Studies of animal behavior consistently demonstrate that the social environment impacts cooperation, yet the effect of social dynamics has been largely excluded from studies of human cooperation. Here, we introduce a novel approach inspired by nonhuman primate research to address how social hierarchies impact human cooperation. Participants competed to earn hierarchy positions and then could cooperate with another individual in the hierarchy by investing in a common effort. Cooperation was achieved if the combined investments exceeded a threshold, and the higher ranked individual distributed the spoils unless control was contested by the partner. Compared to a condition lacking hierarchy, cooperation declined in the presence of a hierarchy due to a decrease in investment by lower ranked individuals. Furthermore, hierarchy was detrimental to cooperation regardless of whether it was earned or arbitrary. These findings mirror results from nonhuman primates and demonstrate that hierarchies are detrimental to cooperation. However, these results deviate from nonhuman primate findings by demonstrating that human behavior is responsive to changing hierarchical structures and suggests partnership dynamics that may improve cooperation. This work introduces a controlled way to investigate the social influences on human behavior, and demonstrates the evolutionary continuity of human behavior with other primate species.
Movement interactions and the underlying social structure in groups have relevance across many social-living species. Collective motion of groups could be based on an “egalitarian” decision system, but in practice it is often influenced by underlying social network structures and by individual characteristics. We investigated whether dominance rank and personality traits are linked to leader and follower roles during joint motion of family dogs. We obtained high-resolution spatio-temporal GPS trajectory data (823,148 data points) from six dogs belonging to the same household and their owner during 14 30-40 min unleashed walks. We identified several features of the dogs' paths (e.g., running speed or distance from the owner) which are characteristic of a given dog. A directional correlation analysis quantifies interactions between pairs of dogs that run loops jointly. We found that dogs play the role of the leader about 50-85% of the time, i.e. the leader and follower roles in a given pair are dynamically interchangable. However, on a longer timescale tendencies to lead differ consistently. The network constructed from these loose leader-follower relations is hierarchical, and the dogs' positions in the network correlates with the age, dominance rank, trainability, controllability, and aggression measures derived from personality questionnaires. We demonstrated the possibility of determining dominance rank and personality traits of an individual based only on its logged movement data. The collective motion of dogs is influenced by underlying social network structures and by characteristics such as personality differences. Our findings could pave the way for automated animal personality and human social interaction measurements.
Integration of local elements into a coherent global form is a fundamental aspect of visual object recognition. How the different hierarchically organized stages of visual analysis develop in order to support object representation in infants remains unknown. The aim of this study was to investigate structural encoding of natural images in 4- to 6-month-old infants and adults. We used the steady-state visual evoked potential (ssVEP) technique to measure cortical responses specific to the global structure present in object and face images, and assessed whether differential responses were present for these image categories. This study is the first to apply the ssVEP method to high-level vision in infants. Infants and adults responded to the structural relations present in both image categories, and topographies of the responses differed based on image category. However, while adult responses to face and object structure were localized over occipitotemporal scalp areas, only infant face responses were distributed over temporal regions. Therefore, both infants and adults show object category specificity in their neural responses. The topography of the infant response distributions indicates that between 4 and 6 months of age, structure encoding of faces occurs at a higher level of processing than that of objects.
Human dental enamel, the hardest tissue in the body, plays a vital role in protecting teeth from wear as a result of daily grinding and chewing as well as from chemical attack. It is well established that the mechanical strength and fatigue resistance of dental enamel are derived from its hierarchical structure, which consists of periodically arranged bundles of hydroxyapatite (HAP) nanowires. However, we do not yet have a full understanding of the in vivo HAP crystallization process that leads to this structure. Mg(2+) ions, which are present in many biological systems, regulate HAP crystallization by stabilizing its precursor, amorphous calcium phosphate (ACP), but their atomic-scale distribution within HAP is unknown. We use atom probe tomography to provide the first direct observations of an intergranular Mg-rich ACP phase between the HAP nanowires in mature human dental enamel. We also observe Mg-rich elongated precipitates and pockets of organic material among the HAP nanowires. These observations support the postclassical theory of amelogenesis (that is, enamel formation) and suggest that decay occurs via dissolution of the intergranular phase. This information is also useful for the development of more accurate models to describe the mechanical behavior of teeth.
