Hydrogen sulfide (H2S, “sulfide”) is a naturally occurring component of the marine sediment. Eutrophication of coastal waters, however, can lead to an excess of sulfide production that can prove toxic to seagrasses. We used stable sulfur isotope ratio (δ34S) measurements to assess sulfide intrusion in the seagrass Halodule wrightii, a semi-tropical species found throughout the Gulf of Mexico, Caribbean Sea, and both western and eastern Atlantic coasts. We found a gradient in δ34S values (−5.58 ± 0.54‰+13.58 ± 0.30‰) from roots to leaves, in accordance with prior observations and those from other species. The results may also represent the first values reported for H. wrightii rhizome tissue. The presence of sulfide-derived sulfur in varying proportions (15–55%) among leaf, rhizome, and root tissues suggests H. wrightii is able to assimilate sedimentary H2S into non-toxic forms that constitute a significant portion of the plant’s total sulfur content.

This article uses a “mystery client” approach and visits the websites of National Statistical Offices and international microdata libraries to assess whether foundational microdata sets for countries in the Middle East and North Africa region are collected, up to date, and made available to researchers. The focus is on population and economic censuses, price data and consumption, labor, health, and establishment surveys. The results show that about half of the expected core data sets are being collected and that only a fraction is made available publicly. As a consequence, many summary statistics, including national accounts and welfare estimates, are outdated and of limited relevance to decision-makers. Additional investments in microdata collection and publication of the data once collected are strongly advised.

The 2014 Research Excellence Framework (REF) assessed the quality of university research in the UK. 20% of the assessment was allocated according to peer review of the impact of research, reflecting the growing importance of impact in UK government policy. Beyond academia, impact is defined as a change or benefit to the economy, society, culture, public policy or services, health, the environment, or quality of life. Each institution submitted a set of four-page impact case studies. These are predominantly free-form descriptions and evidences of the impact of study. Numerous analyses of these case studies have been conducted, but they have utilised either qualitative methods or primary forms of text searching. These approaches have limitations, including the time required to manually analyse the data and the frequently inferior quality of the answers provided by applying computational analysis to unstructured, context-less free text data. This paper describes a new system to address these problems. At its core is a structured, queryable representation of the case study data. We describe the ontology design used to structure the information and how semantic web related technologies are used to store and query the data. Experiments show that this gives two significant advantages over existing techniques: improved accuracy in question answering and the capability to answer a broader range of questions, by integrating data from external sources. Then we investigate whether machine learning can predict each case study’s grade using this structured representation. The results provide accurate predictions for computer science impact case studies.

Graph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes themselves. If these objectives are achieved, an embedding is a meaningful, understandable, and often compressed representation of a network. Unfortunately, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we extend the framework for evaluating graph embeddings that was recently introduced in [15]. Now, the framework assigns two scores, local and global, to each embedding that measure the quality of an evaluated embedding for tasks that require good representation of local and, respectively, global properties of the network. The best embedding, if needed, can be selected in an unsupervised way, or the framework can identify a few embeddings that are worth further investigation. The framework is flexible and scalable and can deal with undirected/directed and weighted/unweighted graphs.

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

In this paper, we study asymmetric Ramsey properties of the random graph $G_{n,p}$. Let $r \in \mathbb{N}$ and $H_1, \ldots, H_r$ be graphs. We write $G_{n,p} \to (H_1, \ldots, H_r)$ to denote the property that whenever we colour the edges of $G_{n,p}$ with colours from the set $[r] \,{:\!=}\, \{1, \ldots, r\}$ there exists $i \in [r]$ and a copy of $H_i$ in $G_{n,p}$ monochromatic in colour $i$. There has been much interest in determining the asymptotic threshold function for this property. In several papers, Rödl and Ruciński determined a threshold function for the general symmetric case; that is, when $H_1 = \cdots = H_r$. A conjecture of Kohayakawa and Kreuter from 1997, if true, would fully resolve the asymmetric problem. Recently, the $1$-statement of this conjecture was confirmed by Mousset, Nenadov and Samotij.

Building on work of Marciniszyn, Skokan, Spöhel and Steger from 2009, we reduce the $0$-statement of Kohayakawa and Kreuter’s conjecture to a certain deterministic subproblem. To demonstrate the potential of this approach, we show this subproblem can be resolved for almost all pairs of regular graphs. This therefore resolves the $0$-statement for all such pairs of graphs.

“Return-to-player” information is used in several jurisdictions to display the long-run cost of gambling, but previous evidence suggests that these messages are frequently misunderstood by gamblers. Two ways of improving the communication of return-to-player information have been suggested: switching to an equivalent “house-edge” format, or via the use of a “volatility warning,” clarifying that the information applies only in the statistical long run. In this study, Australian participants (N = 603) were presented with either a standard return-to-player message, the same message supplemented with a volatility warning, or a house-edge message. The return-to-player plus volatility warning message was understood correctly more frequently than the return-to-player message, but the house-edge message was understood best of all. Participants perceived the lowest chance of winning in the return-to-player plus volatility warning condition. These findings contribute data on the relative merits of two proposed approaches in the design of improved gambling information.

We use probabilistic methods to study properties of mean-field models, which arise as large-scale limits of certain particle systems with mean-field interaction. The underlying particle system is such that n particles move forward on the real line. Specifically, each particle ‘jumps forward’ at some time points, with the instantaneous rate of jumps given by a decreasing function of the particle’s location quantile within the overall distribution of particle locations. A mean-field model describes the evolution of the particles’ distribution when n is large. It is essentially a solution to an integro-differential equation within a certain class. Our main results concern the existence and uniqueness of—and attraction to—mean-field models which are traveling waves, under general conditions on the jump-rate function and the jump-size distribution.

The unfolded protein response has recently been implicated as a mechanism by which 1,10-phenanthroline-containing coordination compounds trigger cell death. We explored the interaction of two such compounds—one containing copper and one containing manganese—with endoplasmic reticulum (ER) stress. Pretreatment with anisomycin significantly enhanced the cytotoxic activity of both metal-based compounds in A2780, but only the copper-based compound in A549 cells. The effects of pretreatment with tunicamycin were dependent on the nature of the metal center in the compounds. In A2780 cells, the cytotoxic action of the copper compound was reduced by tunicamycin only at high concentration. In contrast, in A549 cells the efficacy of the manganese compound cells was reduced at all tested concentrations. Intriguingly, some impact of free 1,10-phenanthroline was also observed in A549 cells. These results are discussed in the context of the emerging evidence that the ER plays a role in the cytotoxic action of 1,10-phenanthroline-based compounds.