Bridging the genetical environmental gap in Parkinson's disease through epigenetics

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting 2% of the population over 60 years old, yet the exact molecular mechanism underlying its pathogenesis remains elusive. PD is a multifactorial disease with genetic and environmental factors intricately associated. Recently, epigenetic mechanisms have been recognized as potential mediators of environmental factors participating in the pathogenesis of PD. Epigenetics refer to the heritable changes in gene expression that do not involve changes to the underlying DNA sequence. Altered epigenetic mechanisms have been attributed to PD, Alzheimer’s and Huntington’s disease. Several studies have shown that DNA methylation, histone modifications and non-coding RNAs mechanisms contribute to the pathogenesis of PD. Accumulation of toxic metals such as manganese and iron, due to abnormal environmental exposure or increased dietary intake, can impact varied components of the epigenetic machinery through free radical formation. Current pharmacological agents only provide symptomatic relief, of which levodopa still remains the gold standard. However, drugs that halt or delay progression of PD are still lacking. In recent years, there has been considerable progress in the development of epigenetic drugs as a novel therapeutic modality in the management of PD. Cell replacement therapy is a promising avenue for the treatment of PD with scientific research making great progress in the development of Induced Pluripotent Stem Cells (iPSCs) to produce midbrain dopamine phenotypes. With direct access to the neurons that are affected in PD, the pace of discovery should speed up and the cure for PD should become an attainable goal.peer-reviewe

Diagnostic application of padlock probes—multiplex detection of plant pathogens using universal microarrays

Padlock probes (PLPs) are long oligonucleotides, whose ends are complementary to adjacent target sequences. Upon hybridization to the target, the two ends are brought into contact, allowing PLP circularization by ligation. PLPs provide extremely specific target recognition, which is followed by universal amplification and microarray detection. Since target recognition is separated from downstream processing, PLPs enable the development of flexible and extendable diagnostic systems, targeting diverse organisms. To adapt padlock technology for diagnostic purposes, we optimized PLP design to ensure high specificity and eliminating ligation on non-target sequences under real-world assay conditions. We designed and tested 11 PLPs to target various plant pathogens at the genus, species and subspecies levels, and developed a prototype PLP-based plant health chip. Excellent specificity was demonstrated toward the target organisms. Assay background was determined for each hybridization using a no-target reference sample, which provided reliable and sensitive identification of positive samples. A sensitivity of 5 pg genomic DNA and a dynamic range of detection of 100 were observed. The developed multiplex diagnostic system was validated using genomic DNAs of characterized isolates and artificial mixtures thereof. The demonstrated system is adaptable to a wide variety of applications ranging from pest management to environmental microbiology

A recursive paradigm for aligning observed behavior of large structured process models

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft

Life-long course and molecular characterization of the original Dutch family with epidermolysis bullosa simplex with muscular dystrophy due to a homozygous novel plectin point mutation

Plectin is one of the largest and most versatile cytolinker proteins known. Cloned and sequenced in 1991, it was later shown to have nonsense mutations in recessive epidermolysis bullosa with muscular dystrophy. A dominant mutation in the gene was found to cause epidermolysis bullosa simplex Ogna without muscular dystrophy. Here we report the DNA sequencing of the plectin gene (PLEC1) in a Dutch family originally described in 1972 as having epidermolysis bullosa with muscular dystrophy. The results revealed homozygosity for a new plectin nonsense mutation at position 13187 and its specific 8q24 marker haplotype profile. Western blotting of cultured fibroblasts and immunofluorescence microscopy of skin biopsy confirm that the plectin protein expression is grossly reduced or absent. A summary of the life-long clinical course of the two affected brothers homozygous for the new E1914X mutation is given

Life-long course and molecular characterization of the original Dutch family with epidermolysis bullosa simplex with muscular dystrophy due to a homozygous novel plectin point mutation

