Cooperation, Retaliation and Forgiveness in Revision Games

Revision game is a very new model formulating the real-time situation where players dynamically prepare and revise their actions in advance before a deadline when payoffs are realized. It is at the cutting edge of dynamic game theory and can be applied in many real-world scenarios, such as eBay auction, stock market, election, online games, crowdsourcing, etc. In this work, we novelly identify a class of strategies for revision games which are called Limited Retaliation strategies. An limited retaliation strategy stipulates that, (1) players first follow a recommended cooperative plan; (2) if anyone deviates from the plan, the limited retaliation player retaliates by using the defection action for a limited duration; (3) after the retaliation, the limited retaliation player returns to the cooperative plan. A limited retaliation strategy has three key features. It is cooperative, sustaining a high level of social welfare. It is vengeful, deterring the opponent from betrayal by threatening with a future retaliation. It is yet forgiving, since it resumes cooperation after a proper retaliation. The cooperativeness and vengefulness make it constitute cooperative subgame perfect equilibrium, while the forgiveness makes it tolerate occasional mistakes. limited retaliation strategies show significant advantages over Grim Trigger, which is currently the only known strategy for revision games. Besides its contribution as a new robust and welfare-optimizing equilibrium strategy, our results about limited retaliation strategy can also be used to explain how easy cooperation can happen, and why forgiveness emerges in real-world multi-agent interactions. In addition, limited retaliation strategies are simple to derive and computationally efficient, making it easy for algorithm design and implementation in many multi-agent systems

Correlation between porosity, amorphous phase and CMAS corrosion behaviour of LaMgAl11O19 thermal barrier coatings

Calcium-magnesium-alumino-silicate (CMAS) attack is one of the significant failure mechanisms of thermal barrier coatings (TBCs), which can facilitate TBC’s degradation at elevated temperatures. To clarify the correlation between the porosity, CMAS corrosion behaviour, lanthanum magnesium hexaluminate (LaMgAl11O19, LMA) TBCs were prepared by atmospheric plasma spraying (APS) and then heat-treated at 1173K and 1523K, respectively. For comparison, LMA tablets were prepared by mechanical and cold isostatic pressing. CMAS attack at 1523K was carried out both for LMA tablets and LMA coatings. Their microstructure, phase composition, and crystallization behavior after CMAS attack were investigated using scanning electron microscopy (SEM) equipped with energy dispersive spectroscopy (EDS), X-ray diffraction as well as differential scanning calorimetry (DSC). The results indicated that CMAS attack was arrested for LMA tablets due to the formation of a dense crystalline layer induced by the chemical interactions between LMA and CMAS glass, while the as-sprayed LMA coatings were completely penetrated by molten CMAS due to the presence of amorphous phase and connected pores. Although the isothermal heat-treatment promoted a crystallization of LMA coatings, much vertical cracks formed during heat-treatment. The heat-treated LMA coatings suffered a severer CMAS attack than the as-sprayed one, since the vertical cracks inevitably provided efficient infiltration paths for molten CMAS

Boundary domain genes were recruited to suppress bract growth and promote branching in maize

Grass inflorescence development is diverse and complex and involves sophisticated but poorly understood interactions of genes regulating branch determinacy and leaf growth. Here, we use a combination of transcript profiling and genetic and phylogenetic analyses to investigate tasselsheath1 (tsh1) and tsh4, two maize genes that simultaneously suppress inflorescence leaf growth and promote branching. We identify a regulatory network of inflorescence leaf suppression that involves the phase change gene tsh4 upstream of tsh1 and the ligule identity gene liguleless2 (lg2). We also find that a series of duplications in the tsh1 gene lineage facilitated its shift from boundary domain in nongrasses to suppressed inflorescence leaves of grasses. Collectively, these results suggest that the boundary domain genes tsh1 and lg2 were recruited to inflorescence leaves where they suppress growth and regulate a nonautonomous signaling center that promotes inflorescence branching, an important component of yield in cereal grasses

TFPI-2 is a putative tumor suppressor gene frequently inactivated by promoter hypermethylation in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Epigenetic silencing of tumor suppressor genes play important roles in NPC tumorgenesis. Tissue factor pathway inhibitor-2 (TFPI-2), is a protease inhibitor. Recently, <it>TFPI-2 </it>was suggested to be a tumor suppressor gene involved in tumorigenesis and metastasis in some cancers. In this study, we investigated whether <it>TFPI-2 </it>was inactivated epigenetically in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Transcriptional expression levels of <it>TFPI-2 </it>was evaluated by RT-PCR. Methylation status were investigated by methylation specific PCR and bisulfate genomic sequencing. The role of <it>TFPI-2 </it>as a tumor suppressor gene in NPC was addressed by re-introducing <it>TFPI-2 </it>expression into the NPC cell line CNE2.</p> <p>Results</p> <p><it>TFPI-2 </it>mRNA transcription was inactivated in NPC cell lines. <it>TFPI-2 </it>was aberrantly methylated in 66.7% (4/6) NPC cell lines and 88.6% (62/70) of NPC primary tumors, but not in normal nasopharyngeal epithelia. <it>TFPI-2 </it>expression could be restored in NPC cells after demethylation treatment. Ectopic expression of TFPI-2 in NPC cells induced apoptosis and inhibited cell proliferation, colony formation and cell migration.</p> <p>Conclusions</p> <p>Epigenetic inactivation of <it>TFPI-2 </it>by promoter hypermethylation is a frequent and tumor specific event in NPC. <it>TFPI-2 </it>might be considering as a putative tumor suppressor gene in NPC.</p

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.</p> <p>Results</p> <p>We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.</p> <p>Conclusions</p> <p>The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.</p> <p>Availability</p> <p>Datasets and software are available at <url>http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm</url>.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

The Space of Jumping Emerging Patterns and Its Incremental Maintenance

The concept of jumping emerging patterns (JEPs) has been proposed to describe those discriminating features which only occur in the positive training instances but do not occur in the negative class at all; JEPs have been used to construct classifiers which generally provide better accuracy than the state-of-the-art classifiers such as C4.5. The algorithms for maintaining the space of jumping emerging patterns (JEP space) are presented in this paper. We prove that JEP spaces satisfy the property of convexity. Therefore JEP spaces can be concisely represented by two bounds: consisting respectively of the most general elements and the most specific elements. In response to insertion of new training instances, a JEP space is modified by operating on its boundary elements and the boundary elements of the JEP spaces associated with the new instances. This strategy completely avoids the need to go back to the most initial step to build the new JEP space. In addition, our maintenance algorithms can well handle such other cases as deletion of instances, insertion of new attributes, and deletion of attributes