The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.
We introduce a new method for detecting communities of arbitrary size in an undirected weighted network. Our approach is based on tracing the path of closest-friendship between nodes in the network using the recently proposed Generalized Erds Numbers. This method does not require the choice of any arbitrary parameters or null models, and does not suffer from a system-size resolution limit. Our closest-friend community detection is able to accurately reconstruct the true network structure for a large number of real world and artificial benchmarks, and can be adapted to study the multi-level structure of hierarchical communities as well. We also use the closeness between nodes to develop a degree of robustness for each node, which can assess how robustly that node is assigned to its community. To test the efficacy of these methods, we deploy them on a variety of well known benchmarks, a hierarchal structured artificial benchmark with a known community and robustness structure, as well as real-world networks of coauthorships between the faculty at a major university and the network of citations of articles published in Physical Review. In all cases, microcommunities, hierarchy of the communities, and variable node robustness are all observed, providing insights into the structure of the network.
Biogenic amines, particularly serotonin, are recognised to play an important role in controlling the aggression of invertebrates, whereas the effect of neurohormones is still underexplored. The crustacean Hyperglycemic Hormone (cHH) is a multifunctional member of the eyestalk neuropeptide family. We expect that this neuropeptide influences aggression either directly, by controlling its expression, or indirectly, by mobilizing the energetic stores needed for the increased activity of an animal. Our study aims at testing such an influence and the possible reversion of hierarchies in the red swamp crayfish, Procambarus clarkii, as a model organism. Three types of pairs of similarly sized males were formed: (1) ‘control pairs’ (CP, n = 8): both individuals were injected with a phosphate saline solution (PBS); (2) ‘reinforced pairs’ (RP, n = 9): the alpha alone was injected with native cHH, and the beta with PBS; (3) ‘inverted pairs’ (IP, n = 9): the opposite of (2). We found that, independently of the crayfish’s prior social experience, cHH injections induced (i) the expression of dominance behaviour, (ii) higher glycemic levels, and (iii) lower time spent motionless. In CP and RP, fight intensity decreased with the establishment of dominance. On the contrary, in IP, betas became increasingly likely to initiate and escalate fights and, consequently, increased their dominance till a temporary reversal of the hierarchy. Our results demonstrate, for the first time, that, similarly to serotonin, cHH enhances individual aggression, up to reverse, although transitorily, the hierarchical rank. New research perspectives are thus opened in our intriguing effort of understanding the role of cHH in the modulation of agonistic behaviour in crustaceans.
Power and performance management problem in large scale computing systems like data centers has attracted a lot of interests from both enterprises and academic researchers as power saving has become more and more important in many fields. Because of the multiple objectives, multiple influential factors and hierarchical structure in the system, the problem is indeed complex and hard. In this paper, the problem will be investigated in a virtualized computing system. Specifically, it is formulated as a power optimization problem with some constraints on performance. Then, the adaptive controller based on least-square self-tuning regulator(LS-STR) is designed to track performance in the first step; and the resource solved by the controller is allocated in order to minimize the power consumption as the second step. Some simulations are designed to test the effectiveness of this method and to compare it with some other controllers. The simulation results show that the adaptive controller is generally effective: it is applicable for different performance metrics, for different workloads, and for single and multiple workloads; it can track the performance requirement effectively and save the power consumption significantly.
Flower-like AgCl microstructures with enhanced visible light-driven photocatalysis are synthesized by a facile one-pot hydrothermal process for the first time. The evolution process of AgCl from dendritic structures to flower-like octagonal microstructures is investigated quantitatively. Furthermore, the flower-like AgCl microstructures exhibit enhanced ability of visible light-assisted photocatalytic degradation of methyl orange. The enhanced photocatalytic activity of the flower-like AgCl microstructure is attributed to its three-dimensional hierarchical structure exposing with  facets. This work provides a fresh view into the insight of electrochemical process and the application area of visible light photocatalysts.
Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for “omics-driven” classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.