An autonomous debating system.

Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research1. Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans. We provide a complete description of the system's architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics, and a detailed account of the system's performance in its public debut against three expert human debaters. We also highlight the fundamental differences between debating with humans as opposed to challenging humans in game competitions, the latter being the focus of classical 'grand challenges' pursued by the AI research community over the past few decades. We suggest that such challenges lie in the 'comfort zone' of AI, whereas debating with humans lies in a different territory, in which humans still prevail, and for which novel paradigms are required to make substantial progress.

Genetic and chemical characterization of an EMS induced mutation in Cucumis melo CRTISO gene.

In order to broaden the available genetic variation of melon, we developed an ethyl methanesulfonate mutation library in an orange-flesh 'Charentais' type melon line that accumulates ß-carotene. One mutagenized M2 family segregated for a novel recessive trait, a yellow-orange fruit flesh ('yofI'). HPLC analysis revealed that 'yofI' accumulates pro-lycopene (tetra-cis-lycopene) as its major fruit pigment. The altered carotenoid composition of 'yofI' is associated with a significant change of the fruit aroma since cleavage of ß-carotene yields different apocarotenoids than the cleavage of pro-lycopene. Normally, pro-lycopene is further isomerized by CRTISO (carotenoid isomerase) to yield all-trans-lycopene, which is further cyclized to ß-carotene in melon fruit. Cloning and sequencing of 'yofI' CRTISO identified two mRNA sequences which lead to truncated forms of CRTISO. Sequencing of the genomic CRTISO identified an A-T transversion in 'yofI' which leads to a premature STOP codon. The early carotenoid pathway genes were up regulated in yofI fruit causing accumulation of other intermediates such as phytoene and ζ-carotene. Total carotenoid levels are only slightly increased in the mutant. Mutants accumulating pro-lycopene have been reported in both tomato and watermelon fruits, however, this is the first report of a non-lycopene accumulating fruit showing this phenomenon.

Predicting response to antiretroviral treatment by machine learning: the EuResist project.

For a long time, the clinical management of antiretroviral drug resistance was based on sequence analysis of the HIV genome followed by estimating drug susceptibility from the mutational pattern that was detected. The large number of anti-HIV drugs and HIV drug resistance mutations has prompted the development of computer-aided genotype interpretation systems, typically comprising rules handcrafted by experts via careful examination of in vitro and in vivo resistance data. More recently, machine learning approaches have been applied to establish data-driven engines able to indicate the most effective treatments for any patient and virus combination. Systems of this kind, currently including the Resistance Response Database Initiative and the EuResist engine, must learn from the large data sets of patient histories and can provide an objective and accurate estimate of the virological response to different antiretroviral regimens. The EuResist engine was developed by a European consortium of HIV and bioinformatics experts and compares favorably with the most commonly used genotype interpretation systems and HIV drug resistance experts. Next-generation treatment response prediction engines may valuably assist the HIV specialist in the challenging task of establishing effective regimens for patients harboring drug-resistant virus strains. The extensive collection and accurate processing of increasingly large patient data sets are eagerly awaited to further train and translate these systems from prototype engines into real-life treatment decision support tools.

The effectiveness of foot reflexology in reducing anxiety and duration of labor in primiparas: An open-label randomized controlled trial.

BACKGROUND AND PURPOSE: Reflexology alleviates anxiety and may shorten the duration of labor. We evaluated the effect of reflexology on anxiety level and duration of labor in primiparas with moderate-to-severe anxiety. MATERIALS AND METHODS: In this open-label randomized-controlled trial, primiparas with moderate-to-severe anxiety were randomized into one of two groups: addition of reflexology to usual care, or usual care only. The primary outcome was a change in the level of anxiety during reflexology treatment. RESULTS: Ninety-nine women were assigned to reflexology treatment while ninety received usual care only. A larger alleviation of anxiety was observed immediately after reflexology treatment as compared to the control group during the 30 min following group assignment. Reflexology did not affect the length of delivery. CONCLUSION: Foot reflexology had a positive short-term anxiolytic effect during labor in primiparas with moderate-to-severe anxiety but did not affect the duration of labor.