Plectin is one of the largest and most versatile cytolinker proteins known. Cloned and sequenced in 1991, it was later shown to have nonsense mutations in recessive epidermolysis bullosa with muscular dystrophy. A dominant mutation in the gene was found to cause epidermolysis bullosa simplex Ogna without muscular dystrophy. Here we report the DNA sequencing of the plectin gene (PLEC1) in a Dutch family originally described in 1972 as having epidermolysis bullosa with muscular dystrophy. The results revealed homozygosity for a new plectin nonsense mutation at position 13187 and its specific 8q24 marker haplotype profile. Western blotting of cultured fibroblasts and immunofluorescence microscopy of skin biopsy confirm that the plectin protein expression is grossly reduced or absent. A summary of the life-long clinical course of the two affected brothers homozygous for the new E1914X mutation is given.</p

A Better-response Strategy for Self-interested Planning Agents

[EN] When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents¿ strategic behavior considering the interactions as part of the agents¿ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents¿ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.This work is supported by the GLASS project TIN2014-55637-C2-2-R of the Spanish MINECO and the Prometeo project II/2013/019 funded by the Valencian Government.Jordán, J.; Torreño Lerma, A.; De Weerdt, M.; Onaindia De La Rivaherrera, E. (2018). A Better-response Strategy for Self-interested Planning Agents. Applied Intelligence. 48(4):1020-1040. https://doi.org/10.1007/s10489-017-1046-5S10201040484Aghighi M, Bäckström C (2016) A multi-parameter complexity analysis of cost-optimal and net-benefit planning. In: Proceedings of the Twenty-Sixth International Conference on International Conference on Automated Planning and Scheduling. AAAI Press, London, pp 2–10Bercher P, Mattmüller R (2008) A planning graph heuristic for forward-chaining adversarial planning. In: ECAI, vol 8, pp 921–922Brafman RI, Domshlak C, Engel Y, Tennenholtz M (2009) Planning games. In: IJCAI 2009, Proceedings of the 21st international joint conference on artificial intelligence, pp 73–78Bylander T (1994) The computational complexity of propositional strips planning. Artif Intell 69(1):165–204Chen X, Deng X (2006) Settling the complexity of two-player nash equilibrium. In: 47th annual IEEE symposium on foundations of computer science, 2006. FOCS’06. IEEE, pp 261–272Chien S, Sinclair A (2011) Convergence to approximate nash equilibria in congestion games. Games and Economic Behavior 71(2):315–327de Cote EM, Chapman A, Sykulski AM, Jennings N (2010) Automated planning in repeated adversarial games. In: 26th conference on uncertainty in artificial intelligence (UAI 2010), pp 376–383Dunne PE, Kraus S, Manisterski E, Wooldridge M (2010) Solving coalitional resource games. Artif Intell 174(1):20–50Fabrikant A, Papadimitriou C, Talwar K (2004) The complexity of pure nash equilibria. In: Proceedings of the thirty-sixth annual ACM symposium on theory of computing, STOC ’04, pp 604–612Friedman JW, Mezzetti C (2001) Learning in games by random sampling. J Econ Theory 98(1):55–84Ghallab M, Nau D, Traverso P (2004) Automated planning: theory & practice. ElsevierGoemans M, Mirrokni V, Vetta A (2005) Sink equilibria and convergence. In: Proceedings of the 46th annual IEEE symposium on foundations of computer science, FOCS ’05, pp 142–154Hadad M, Kraus S, Hartman IBA, Rosenfeld A (2013) Group planning with time constraints. Ann Math Artif Intell 69(3):243–291Hart S, Mansour Y (2010) How long to equilibrium? the communication complexity of uncoupled equilibrium procedures. Games and Economic Behavior 69(1):107–126Helmert M (2003) Complexity results for standard benchmark domains in planning. Artif Intell 143(2):219–262Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Jennings N, Faratin P, Lomuscio A, Parsons S, Wooldrige M, Sierra C (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negot 10(2):199–215Johnson DS, Papadimtriou CH, Yannakakis M (1988) How easy is local search? J Comput Syst Sci 37 (1):79–100Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling, ICAPSJordán J, Onaindía E (2015) Game-theoretic approach for non-cooperative planning. In: 29th AAAI conference on artificial intelligence (AAAI-15), pp 1357–1363McDermott D, Ghallab M, Howe A, Knoblock C, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL: the planning domain definition language. Yale Center for Computational Vision and Control, New HavenMilchtaich I (1996) Congestion games with player-specific payoff functions. Games and Economic Behavior 13(1):111–124Monderer D, Shapley LS (1996) Potential games. Games and Economic Behavior 14(1):124–143Nigro N, Welch D, Peace J (2015) Strategic planning to implement publicly available ev charching stations: a guide for business and policy makers. Tech rep, Center for Climate and Energy SolutionsNisan N, Ronen A (2007) Computationally feasible vcg mechanisms. J Artif Intell Res 29(1):19–47Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, New YorkPapadimitriou CH (1994) On the complexity of the parity argument and other inefficient proofs of existence. J Comput Syst Sci 48(3):498–532Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Rosenthal RW (1973) A class of games possessing pure-strategy nash equilibria. Int J Game Theory 2(1):65–67Shoham Y, Leyton-Brown K (2009) Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University PressTorreño A, Onaindia E, Sapena Ó (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Torreño A, Onaindia E, Sapena Ó (2014) FMAP: distributed cooperative multi-agent planning. Appl Intell 41(2):606– 626Torreño A, Sapena Ó, Onaindia E (2015) Global heuristics for distributed cooperative multi-agent planning. In: ICAPS 2015. 25th international conference on automated planning and scheduling. AAAI Press, pp 225–233Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior. Princeton University Pressde Weerdt M, Bos A, Tonino H, Witteveen C (2003) A resource logic for multi-agent plan merging. Ann Math Artif Intell 37(1):93–130Wooldridge M, Endriss U, Kraus S, Lang J (2013) Incentive engineering for boolean games. Artif Intell 195:418–43

