Personal Reflections on Some Early Work in Evolving Strategies in the Iterated Prisoner's Dilemma.

On the occasion of the 30-year anniversary of the Evolutionary Computation journal, I was invited by Professor Hart to offer some reflections on the article on evolving behaviors in the iterated prisoner's dilemma that I contributed to its first issue in 1993. It's an honor to do so. I would like to thank Professor Ken De Jong, the journal's first editor-in-chief, for his vision in creating the journal, and the editors who have followed and maintained that vision. This article contains some personal reflections on the topic and the field as a whole.

Agonist-specific desensitization of PGE2-stimulated cAMP signaling due to upregulated phosphodiesterase expression in human lung fibroblasts.

Pulmonary fibrosis is characterized by fibroblasts persisting in an activated form, producing excessive fibrous material that destroys alveolar structure. The second messenger molecule cyclic 3',5'-adenosine monophosphate (cAMP) has antifibrotic properties, and prostaglandin E2 (PGE2) can stimulate cAMP production through prostaglandin E (EP)2 and EP4 receptors. Although EP receptors are attractive therapeutic targets, the effects of long-term exposure to PGE2 have not been characterized. To determine the effects of long-term exposure of lung fibroblasts to PGE2, human fetal lung (HFL)-1 cells were treated for 24 h with 100 nM PGE2 or other cAMP-elevating agents. cAMP levels stimulated by acute exposure to PGE2 were measured using a fluorescent biosensor. Pretreatment for 24 h with PGE2 shifted the concentration-response curve to PGE2 rightward by approximately 22-fold but did not affect responses to the beta-adrenoceptor agonist isoproterenol. Neither isoproterenol nor forskolin pretreatment altered PGE2 responses, implying that other cAMP-elevating agents do not induce desensitization. Use of EP2- and EP4-selective agonists and antagonists suggested that PGE2-stimulated cAMP responses in HFL-1 cells are mediated by EP2 receptors. EP2 receptors are resistant to classical mechanisms of agonist-specific receptor desensitization, so we hypothesized that increased PDE activity mediates the loss of signaling after PGE2 pretreatment. PGE2 treatment upregulated messenger RNA for PDE3A, PDE3B, PDE4B, and PDE4D and increased overall PDE activity. The PDE4 inhibitor rolipram partially reversed PGE2-mediated desensitization and PDE4 activity was increased, but rolipram did not alter responses to isoproterenol. The PDE3 inhibitor cilostazol had minimal effect. These results show that long-term exposure to PGE2 causes agonist-specific desensitization of EP2 receptor-stimulated cAMP signaling through the increased expression of PDE isozymes, most likely of the PDE4 family.

Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review.

Clinical trials are time consuming, expensive, and often burdensome on patients. Clinical trials can fail for many reasons. This survey reviews many of these reasons and offers insights on opportunities for improving the likelihood of creating and executing successful clinical trials. Literature from the past 30 years was reviewed for relevant data. Common patterns in reported successful trials are identified, including factors regarding the study site, study coordinator/investigator, and the effects on participating patients. Specific instances where artificial intelligence can help improve clinical trials are identified.

Relevance of Quality Measurement to Integrative Healthcare in the United States.

With the advent of new models for payment and delivery of healthcare services, the use of quality measures for continual improvement of clinical healthcare is now an integral feature of medical practice in the United States. However, quality measurement and quality improvement activities are not common practice among integrative health providers. This article discusses the import and application of quality measurement to the practice of integrative healthcare. It reviews developments in the healthcare quality improvement movement, explores the relevance of quality measures to integrative healthcare, describes examples of the current use of quality measures in integrative health practice, discusses discriminatory policies that may prevent participation in quality improvement by integrative health practitioners, and makes recommendations for practice and policy.

Incorporating Yoga Therapy into Primary Care: The Casey Health Institute.

