Cooperation and Contagion in Web-Based, Networked Public Goods Experiments

A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of web-based experiments, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous theoretical work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial seed players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation were contagious only to direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications

Betel nut and tobacco chewing; potential risk factors of cancer of oesophagus in Assam, India

Cancer of the oesophagus is the most commonly diagnosed cancer in males in Assam, in north-eastern India, and ranks second for females. The chewing of betel nut, with or without tobacco and prepared in various ways, is a common practice in the region and a case–control study has been designed to study the pattern of risk associated with different ways of preparing and chewing the nuts. 358 newly diagnosed male patients and 144 female have been interviewed together with 2 control subjects for each case chosen at random from among the attendants who accompanied patients to hospital. There were significant trends in risk ratios associated with the frequency of chewing each day, with the duration of chewing in years and with the age at which the habit was started that were apparent for both males and females and which remained significant after allowance was made for other known risk factors, notably tobacco smoking and alcohol consumption. The adjusted ratios, in comparison with non-chewers, were 13.3 M and 5.7 F for chewing more than 20 times a day, 10.6 M and 7.2 F for persons who had chewed for more than 20 years and 10.3 M and 5.3 F for those who had started before the age of 20. Among the different combinations of ingredients that were chewed the adjusted odds ratios were highest for those who had been using fermented betel nut with any form of tobacco (7.1 M and 3.6 F). The risk associated with tobacco smoking and alcohol consumption, which are high in some parts of the world, were less in Assam than those associated with the chewing of betel nut. © 2001 Cancer Research Campaign http://www.bjcancer.co

Acute effects of cigarette smoking on inflammation in healthy intermittent smokers

BACKGROUND: Chronic smoking is the main risk factor for chronic obstructive pulmonary disease. Knowledge on the response to the initial smoke exposures might enhance the understanding of changes due to chronic smoking, since repetitive acute smoke effects may cumulate and lead to irreversible lung damage. METHODS: We investigated acute effects of smoking on inflammation in 16 healthy intermittent smokers in an open randomised cross-over study. We compared effects of smoking of two cigarettes on inflammatory markers in exhaled air, induced sputum, blood and urine at 0, 1, 3, 6, 12, 24, 48, 96 and 192 hours and outcomes without smoking. All sputum and blood parameters were log transformed and analysed using a linear mixed effect model. RESULTS: Significant findings were: Smoking increased exhaled carbon monoxide between 0 and 1 hour, and induced a greater decrease in blood eosinophils and sputum lymphocytes between 0 and 3 hours compared to non-smoking. Compared to non-smoking, smoking induced a greater interleukin-8 release from stimulated blood cells between 0 and 3 hours, and a greater increase in sputum lymphocytes and neutrophils between 3 and 12 hours. CONCLUSION: We conclude that besides an increase in inflammation, as known from chronic smoking, there is also a suppressive effect of smoking two cigarettes on particular inflammatory parameters

Angiopoietin-1 inhibits tumour growth and ascites formation in a murine model of peritoneal carcinomatosis

Angiopoietin-1 is an important regulator of endothelial cell survival. Angiopoietin-1 also reduces vascular permeability mediated by vascular endothelial growth factor. The effects of angiopoietin-1 on tumour growth and angiogenesis are controversial. We hypothesised that angiopoietin-1 would decrease tumour growth and ascites formation in peritoneal carcinomatosis. Human colon cancer cells (KM12L4) were transfected with vector (pcDNA) alone (control) or vector containing angiopoietin-1 and injected into the peritoneal cavities of mice. After 30 days, the following parameters were measured: number of peritoneal nodules, ascites volume, and diameter of the largest tumour. Effects of angiopoietin-1 on vascular permeability were investigated using an intradermal Miles assay with conditioned media from transfected cells. Seven of the nine mice in the pcDNA group developed ascites (1.3±0.5 ml (mean±s.e.m.)), whereas no ascites was detectable in the angiopoietin-1 group (0 out of 10) (P<0.01). Number of peritoneal metastases (P<0.05), tumour volume, (P<0.05), vessel counts (P<0.01), and tumour cell proliferation (P<0.01) were significantly reduced in angiopoietin-1-expressing tumours. Conditioned medium from angiopoietin-1-transfected cells decreased vascular permeability more than did conditioned medium from control cells (P<0.05). Our results suggest that angiopoietin-1 is an important mediator of angiogenesis and vascular permeability and thus could theoretically serve as an anti-neoplastic agent for patients with carcinomatosis from colorectal cancer

