The evolution of information suppression in communicating robots with conflicting interests.

Reliable information is a crucial factor influencing decision-making and, thus, fitness in all animals. A common source of information comes from inadvertent cues produced by the behavior of conspecifics. Here we use a system of experimental evolution with robots foraging in an arena containing a food source to study how communication strategies can evolve to regulate information provided by such cues. The robots could produce information by emitting blue light, which the other robots could perceive with their cameras. Over the first few generations, the robots quickly evolved to successfully locate the food, while emitting light randomly. This behavior resulted in a high intensity of light near food, which provided social information allowing other robots to more rapidly find the food. Because robots were competing for food, they were quickly selected to conceal this information. However, they never completely ceased to produce information. Detailed analyses revealed that this somewhat surprising result was due to the strength of selection on suppressing information declining concomitantly with the reduction in information content. Accordingly, a stable equilibrium with low information and considerable variation in communicative behaviors was attained by mutation selection. Because a similar coevolutionary process should be common in natural systems, this may explain why communicative strategies are so variable in many animal species.

Relatedness influences signal reliability in evolving robots.

Communication is an indispensable component of animal societies, yet many open questions remain regarding the factors affecting the evolution and reliability of signalling systems. A potentially important factor is the level of genetic relatedness between signallers and receivers. To quantitatively explore the role of relatedness in the evolution of reliable signals, we conducted artificial evolution over 500 generations in a system of foraging robots that can emit and perceive light signals. By devising a quantitative measure of signal reliability, and comparing independently evolving populations differing in within-group relatedness, we show a strong positive correlation between relatedness and reliability. Unrelated robots produced unreliable signals, whereas highly related robots produced signals that reliably indicated the location of the food source and thereby increased performance. Comparisons across populations also revealed that the frequency for signal production-which is often used as a proxy of signal reliability in empirical studies on animal communication-is a poor predictor of signal reliability and, accordingly, is not consistently correlated with group performance. This has important implications for our understanding of signal evolution and the empirical tools that are used to investigate communication.

Using robots to understand social behaviour.

A major challenge in studying social behaviour stems from the need to disentangle the behaviour of each individual from the resulting collective. One way to overcome this problem is to construct a model of the behaviour of an individual, and observe whether combining many such individuals leads to the predicted outcome. This can be achieved by using robots. In this review we discuss the strengths and weaknesses of such an approach for studies of social behaviour. We find that robots-whether studied in groups of simulated or physical robots, or used to infiltrate and manipulate groups of living organisms-have important advantages over conventional individual-based models and have contributed greatly to the study of social behaviour. In particular, robots have increased our understanding of self-organization and the evolution of cooperative behaviour and communication. However, the resulting findings have not had the desired impact on the biological community. We suggest reasons for why this may be the case, and how the benefits of using robots can be maximized in future research on social behaviour.

Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots.

One of the key innovations during the evolution of life on earth has been the emergence of efficient communication systems, yet little is known about the causes and consequences of the great diversity within and between species. By conducting experimental evolution in 20 independently evolving populations of cooperatively foraging simulated robots, we found that historical contingency in the occurrence order of novel phenotypic traits resulted in the emergence of two distinct communication strategies. The more complex foraging strategy was less efficient than the simpler strategy. However, when the 20 populations were placed in competition with each other, the populations with the more complex strategy outperformed the populations with the less complex strategy. These results demonstrate a tradeoff between communication efficiency and robustness and suggest that stochastic events have important effects on signal evolution and the outcome of competition between distinct populations.

Implementation of robotic laparoscopic cholecystectomy in a university hospital.

BACKGROUND: Robot surgery is a further step towards new potential developments in minimally invasive surgery. Surgeons must keep abreast of these new technologies and learn their limits and possibilities. Robot-assisted laparoscopic cholecystectomy has not yet been performed in our institution. The purpose of this report is to present the pathway of implementation of robotic laparoscopic cholecystectomy in a university hospital. METHODS: The Zeus(R) robot system was used. Experimental training was performed on animals. The results of our experimental training allowed us to perform our first two clinical cases. RESULTS: Robot arm set-up and trocar placement required 53 and 35 minutes. Operative time were 59 and 45 minutes respectively. The overall operative time was 112 and 80 minutes, respectively. There were no intraoperative complications. Patients were discharged from the hospital after an overnight stay. CONCLUSION: Robotic laparoscopic cholecystectomy is safe and patient recovery similar to those of standard laparoscopy. At present, there are no advantages of robotic over conventional surgery. Nevertheless, robots have the potential to revolutionise the way surgery is performed. Robot surgery is not reserved for a happy few. This technology deserves more attention because it has the potential to change the way surgery is performed.