Phylogenetic Relationships of the Marine Haplosclerida (Phylum Porifera) Employing Ribosomal (28S rRNA) and Mitochondrial (cox1, nad1) Gene Sequence Data

The systematics of the poriferan Order Haplosclerida (Class Demospongiae) has been under scrutiny for a number of years without resolution. Molecular data suggests that the order needs revision at all taxonomic levels. Here, we provide a comprehensive view of the phylogenetic relationships of the marine Haplosclerida using many species from across the order, and three gene regions. Gene trees generated using 28S rRNA, nad1 and cox1 gene data, under maximum likelihood and Bayesian approaches, are highly congruent and suggest the presence of four clades. Clade A is comprised primarily of species of Haliclona and Callyspongia, and clade B is comprised of H. simulans and H. vansoesti (Family Chalinidae), Amphimedon queenslandica (Family Niphatidae) and Tabulocalyx (Family Phloeodictyidae), Clade C is comprised primarily of members of the Families Petrosiidae and Niphatidae, while Clade D is comprised of Aka species. The polyphletic nature of the suborders, families and genera described in other studies is also found here

A functional spleen contributes to afucosylated IgG in humans

As a lymphoid organ, the spleen hosts a wide range of immune cell populations, which not only remove blood-borne antigens, but also generate and regulate antigen-specific immune responses. In particular, the splenic microenvironment has been demonstrated to play a prominent role in adaptive immune responses to enveloped viral infections and alloantigens. During both types of immunizations, antigen-specific immunoglobulins G (IgGs) have been characterized by the reduced amount of fucose present on N-linked glycans of the fragment crystallizable (Fc) region. These glycans are essential for mediating the induction of immune effector functions. Therefore, we hypothesized that a spleen may modulate humoral responses and serve as a preferential site for afucosylated IgG responses, which potentially play a role in immune thrombocytopenia (ITP) pathogenesis. To determine the role of the spleen in IgG-Fc glycosylation, we performed IgG subclass-specific liquid chromatography–mass spectrometry (LC–MS) analysis of Fc glycosylation in a large cohort of individuals splenectomized due to trauma, due to ITP, or spherocytosis. IgG-Fc fucosylation was consistently increased after splenectomy, while no effects for IgG-Fc galactosylation and sialylation were observed. An increase in IgG1- and IgG2/3-Fc fucosylation level upon splenectomy has been reported here for the first time, suggesting that immune responses occurring in the spleen may be particularly prone to generate afucosylated IgG responses. Surprisingly, the level of total IgG-Fc fucosylation was decreased in ITP patients compared to healthy controls. Overall, our results suggest a yet unrecognized role of the spleen in either the induction or maintenance of afucosylated IgG responses by B cells

