Mutations in TTBK2, encoding a kinase implicated in tau phosphorylation, segregate with spinocerebellar ataxia type 11

The microtubule-associated protein tau (encoded by MAPT) and several tau kinases have been implicated in neurodegeneration, but only MAPT has a proven role in disease. We identified mutations in the gene encoding tau tubulin kinase 2 (TTBK2) as the cause of spinocerebellar ataxia type 11. Affected brain tissue showed substantial cerebellar degeneration and tau deposition. These data suggest that TTBK2 is important in the tau cascade and in spinocerebellar degeneration.     

Loss of ACTN3 gene function alters mouse muscle metabolism and shows evidence of positive selection in humans

More than a billion humans worldwide are predicted to be completely deficient in the fast skeletal muscle fiber protein alpha-actinin-3 owing to homozygosity for a premature stop codon polymorphism, R577X, in the ACTN3 gene. The R577X polymorphism is associated with elite athlete status and human muscle performance, suggesting that alpha-actinin-3 deficiency influences the function of fast muscle fibers. Here we show that loss of alpha-actinin-3 expression in a knockout mouse model results in a shift in muscle metabolism toward the more efficient aerobic pathway and an increase in intrinsic endurance performance. In addition, we demonstrate that the genomic region surrounding the 577X null allele shows low levels of genetic variation and recombination in individuals of European and East Asian descent, consistent with strong, recent positive selection. We propose that the 577X allele has been positively selected in some human populations owing to its effect on skeletal muscle metabolism.     

Effect of ethanol on toxicity and metabolism of amphetamine in the mouse.

Ethanol, 3 g/kg i.p., did not significantly alter the acute toxicity of amphetamine in the mouse. However, the urinary metabolite pattern was changed, suggesting that ethanol suppressed metabolism of the stimulant during the initial 6 h period. After 24 h, the mouse metabolized the same fraction of a given dose of amphetamine, whether it was given as amphetamine alone or amphetamine mixed with 2,3 or 4 g/kg ethanol.     

The Third Track of the Open-Systems-Thinking School: An Application of Domain Theory to New Zealand Ports

The Open-Systems Thinking (OST) school identified with Emery and Trist is situated as an important but underutilized approach in systems thinking. The main features of OST are described, including four "tracks" along which OST has developed. The third track, which focuses on interorganizational domains, is considered in detail. It is then applied to a case concerning the complex social dynamics of and in port domains in New Zealand. This exercise enables us to illuminate some lacunae in previous representations of the port domain research, to assess OST's third track regarding its relevance for systemic practice, and to help to bring OST to the table with other systems approaches that are all vying for the attention of researchers, practicing managers, students, and consultants.     

Decentralized Bezout Identity and a Parametrization of Decentralized Controllers for Singular Systems

1　 IntroductionConsider a singular decentralized control system of the formEx(t) =Ax(t) ∑Ni=1Biui(t)yi =Cix(t) ,i∈ N ={ 1 ,2 ,… ,N}(1 )where x(t)∈ Rn is the state vector,ui(t)∈ Rmi and yi(t)∈ Rpi are respectively the localcontrol input and measure outputvectors of the ith control channel.The matrix E may besingular,i.e.,rank(E) 相似文献   

A DEA-TOPSIS method for multiple criteria decision analysis in emergency management

A hybrid approach of DEA (data envelopment analysis) and TOPSIS (technique for order performance (preference) by similarity to ideal solution) is proposed for multiple criteria decision analysis in emergency management. Two DEA-based optimization models are constructed to facilitate identifying parameter information regarding criterion weights and quantifying qualitative criteria in TOPSIS. An emergency management case study utilizing data from the Emergency Management Australia (EMA) Disasters Database is provided to demonstrate the feasibility of the proposed analysis procedure.     

Risk reduction in a project portfolio

The positive impacts of managing projects as a portfolio are quantified by comparing the value of the integrated risk of a project portfolio and the aggregation of single project risks implemented separately. Firstly, the integrated risk is defined by proposing risky events based on set theory. Secondly, as projects interact with each other in a project portfolio, the integrated risk is evaluated by using a Bayesian network structure learning algorithm to construct an interdependent network of risks. Finally, the integrated risk of a practical case is assessed using this method, and the results show that the proposed method is an effective tool for calculating the extent of risk reduction of implementing a project portfolio and identifying the most risky project, so as to assist companies in making comprehensive decisions in the phase of portfolio selection and portfolio controlling.     

Conflict analysis approaches for investigating attitudes and misperceptions in the War of 1812

Formal systems engineering approaches to modeling misperceptions and attitudes are employed within the framework of the graph model for conflict resolution to systematically study the War of 1812 between the United States of America and Great Britain in order to provide enhanced insights into the causes of the war. More specifically, relational definitions for preferences, movements and stability concepts are defined for describing the attitudes and associated behavior of decision makers involved in a conflict. To capture misperceptions of decision makers in the War of 1812, attitudes are studied within the structure of a hypergame. Combining attitudes and misperceptions within the paradigm of the graph model furnishes the flexible analytical tool which demonstrates that misunderstanding of attitudes by Great Britain and the United States may have contributed to the outbreak of this nasty war.     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.