Social dynamics and cooperation: The case of nonhuman primates and its implications for human behavior.

The social factors that influence cooperation have remained largely uninvestigated but have the potential to explain much of the variation in cooperative behavior observed in the natural world. We show here that certain dimensions of the social environment, namely the size of the social group, the degree of social tolerance expressed, the structure of the dominance hierarchy, and the patterns of dispersal, may influence the emergence and stability of cooperation in predictable ways. Furthermore, the social environment experienced by a species over evolutionary time will have shaped their cognition to provide certain strengths and strategies that are beneficial in their species' social world. These cognitive adaptations will in turn impact the likelihood of cooperating in a given social environment. Experiments with one primate species, the cottontop tamarin, illustrate how social dynamics may influence emergence and stability of cooperative behavior in this species. We then take a more general viewpoint and argue that the hypotheses presented here require further experimental work and the addition of quantitative modeling to obtain a better understanding of how social dynamics influence the emergence and stability of cooperative behavior in complex systems. We conclude by pointing out subsequent specific directions for models and experiments that will allow relevant advances in the understanding of the emergence of cooperation.Ángel Sánchez was partially supported by Ministerio de Economía y Competitividad (Spain) through grants MOSAICO, PRODIEVO and Complexity-NET RESINEE, and by Comunidad de Madrid (Spain) through grant MODELICO-CM.Publicad

Hierarchy is Detrimental for Human Cooperation

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.We are indebted to Luis Quevedo for discussions about the origin of rank societies. We thank Lydia Hopper, Antonio Cabrales, Gary Charness, Arno Riedl, Jordi Brandts, and Gross Jörg for feedback on an earlier version of this manuscript. We thank the anonymous reviewers for feedback that improved this manuscript. This work was supported by the Netherlands Organisation for Scientific Research, by Universidad Carlos III de Madrid, MEC Spain (ECO2013-46550-R) and the Generalitat Valenciana (PROMETEOII/2014/054)

Collaborative hierarchy maintains cooperation in asymmetric games

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.A.A. gratefully acknowledges the financial support of the Swiss National Science Foundation under Grants No. P2LAP1-161864 and P300P1-171537. This work was also supported by the EU through FET-Proactive Project DOLFINS (contract no. 640772, A.S.) and FET-Open Project IBSEN (contract no. 662725, A.S.), and by the Ministerio de Econom a y Competitividad of Spain (grant no. FIS2015-64349-P, A.S.) (MINECO/FEDER, UE)

Association of transcript levels of 10 established or candidate-biomarker gene targets with cancerous versus non-cancerous prostate tissue from radical prostatectomy specimens

Objectives: The benefits of PSA (prostate specific antigen)-testing in prostate cancer remain controversial with a consequential need for validation of additional biomarkers. We used highly standardized reverse-transcription (RT)-PCR assays to compare transcript levels of 10 candidate cancer marker genes - BMP6, FGF-8b, KLK2, KLK3, KLK4, KLK15, MSMB, PCA3, PSCA and Trpm8 - in carefully ascertained non-cancerous versus cancerous prostate tissue from patients with clinically localized prostate cancer treated by radical prostatectomy. Design and methods: Total RNA was isolated from fresh frozen prostate tissue procured immediately after resection from two separate areas in each of 87 radical prostatectomy specimens. Subsequent histopathological assessment classified 86 samples as cancerous and 88 as histologically benign prostate tissue. Variation in total RNA recovery was accounted for by using external and internal standards and enabled us to measure transcript levels by RT-PCR in a highly quantitative manner. Results: Of the ten genes, there were significantly higher levels only of one of the less abundant transcripts, PCA3, in cancerous versus non-cancerous prostate tissue whereas PSCA mRNA levels were significantly lower in cancerous versus histologically benign tissue. Advanced pathologic stage was associated with significantly higher expression of KLK15 and PCA3 mRNAs. Median transcript levels of the most abundantly expressed genes (i.e. MSMB, KLK3, KLK4 and KLK2) in prostate tissue were up to 10(5)-fold higher than those of other gene targets. Conclusions: PCA3 expression was associated with advanced pathological stage but the magnitude of overexpression of PCA3 in cancerous versus non-cancerous prostate tissue was modest compared to previously reported data. (C) 2013 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved

Prostate specific antigen concentration at age 60 and death or metastasis from prostate cancer: case-control study

Objective To determine the relation between concentrations of prostate specific antigen at age 60 and subsequent diagnosis of clinically relevant prostate cancer in an unscreened population to evaluate whether screening for prostate cancer and chemoprevention could be stratified by risk

Cancer-associated Changes in the Expression of TMPRSS2-ERG, PCA3, and SPINK1 in Histologically Benign Tissue From Cancerous vs Noncancerous Prostatectomy Specimens.