FMAP: Distributed Cooperative Multi-Agent Planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019, and the FPI-UPV scholarship granted to the first author by the Universitat Politecnica de Valencia.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). FMAP: Distributed Cooperative Multi-Agent Planning. Applied Intelligence. 41(2):606-626. https://doi.org/10.1007/s10489-014-0540-2S606626412Benton J, Coles A, Coles A (2012) Temporal planning with preferences and time-dependent continuous costs. In: Proceedings of the 22nd international conference on automated planning and scheduling (ICAPS). AAAI, pp 2–10Borrajo D. (2013) Multi-agent planning by plan reuse. In: Proceedings of the 12th international conference on autonomous agents and multi-agent systems (AAMAS). IFAAMAS, pp 1141–1142Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). AAAI, pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiagent Syst 19(3):297–331Bresina J, Dearden R, Meuleau N, Ramakrishnan S, Smith D, Washington R (2002) Planning under continuous time and resource uncertainty: a challenge for AI. In: Proceedings of the 18th conference on uncertainty in artificial intelligence (UAI). Morgan Kaufmann, pp 77–84Cox J, Durfee E (2009) Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst 5(4):373–408Crosby M, Rovatsos M, Petrick R (2013) Automated agent decomposition for classical planning. In: Proceedings of the 23rd international conference on automated planning and scheduling (ICAPS). AAAI, pp 46–54Dimopoulos Y, Hashmi MA, Moraitis P (2012) μ-satplan: Multi-agent planning as satisfiability. Knowl-Based Syst 29:54–62Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5-6):619–668Ghallab M, Nau D, Traverso P (2004) Automated planning. Theory and practice. Morgan KaufmannGünay A, Yolum P (2013) Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Appl Intell 39(3):489–509Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of the 14th international conference on automated planning and scheduling ICAPS. AAAI, pp 161–170Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jannach D, Zanker M (2013) Modeling and solving distributed configuration problems: a CSP-based approach. IEEE Trans Knowl Data Eng 25(3):603–618Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kala R, Warwick K (2014) Dynamic distributed lanes: motion planning for multiple autonomous vehicles. Appl Intell:1–22Koehler J, Ottiger D (2002) An AI-based approach to destination control in elevators. AI Mag 23(3):59–78Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportvan der Krogt R (2009) Quantifying privacy in multiagent planning. Multiagent Grid Syst 5(4):451–469Kvarnström J (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi-Agent Syst 9(1–2):87–143Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 1323–1330O’Brien P, Nicol R (1998) FIPA - towards a standard for software agents. BT Tech J 16(3):51–59Öztürk P, Rossland K, Gundersen O (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143Pal A, Tiwari R, Shukla A (2013) Communication constraints multi-agent territory exploration task. Appl Intell 38(3):357–383Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177de la Rosa T, García-Olaya A, Borrajo D (2013) A case-based approach to heuristic planning. Appl Intell 39(1):184–201Sapena O, Onaindia E (2008) Planning in highly dynamic environments: an anytime approach for planning under time constraints. Appl Intell 29(1):90–109Sapena O, Onaindia E, Garrido A, Arangú M (2008) A distributed CSP approach for collaborative planning systems. Eng Appl Artif Intell 21(5):698–709Serrano E, Such J, Botía J, García-Fornes A (2013) Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl Intell:1–16Smith D, Frank J, Jónsson A (2000) Bridging the gap between planning and scheduling. Knowl Eng Rev 15(1):47–83Such J, García-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a privacy-enhancing agent platform. Eng Appl Artif Intell:96–109Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Torreño A, Onaindia E, Sapena O (2012) An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European conference on artificial intelligence (ECAI), vol 242. IOS Press, pp 762–767Torreño A, Onaindia E, Sapena O (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Van Der Krogt R, De Weerdt M (2005) Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). AAAI, pp 161–170de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345– 355Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th international joint conference conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 798–80