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Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric—the “triadic influence”—that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks—functions of the triadic influence and the personalities of the students—control the evolution of real social networks, opening an avenue for the quantitative study of these systems. [ABSTRACT FROM AUTHOR]

Copyright of Eutopía: Revista de Desarrollo Económico Territorial is the property of Eutopia: Revista de Desarrollo Economico Territorial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent studies have shown that long non-coding RNAs (lncRNAs) have key functions during breast cancer development. Considering the complexity of IncRNAs regulatory network, the identification of novel and functional lncRNAs associated with breast cancer is thus very important. By using Agilent LncRNA Human Gene Expression Microarray, we identified a number of lncRNAs that were differentially expressed in breast cancer compared to their corresponding adjacent tissues. According to the microarray, the expression of p10247, henceforth named as lncRNA-BCHE (standing for lncRNA high expressed in breast cancer), was found to be uniformly higher in all the five breast cancer tissues tested, and this was further confirmed in 56 breast cancer tissues by real-time RT-PCR. The function of lncRNA-BCHE in breast cancer cells was tested by knockdown and over-expression experiments in vitro. We also analyzed the public cohorts of breast cancer patients on the Kaplan Meier plotter platform. Clinical analysis revealed that the expression of lncRNA-BCHE was significantly correlated with advanced clinical stage and lymph node metastasis. Our data indicate that lncRNA-BCHE regulates the growth, migration and invasion of breast cancer cells. In addition, we found that these functions are mediated, at least in part, by the regulation of integrin subunit beta 1 (ITGB1) levels. The expression of ITGB1 serves as a negative prognostic factor and metastasis risk predictor in breast cancer, irrespective of subtype and therapeutic regimen. In summary, our results suggest that lncRNA-BCHE is an oncogenic lncRNA enhancing the growth and metastatic potential of breast cancer cells, and a potential predictor of breast cancer metastatic progression. [ABSTRACT FROM AUTHOR]

In this paper, we study how the pro-social impact due to the vigilance by other individuals is conditioned by both environmental and evolutionary effects. To this aim, we consider a known model where agents play a Prisoner's Dilemma Game (PDG) among themselves and the pay-off matrix of an individual changes according to the number of neighbors that are "vigilant", i.e., how many neighbors watch out for her behavior. In particular, the temptation to defect decreases linearly with the number of vigilant neighbors. This model proved to support cooperation in specific conditions, and here we check its robustness with different topologies, microscopical update rules and initial conditions. By means of many numerical simulations and few theoretical considerations, we find in which situations the vigilance by the others is more effective in favoring cooperative behaviors and when its influence is weaker. [ABSTRACT FROM AUTHOR]

Copyright of MediSur is the property of Centro Provincial de Informacion de Ciencias Medicas de Cinfuegos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of MediSur is the property of Centro Provincial de Informacion de Ciencias Medicas de Cinfuegos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents an agent-based model designed to explore the development of cooperation in hunter-fisher-gatherer societies that face a dilemma of sharing an unpredictable resource that is randomly distributed in space. The model is a stylised abstraction of the Yamana society, which inhabited the channels and islands of the southernmost part of Tierra del Fuego (Argentina-Chile). According to ethnographic sources, the Yamana developed cooperative behaviour supported by an indirect reciprocity mechanism: whenever someone found an extraordinary confluence of resources, such as a beached whale, they would use smoke signals to announce their find, bringing people together to share food and exchange different types of social capital. The model provides insight on how the spatial concentration of beachings and agents’ movements in the space can influence cooperation. We conclude that the emergence of informal and dynamic communities that operate as a vigilance network preserves cooperation and makes defection very costly. [ABSTRACT FROM AUTHOR]

Background It is estimated, there are around 370 million indigenous people in the world. For 2017, 5.4% of tuberculosis cases in Colombia occurred in the indigenous population (IP), while in the Cauca region, it was 32% and only 19.3% of new cases were detected. This is the second region with the highest proportion of IP in Colombia. This could affect the fulfillment of the aims of the Colombian Strategic Plan Towards the End of TB. Our aim was to evaluate the pulmonary tuberculosis (PTB) care cascade in IP of Cauca and identified gaps in the cascade. Methods Mixed methods (explanatory sequential desing). In the quantitative phase, the care cascade was evaluated. In the qualitative phase we sought to explain the gaps in the cascade. Tuberculosis program information and semi-structured interviews were used. Results In the care cascade in IP, 202 cases of presumptive PTB (New smear-positive expected) were estimated. A gap of undiagnosed cases of 47.5% was found (n = 96). This gap was explained by poor quality of the Acid-Fast Bacillus Smear, failures in the identification of presumptive tuberculosis, limited access to diagnostic methods, poor training and high turnover of healthcare workers were identified as possible explanations. Conclusions The efforts of the tuberculosis control program in the IP should focus on reducing the gap of undiagnosed cases. Key messages The PTB in IP could affect the fulfillment of the goals of the program. Approximately half of the expected cases were not detected during 2016-2017. [ABSTRACT FROM AUTHOR]

We study the influence that resource availability has on cooperation in the context of hunter-gatherer societies. This paper proposes a model based on archaeological and ethnographic research on resource stress episodes, which exposes three different cooperative regimes according to the relationship between resource availability in the environment and population size. The most interesting regime represents moderate survival stress in which individuals coordinate in an evolutionary way to increase the probabilities of survival and reduce the risk of failing to meet the minimum needs for survival. Populations self-organise in an indirect reciprocity system in which the norm that emerges is to share the part of the resource that is not strictly necessary for survival, thereby collectively lowering the chances of starving. Our findings shed further light on the emergence and evolution of cooperation in hunter-gatherer societies. [ABSTRACT FROM AUTHOR]

