An experimental evaluation of the consistency of competitive ability and agonistic dominance in different social contexts in captive bonobos

Bonobos have been described as a relatively egalitarian and female dominant species. The exact nature and quality of their dominance relationships and the existence of female dominance are current topics of dispute. We investigated the consistency across social contexts, the stability in time, and the degree of expression of the competitive feeding ability and agonistic dominance in a captive group of bonobos. First, we examined whether the competitive feeding ranks and agonistic ranks differed in different dyadic contexts, triadic contexts and the whole group context. For some pairs of animals the dominance relationships with respect to competitive feeding altered with different group compositions. The agonistic dominance relationships changed accordingly. The competitive feeding ranks and agonistic ranks in the experiments correlated strongly with each other. The alpha position was occupied by a female, but not all females outranked all males. We suggest that females can profit from each others presence to gain inter-sexual dominance. Second, although the agonistic rank order in the whole group remained the same over at least five years, some dyadic competitive feeding ranks changed over time, resulting in a stronger female intersexual dominance. Third, the degree of expression of the behaviors used to quantify dyadic competitive and agonistic dominance was not high, in line with the popular egalitarian epithet. Notwithstanding its low consistency across contexts, the dominance hierarchy in the whole group has a strong predictive value for other social relationships such as grooming. Given this strong effect of rank on other behaviours and given the strong dependency of rank on social context, the choice of the right party members may be a crucial factor in the fission-fusion processes of free-ranging bonobos

The pivotal role of rank in grooming and support behaviour in a captive group of bonobos (Pan paniscus)

We investigated dyadic grooming relationships in a captive group of bonobos (Pan paniscus) and questioned what social function grooming fulfils in the market of services and favors. Hereto we examined which of two theoretical models - grooming for support (Seyfarth, 1977, 1980) or grooming according to the similarity principle (de Waal & Luttrell, 1986) - best accounted for the observed grooming distribution. Similarity in traits did not correlate with increased grooming or close proximity among the individuals. Therefore, the similarity hypothesis was rejected. Seyfarths model of rank-related grooming was largely confirmed. The animals distributed their grooming according to the rank of the receivers. We found an exchange between grooming and receipt of support. There was more grooming up than down the hierarchy. However, not all predictions about rank-related competition over grooming were confirmed. We found that dyadic grooming reciprocity indeed increased with decreasing rank distance. Yet, there was no increase of grooming within the dyad with decreasing rank distance and high ranking individuals were not competed over at the highest rates. The observed correlation between grooming and support received represents an important fit with Seyfarths prediction, but does not allow for conclusions about underlying causal processes. Other causal explanations, besides the groom to receive support hypothesis, that could explain a similar correlation are discussed

Statically detecting message confusions in a multi-protocol setting

In a multi-protocol setting, different protocols are concurrently executed, and each principal can participate in more than one. The possibilities of attacks therefore increase, often due to the presence of similar patterns in messages. Messages coming from one protocol can be confused with similar messages coming from another protocol. As a consequence, data of one type may be interpreted as data of another, and it is also possible that the type is the expected one, but the message is addressed to another protocol. In this paper, we shall present an extension of the LySa calculus [7, 4] that decorates encryption with tags including the protocol identifier, the protocol step identifier and the intended types of the encrypted terms. The additional information allows us to find the messages that can be confused and therefore to have hints to reconstruct the attack. We extend accordingly the standard static Control Flow Analysis for LySa, which over-approximates all the possible behaviour of the studied protocols, included the possible message confusions that may occur at run-time. Our analysis has been implemented and successfully applied to small sets of protocols. In particular, we discovered an undocumented family of attacks, that may arise when Bauer-Berson-Feiertag and the Woo-Lam authentication protocols are running in parallel. The implementation complexity of the analysis is low polynomial

Endothelial cell injury by high glucose and heparanase is prevented by insulin, heparin and basic fibroblast growth factor

BACKGROUND: Uncontrolled hyperglycemia is the main risk factor in the development of diabetic vascular complications. The endothelial cells are the first cells targeted by hyperglycemia. The mechanism of endothelial injury by high glucose is still poorly understood. Heparanase production, induced by hyperglycemia, and subsequent degradation of heparan sulfate may contribute to endothelial injury. Little is known about endothelial injury by heparanase and possible means of preventing this injury. OBJECTIVES: To determine if high glucose as well as heparanase cause endothelial cell injury and if insulin, heparin and bFGF protect cells from this injury. METHODS: Cultured porcine aortic endothelial cells were treated with high glucose (30 mM) and/or insulin (1 U/ml) and/or heparin (0.5 μg/ml) and /or basic fibroblast growth factor (bFGF) (1 ng/ml) for seven days. Cells were also treated with heparinase I (0.3 U/ml, the in vitro surrogate heparanase), plus insulin, heparin and bFGF for two days in serum free medium. Endothelial cell injury was evaluated by determining the number of live cells per culture and lactate dehydrogenase (LDH) release into medium expressed as percentage of control. RESULTS: A significant decrease in live cell number and increase in LDH release was found in endothelial cells treated with high glucose or heparinase I. Insulin and/or heparin and/or bFGF prevented these changes and thus protected cells from injury by high glucose or heparinase I. The protective ability of heparin and bFGF alone or in combination was more evident in cells damaged with heparinase I than high glucose. CONCLUSION: Endothelial cells injured by high glucose or heparinase I are protected by a combination of insulin, heparin and bFGF, although protection by heparin and/or bFGF was variable

Hunting for Significance: Bayesian Classifiers Under a Mixture Loss Function

Detecting significance in a high-dimensional sparse data structure has received a large amount of attention in modern statistics. In the current paper, we introduce a compound decision rule to simultaneously classify signals from noise. This procedure is a Bayes rule subject to a mixture loss function. The loss function minimizes the number of false discoveries while controlling the false nondiscoveries by incorporating the signal strength information. Based on our criterion, strong signals will be penalized more heavily for nondiscovery than weak signals. In constructing this classification rule, we assume a mixture prior for the parameter which adapts to the unknown sparsity. This Bayes rule can be viewed as thresholding the “local fdr” (Efron, 2007) by adaptive thresholds. Both parametric and nonparametric methods will be discussed. The nonparametric procedure adapts to the unknown data structure well and outperforms the parametric one. Performance of the procedure is illustrated by various simulation studies and a real data application

Real-Time Dynamic Map with Crowdsourcing Vehicles in Edge Computing

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accurac

A step-by-step guide on how NOT to get published in a high-impact educational technology journal

Many elements come together to make for a good, publishable manuscript in a high-impact educational technology journal. There are also some fatal errors and omissions that will doom a paper to be immediately declined. In this editorial, we focus on the latter. Checking the aims and scope of a journal is a simple step to ensure that the publication of an article is not delayed and time is not wasted. Unfortunately, it seems this step is neglected by many aspiring authors. Beyond a misalignment with the aims and scope of the journal, we will discuss some of the other main reasons why manuscripts have recently been declined by AJET. We hope that this editorial will assist authors to ensure that they do not make the same mistakes