A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems

The human brain intrinsically operates with a large number of synapses, more than 10(15). Therefore, one of the most critical requirements for constructing artificial neural networks (ANNs) is to achieve extremely dense synaptic array devices, for which the crossbar architecture containing an artificial synaptic node at each cross is indispensable. However, crossbar arrays suffer from the undesired leakage of signals through neighboring cells, which is a major challenge for implementing ANNs. In this work, we show that this challenge can be overcome by using Pt/TaOy/nanoporous (NP) TaOx/Ta memristor synapses because of their self-rectifying behavior, which is capable of suppressing unwanted leakage pathways. Moreover, our synaptic device exhibits high non-linearity (up to 10(4)), low synapse coupling (S.C, up to 4.00 x 10(-5)), acceptable endurance (5000 cycles at 85 degrees C), sweeping (1000 sweeps), retention stability and acceptable cell uniformity. We also demonstrated essential synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), and spiking-timing-dependent plasticity (STDP), and simulated the recognition accuracy depending on the S.C for MNIST handwritten digit images. Based on the average S.C (1.60 x 10(-4)) in the fabricated crossbar array, we confirmed that our memristive synapse was able to achieve an 89.08% recognition accuracy after only 15 training epochs

Personalized recurrence risk assessment following the birth of a child with a pathogenic de novo mutation

Following the diagnosis of a paediatric disorder caused by an apparently de novo mutation, a recurrence risk of 1-2% is frequently quoted due to the possibility of parental germline mosaicism; but for any specific couple, this figure is usually incorrect. We present a systematic approach to providing individualized recurrence risk. By combining locus-specific sequencing of multiple tissues to detect occult mosaicism with long-read sequencing to determine the parent-of-origin of the mutation, we show that we can stratify the majority of couples into one of seven discrete categories associated with substantially different risks to future offspring. Among 58 families with a single affected offspring (representing 59 de novo mutations in 49 genes), the recurrence risk for 35 (59%) was decreased below 0.1%, but increased owing to parental mixed mosaicism for 5 (9%)-that could be quantified in semen for paternal cases (recurrence risks of 5.6-12.1%). Implementation of this strategy offers the prospect of driving a major transformation in the practice of genetic counselling

The emergence of inequality in social groups: network structure and institutions affect the distribution of earnings in cooperation games

From small communities to entire nations and society at large, inequality in wealth, social status, and power is one of the most pervasive and tenacious features of the social world. What causes inequality to emerge and persist? In this study, we investigate how the structure and rules of our interactions can increase inequality in social groups. Specifically, we look into the effects of four structural conditions—network structure, network fluidity, reputation tracking, and punishment institutions—on the distribution of earnings in network cooperation games. We analyze 33 experiments comprising 96 experimental conditions altogether. We find that there is more inequality in clustered networks compared to random networks, in fixed networks compared to randomly rewired and strategically updated networks, and in groups with punishment institutions compared to groups without. Secondary analyses suggest that the reasons inequality emerges under these conditions may have to do with the fact that fixed networks allow exploitation of the poor by the wealthy and clustered networks foster segregation between the poor and the wealthy, while the burden of costly punishment falls onto the poor, leaving them poorer. Surprisingly, we do not find evidence that inequality is affected by reputation in a systematic way but this could be because reputation needs to play out in a particular network environment in order to have an effect. Overall, our findings suggest possible strategies and interventions to decrease inequality and mitigate its negative impact, particularly in the context of mid- and large-sized organizations and online communities

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Three dimensional structure directs T-cell epitope dominance associated with allergy

<p>Abstract</p> <p>Background</p> <p>CD4+ T-cell epitope immunodominance is not adequately explained by peptide selectivity in class II major histocompatibility proteins, but it has been correlated with adjacent segments of conformational flexibility in several antigens.</p> <p>Methods</p> <p>The published T-cell responses to two venom allergens and two aeroallergens were used to construct profiles of epitope dominance, which were correlated with the distribution of conformational flexibility, as measured by crystallographic B factors, solvent-accessible surface, COREX residue stability, and sequence entropy.</p> <p>Results</p> <p>Epitopes associated with allergy tended to be excluded from and lie adjacent to flexible segments of the allergen.</p> <p>Conclusion</p> <p>During the initiation of allergy, the N- and/or C-terminal ends of proteolytic processing intermediates were preferentially loaded into antigen presenting proteins for the priming of CD4+ T cells.</p