Individuals seek complementary and alternative medicine (CAM) for a variety of health conditions, and yoga is a popular CAM modality. Over the past few decades, yoga has become incorporated into hundreds of healthcare facilities, most commonly in large university medical centers. While research has shown yoga to be effective in reducing symptoms and improving outcomes in chronic health conditions, most patients seek yoga therapy on their own, as few primary care practitioners have incorporated yoga therapy into their practices. The purpose of this article is to describe the efforts of the Casey Health Institute to incorporate yoga therapy into their primary care integrative medicine center. At Casey Health, a full-time Clinical Yoga Specialist works alongside the physicians, nurses, and CAM providers in delivering care to a wide variety of patients. The majority of referrals to yoga therapy have been for pain-related musculoskeletal conditions, as well as hypertension, headaches, anxiety, depression, and sleep disturbances. Most patients attend weekly 60-minute individual sessions, and the Clinical Yoga Specialist stays in touch with the patient between appointments via telephone and email. T h e Clinical Yoga Specialist has become an integral part of Casey Health, participating in collaborative medical appointments in which two CAM practitioners provide simultaneous treatments to a patient. She also participates in the clinic's ongoing weight loss program. The Clinical Yoga Specialist spends one morning each week "floating" in the clinic, when she is on-call to the practitioners to assist in treatment and/or to introduce a yoga therapy experience to the patients. These brief interventions introduce the patients to the therapeutic benefits of yoga, while simultaneously demonstrating yoga's effectiveness to the healthcare providers. Casey Health has developed a unique teacher training program whose faculty includes senior Iyengar yoga teachers as well as physicians and CAM practitioners. Casey Health is incorporating clinical outcomes into their electronic medical record that can be used internally to assess comparable effectiveness and cost effectiveness of the different treatment modalities such as yoga therapy, providing evidence that eventually may lead to yoga therapy becoming an accepted treatment that is eligible for insurance reimbursement.

Simulating natural selection as a culling mechanism on finite populations with the hawk-dove game.

The behaviors of individuals and species are often explained in terms of evolutionary stable strategies (ESSs). The analysis of ESSs determines which, if any, combinations of behaviors cannot be invaded by alternative strategies. Two assumptions required to generate an ESS (i.e., an infinite population and payoffs described only on the average) do not hold under natural conditions. Previous experiments indicated that under more realistic conditions of finite populations and stochastic payoffs, populations may evolve in trajectories that are unrelated to an ESS, even in very simple games. The simulations offered here extend earlier research by employing truncation selection with random parental selection in a hawk-dove game. Payoffs are determined in pairwise contests using either the expected outcome, or the result of a random variable. In each case, however, the mean fraction of hawks over many generations and across many independent trials does not conform to the expected ESS. Implications of these results and philosophical underpinnings of ESS theory are offered.

A platform for evolving intelligently interactive adversaries.

Entertainment software developers face significant challenges in designing games with broad appeal. One of the challenges concerns creating nonplayer (computer-controlled) characters that can adapt their behavior in light of the current and prospective situation, possibly emulating human behaviors. This adaptation should be inherently novel, unrepeatable, yet within the bounds of realism. Evolutionary algorithms provide a suitable method for generating such behaviors. This paper provides background on the entertainment software industry, and details a prior and current effort to create a platform for evolving nonplayer characters with genetic and behavioral traits within a World War I combat flight simulator.

Discovery of RNA structural elements using evolutionary computation.

RNA molecules fold into characteristic secondary and tertiary structures that account for their diverse functional activities. Many of these RNA structures, or certain structural motifs within them, are thought to recur in multiple genes within a single organism or across the same gene in several organisms and provide a common regulatory mechanism. Search algorithms, such as RNAMotif, can be used to mine nucleotide sequence databases for these repeating motifs. RNAMotif allows users to capture essential features of known structures in detailed descriptors and can be used to identify, with high specificity, other similar motifs within the nucleotide database. However, when the descriptor constraints are relaxed to provide more flexibility, or when there is very little a priori information about hypothesized RNA structures, the number of motif 'hits' may become very large. Exhaustive methods to search for similar RNA structures over these large search spaces are likely to be computationally intractable. Here we describe a powerful new algorithm based on evolutionary computation to solve this problem. A series of experiments using ferritin IRE and SRP RNA stem-loop motifs were used to verify the method. We demonstrate that even when searching extremely large search spaces, of the order of 10(23) potential solutions, we could find the correct solution in a fraction of the time it would have taken for exhaustive comparisons.

Quantitative structure-activity relationships by evolved neural networks for the inhibition of dihydrofolate reductase by pyrimidines.

Evolutionary computation provides a useful method for training neural networks in the face of multiple local optima. This paper begins with a description of methods for quantitative structure activity relationships (QSAR). An overview of artificial neural networks for pattern recognition problems such as QSAR is presented and extended with the description of how evolutionary computation can be used to evolve neural networks. Experiments are conducted to examine QSAR for the inhibition of dihydrofolate reductase by pyrimidines using evolved neural networks. Results indicate the utility of evolutionary algorithms and neural networks for the predictive task at hand. Furthermore, results that are comparable or perhaps better than those published previously were obtained using only a small fraction of the previously required degrees of freedom.