To investigate whether messenger ribonucleic acid (mRNA) expression of TMPRSS2-ERG fusion gene, a suggested prostate cancer (PCa) biomarker, was specific to cancerous lesions alone and to study the expression of SPINK1 and PCA3 mRNAs in the same cohort to also explore the proposed mutual exclusivity of TMPRSS2-ERG and SPINK1 expression

Data and programming code from the studies on the learning curve for radical prostatectomy

Our group analyzed a multi-institutional data set to address the question of how the outcomes of surgery for prostate cancer are affected by surgeon-specific factors. The cohort consists of 9076 patients treated by open radical prostatectomy at one of four US academic institutions 1987 - 2003. The primary analyses focused on 7765 patients without neoadjuvant therapy. The most well-known finding is that of a surgical "learning curve", with rates of prostate cancer cure strongly dependent on surgeon experience. In this "data note", we provide the raw data set, as well as well-annotated programming code for the main analyses. Data include markers of cancer severity (stage, grade and prostate-specific antigen level), cancer outcome, and surgeon variables such as training and experience

A panel of kallikrein markers can predict outcome of prostate biopsy following clinical work-up: an independent validation study from the European Randomized Study of Prostate Cancer screening, France

<p>Abstract</p> <p>Background</p> <p>We have previously shown that a panel of kallikrein markers - total prostate-specific antigen (PSA), free PSA, intact PSA and human kallikrein-related peptidase 2 (hK2) - can predict the outcome of prostate biopsy in men with elevated PSA. Here we investigate the properties of our panel in men subject to clinical work-up before biopsy.</p> <p>Methods</p> <p>We applied a previously published predictive model based on the kallikrein panel to 262 men undergoing prostate biopsy following an elevated PSA (≥ 3 ng/ml) and further clinical work-up during the European Randomized Study of Prostate Cancer screening, France. The predictive accuracy of the model was compared to a "base" model of PSA, age and digital rectal exam (DRE).</p> <p>Results</p> <p>83 (32%) men had prostate cancer on biopsy of whom 45 (54%) had high grade disease (Gleason score 7 or higher). Our model had significantly higher accuracy than the base model in predicting cancer (area-under-the-curve [AUC] improved from 0.63 to 0.78) or high-grade cancer (AUC increased from 0.77 to 0.87). Using a decision rule to biopsy those with a 20% or higher risk of cancer from the model would reduce the number of biopsies by nearly half. For every 1000 men with elevated PSA and clinical indication for biopsy, the model would recommend against biopsy in 61 men with cancer, the majority (≈80%) of whom would have low stage <it>and </it>low grade disease at diagnosis.</p> <p>Conclusions</p> <p>In this independent validation study, the model was highly predictive of prostate cancer in men for whom the decision to biopsy is based on both elevated PSA and clinical work-up. Use of this model would reduce a large number of biopsies while missing few cancers.</p

Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study

<p>Abstract</p> <p>Background</p> <p>A common feature of diagnostic research is that results for a diagnostic gold standard are available primarily for patients who are positive for the test under investigation. Data from such studies are subject to what has been termed "verification bias". We evaluated statistical methods for verification bias correction when there are few false negatives.</p> <p>Methods</p> <p>A simulation study was conducted of a screening study subject to verification bias. We compared estimates of the area-under-the-curve (AUC) corrected for verification bias varying both the rate and mechanism of verification.</p> <p>Results</p> <p>In a single simulated data set, varying false negatives from 0 to 4 led to verification bias corrected AUCs ranging from 0.550 to 0.852. Excess variation associated with low numbers of false negatives was confirmed in simulation studies and by analyses of published studies that incorporated verification bias correction. The 2.5^th– 97.5^thcentile range constituted as much as 60% of the possible range of AUCs for some simulations.</p> <p>Conclusion</p> <p>Screening programs are designed such that there are few false negatives. Standard statistical methods for verification bias correction are inadequate in this circumstance.</p

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p