In this work, we aim to contribute to the understanding of human prosocial behavior by studying the influence that a particular form of social pressure, "being watched," has on the evolution of cooperative behavior. We study how cooperation emerges in multiplex complex topologies by analyzing a particular bidirectionally coupled dynamics on top of a two-layer multiplex network (duplex). The coupled dynamics appears between the prisoner's dilemma game in a network and a threshold cascade model in the other. The threshold model is intended to abstract the behavior of a network of vigilant nodes that impose the pressure of being observed altering hence the temptation to defect of the dilemma. Cooperation or defection in the game also affects the state of a node of being vigilant. We analyze these processes on different duplex networks structures and assess the influence of the topology, average degree and correlated multiplexity, on the outcome of cooperation. Interestingly, we find that the social pressure of vigilance may impact cooperation positively or negatively, depending on the duplex structure, specifically the degree correlations between layers is determinant. Our results give further quantitative insights in the promotion of cooperation under social pressure. [ABSTRACT FROM AUTHOR]

A fundamental feature for understanding the diffusion of innovations through a social group is the manner in which we are influenced by our own social interactions. It is usually assumed that only direct interactions, those that form our social network, determine the dynamics of adopting innovations. Here, we test this assumption by experimentally and theoretically studying the role of direct and indirect influences in the adoption of innovations. We perform experiments specifically designed to capture the influence that an individual receives from their direct social ties as well as from those socially close to them, as a function of the separation they have in their social network. The results of 21 experimental sessions with more than 590 participants show that the rate of adoption of an innovation is significantly influenced not only by our nearest neighbors but also by the second and third levels of influences an adopter has. Using a mathematical model that accounts for both direct and indirect interactions in a network, we fit the experimental results and determine the way in which influences decay with social distance. The results indicate that the strength of peer pressure on an adopter coming from its second and third circles of influence is approximately two-third and one-third, respectively, relative to their closest neighbors. Our results strongly suggest that the adoption of an innovation is a complex process in which an individual feels significant pressure not only from their direct ties but also by those socially close to them., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

When some 'entities' are related by the 'features' they share they are amenable to a bipartite network representation. Plant-pollinator ecological communities, co-authorship of scientific papers, customers and purchases, or answers in a poll, are but a few examples. Analyzing clustering of such entities in the network is a useful tool with applications in many fields, like internet technology, recommender systems, or detection of diseases. The algorithms most widely applied to find clusters in bipartite networks are variants of modularity optimization. Here, we provide a hierarchical clustering algorithm based on a dissimilarity between entities that quantifies the probability that the features shared by two entities are due to mere chance. The algorithm performance is O(n^{2}) when applied to a set of n entities, and its outcome is a dendrogram exhibiting the connections of those entities. Through the introduction of a 'susceptibility' measure we can provide an 'optimal' choice for the clustering as well as quantify its quality. The dendrogram reveals further useful structural information though-like the existence of subclusters within clusters or of nodes that do not fit in any cluster. We illustrate the algorithm by applying it first to a set of synthetic networks, and then to a selection of examples. We also illustrate how to transform our algorithm into a valid alternative for one-mode networks as well, and show that it performs at least as well as the standard, modularity-based algorithms-with a higher numerical performance. We provide an implementation of the algorithm in python freely accessible from GitHub.

The interplay of social structure and cooperative behavior is under much scrutiny lately as behavior in social contexts becomes increasingly relevant for everyday life. Earlier experimental work showed that the existence of a social hierarchy, earned through competition, was detrimental for the evolution of cooperative behaviors. Here, we study the case in which individuals are ranked in a hierarchical structure based on their performance in a collective effort by having them play a Public Goods Game. In the first treatment, participants are ranked according to group earnings while, in the second treatment, their rankings are based on individual earnings. Subsequently, participants play asymmetric Prisoner's Dilemma games where higher-ranked players gain more than lower ones. Our experiments show that there are no detrimental effects of the hierarchy formed based on group performance, yet when ranking is assigned individually we observe a decrease in cooperation. Our results show that different levels of cooperation arise from the fact that subjects are interpreting rankings as a reputation which carries information about which subjects were cooperators in the previous phase. Our results demonstrate that noting the manner in which a hierarchy is established is essential for understanding its effects on cooperation.

Expectations, exerting influence through social norms, are a very strong candidate to explain how complex societies function. In the Dictator game (DG), people expect generous behavior from others even if they cannot enforce any sharing of the pie. Here we assume that people donate following their expectations, and that they update their expectations after playing a DG by reinforcement learning to construct a model that explains the main experimental results in the DG. Full agreement with the experimental results is reached when some degree of mismatch between expectations and donations is added into the model. These results are robust against the presence of envious agents, but affected if we introduce selfish agents that do not update their expectations. Our results point to social norms being on the basis of the generous behavior observed in the DG and also to the wide applicability of reinforcement learning to explain many strategic interactions.

Hemorragic dengue fever is a prevalent infection in many countries around the world. Myocarditis is a severe manifestation of dengue virus infection. With prompt intervention and an early diagnosis, the outcome of this condition can be improve. We report a adult patient with complicated dengue, myocarditis, cardiac and respiratory insufficiency with acute renal injury.