Genome-wide association study of major recurrent depression in the U.K. population.

OBJECTIVE: Studies of major depression in twins and families have shown moderate to high heritability, but extensive molecular studies have failed to identify susceptibility genes convincingly. To detect genetic variants contributing to major depression, the authors performed a genome-wide association study using 1,636 cases of depression ascertained in the U.K. and 1,594 comparison subjects screened negative for psychiatric disorders. METHOD: Cases were collected from 1) a case-control study of recurrent depression (the Depression Case Control [DeCC] study; N=1346), 2) an affected sibling pair linkage study of recurrent depression (probands from the Depression Network [DeNT] study; N=332), and 3) a pharmacogenetic study (the Genome-Based Therapeutic Drugs for Depression [GENDEP] study; N=88). Depression cases and comparison subjects were genotyped at Centre National de Génotypage on the Illumina Human610-Quad BeadChip. After applying stringent quality control criteria for missing genotypes, departure from Hardy-Weinberg equilibrium, and low minor allele frequency, the authors tested for association to depression using logistic regression, correcting for population ancestry. RESULTS: Single nucleotide polymorphisms (SNPs) in BICC1 achieved suggestive evidence for association, which strengthened after imputation of ungenotyped markers, and in analysis of female depression cases. A meta-analysis of U.K. data with previously published results from studies in Munich and Lausanne showed some evidence for association near neuroligin 1 (NLGN1) on chromosome 3, but did not support findings at BICC1. CONCLUSIONS: This study identifies several signals for association worthy of further investigation but, as in previous genome-wide studies, suggests that individual gene contributions to depression are likely to have only minor effects, and very large pooled analyses will be required to identify them.

Robotic Gait Training Is not Superior to Conventional Treadmill Training in Parkinson Disease: A Single-Blind Randomized Controlled Trial.

BACKGROUND: The use of robots for gait training in Parkinson disease (PD) is growing, but no evidence points to an advantage over the standard treadmill. METHODS: In this randomized, single-blind controlled trial, participants aged <75 years with early-stage PD (Hoehn-Yahr <3) were randomly allocated to 2 groups: either 30 minutes of gait training on a treadmill or in the Lokomat for 3 d/wk for 4 weeks. Patients were evaluated by a physical therapist blinded to allocation before and at the end of treatment and then at the 3- and 6-month follow-up. The primary outcome measure was the 6-minute walk test. RESULTS: Of 334 screened patients, the authors randomly allocated 30 to receive gait training with treadmill or the Lokomat. At baseline, the 2 groups did not differ. At the 6-month follow-up, both groups had improved significantly in the primary outcome measure (treadmill: mean = 490.95 m, 95% confidence interval [CI] = 448.56-533.34, P = .0006; Lokomat: 458.6 m, 95% CI = 417.23-499.96, P = .01), but no significant differences were found between the 2 groups (P = .53). DISCUSSION: Robotic gait training with the Lokomat is not superior to treadmill training in improving gait performance in patients with PD. Both approaches are safe, with results maintained for up to 6 months.

A quantitative test of Hamilton's rule for the evolution of altruism.

The evolution of altruism is a fundamental and enduring puzzle in biology. In a seminal paper Hamilton showed that altruism can be selected for when rb - c > 0, where c is the fitness cost to the altruist, b is the fitness benefit to the beneficiary, and r is their genetic relatedness. While many studies have provided qualitative support for Hamilton's rule, quantitative tests have not yet been possible due to the difficulty of quantifying the costs and benefits of helping acts. Here we use a simulated system of foraging robots to experimentally manipulate the costs and benefits of helping and determine the conditions under which altruism evolves. By conducting experimental evolution over hundreds of generations of selection in populations with different c/b ratios, we show that Hamilton's rule always accurately predicts the minimum relatedness necessary for altruism to evolve. This high accuracy is remarkable given the presence of pleiotropic and epistatic effects as well as mutations with strong effects on behavior and fitness (effects not directly taken into account in Hamilton's original 1964 rule). In addition to providing the first quantitative test of Hamilton's rule in a system with a complex mapping between genotype and phenotype, these experiments demonstrate the wide applicability of kin selection theory.