Food site residence time and female competitive relationships in wild gray-cheeked mangabeys (Lophocebus albigena)

Authors of socioecological models propose that food distribution affects female social relationships in that clumped food resources, such as fruit, result in strong dominance hierarchies and favor coalition formation with female relatives. A number of Old World monkey species have been used to test predictions of the socioecological models. However, arboreal forest-living Old World monkeys have been understudied in this regard, and it is legitimate to ask whether predominantly arboreal primates living in tropical forests exhibit similar or different patterns of behavior. Therefore, the goal of our study was to investigate female dominance relationships in relation to food in gray-cheeked mangabeys (Lophocebus albigena). Since gray-cheeked mangabeys are largely frugivorous, we predicted that females would have linear dominance hierarchies and form coalitions. In addition, recent studies suggest that long food site residence time is another important factor in eliciting competitive interactions. Therefore, we also predicted that when foods had long site residence times, higher-ranking females would be able to spend longer at the resource than lower-ranking females. Analyses showed that coalitions were rare relative to some other Old World primate species, but females had linear dominance hierarchies. We found that, contrary to expectation, fruit was not associated with more agonism and did not involve long site residence times. However, bark, a food with a long site residence time and potentially high resource value, was associated with more agonism, and higher-ranking females were able to spend more time feeding on it than lower-ranking females. These results suggest that higher-ranking females may benefit from higher food and energy intake rates when food site residence times are long. These findings also add to accumulating evidence that food site residence time is a behavioral contributor to female dominance hierarchies in group-living species

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Global Decrease of Histone H3K27 Acetylation in ZEB1-Induced Epithelial to Mesenchymal Transition in Lung Cancer Cells

cancer

The “Weisbrod Quadrilemma” revisited: insurance incentives on new health technologies

The effect of insurance expansion on the diffusion of new technologies is not a well-understood phenomenon. Arguably, an expansion of insurance coverage provides a motivation for R&D investment in medical technologies. Although risk pooling through insurance gives rise to greater affordability for existing treatments and becomes a volume driver of new treatments, it may also influence provider reimbursement through a monopsony purchaser effect and related cost control measures. However, the impact that insurance has on technology availability and R&D investment, and more generally on the adoption of new technologies, remains an unexplored empirical question. This paper presents evidence of a link between insurance and technology diffusion using OECD panel data and taking advantage of a dynamic specification structure. Our empirical estimates indicate that higher degrees of private expenditure on health care correlate with higher levels of R&D in health care, consistent with the hypothesis forwarded by Weisbrod that increasing insurance coverage boosts technology adoption. However, our findings also suggest that increasing public funding of health care appears to lower technological adoption, which is, of course, consistent with the exercising of monopsony power and an objective of cost containment

CHANCE DISCOVERY IN STOCK INDEX OPTION AND FUTURES ARBITRAGE

The prices of the option and futures of a stock both reflect the market's expectation of futures changes of the stock's price. Their prices normally align with each other within a limited window. When they do not, arbitrage opportunities arise: an investor who spots the misalignment will be able to buy (sell) options on the one hand, and sell (buy) futures on the other and make risk-free profits. Historical data suggest that option and futures prices on the LIFFE Market do not align occasionally. Arbitrage chances are rare. Besides, they last for seconds only before the market adjusts itself. The challenge is not only to discover such chances, but to discover them ahead of other arbitragers. In the past, we have introduced EDDIE as a genetic programming tool for forecasting. This paper describes EDDIE-ARB, a specialization of EDDIE, for forecasting arbitrage opportunities. As a tool, EDDIE-ARB was designed to enable economists and computer scientists to work together to identify relevant independent variables. Trained on historical data, EDDIE-ARB was capable of discovering rules with high precision. Tested on out-of-sample data, EDDIE-ARB out-performed a naive ex ante rule, which reacted only when misalignments were detected. This establishes EDDIE-ARB as a promising tool for arbitrage chances discovery. It also demonstrates how EDDIE brings domain experts and computer scientists together