative roles of changes in sea surface temperature (SST)/sea ice extent (SIE), anthropogenic greenhouse gases (GHGs), and anthropogenic aerosols (AAer). Results indicate that the model forced by changes in all forcings reproduces many of the observed changes since the mid-1990s over Western Europe. Changes in SST/SIE explain 62.2% ± 13.0% of the area averaged seasonal mean warming signal over Western Europe, with the remaining 37.8% ± 13.6% of the warming explained by the direct impact of changes in GHGs and AAer. Results further indicate that the direct impact of the reduction of AAer precursor emissions over Europe, mainly through aerosol-radiation interaction with additional contributions from aerosol-cloud interaction and coupled atmosphere-land surface feedbacks, is a key factor for increases in annual hottest day temperature and in frequency of summer days. It explains 45.5% ± 17.6% and 40.9% ± 18.4% of area averaged signals for these temperature extremes. The direct impact of the reduction of AAer precursor emissions over Europe acts to increase DTR locally, but the change in DTR is countered by the direct impact of GHGs forcing. In the next few decades, greenhouse gas concentrations will continue to rise and AAer precursor emissions over Europe and North America will continue to decline. Our results suggest that the changes in summer seasonal mean SAT and temperature extremes over Western Europe since the mid-1990s are most likely to be sustained or amplified in the near term, unless other factors intervene

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed Sea Surface Temperature of the 54 year period 1960-2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land-atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution

MycoBank gearing up for new horizons

MycoBank, a registration system for fungi established in 2004 to capture all taxonomic novelties, acts as a coordination hub between repositories such as Index Fungorum and Fungal Names. Since January 2013, registration of fungal names is a mandatory requirement for valid publication under the International Code of Nomenclature for algae, fungi and plants (ICN). This review explains the database innovations that have been implemented over the past few years, and discusses new features such as advanced queries, registration of typification events (MBT numbers for lecto, epi- and neotypes), the multi-lingual database interface, the nomenclature discussion forum, annotation system, and web services with links to third parties. MycoBank has also introduced novel identification services, linking DNA sequence data to numerous related databases to enable intelligent search queries. Although MycoBank fills an important void for taxon registration, challenges for the future remain to improve links between taxonomic names and DNA data, and to also introduce a formal system for naming fungi known from DNA sequence data only. To further improve the quality of MycoBank data, remote access will now allow registered mycologists to act as MycoBank curators, using Citrix software