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1.  Continuous shifts in the active set of spinal interneurons during changes in locomotor speed 
Nature neuroscience  2008;11(12):1419-1429.
The classic ‘size principle’ of motor control describes how increasingly forceful movements arise by the recruitment of motoneurons of progressively larger size and force output into the active pool. Here, we explore the activity of pools of spinal interneurons in larval zebrafish and find that increases in swimming speed are not associated with the simple addition of cells to the active pool. Instead, the recruitment of interneurons at faster speeds is accompanied by the silencing of those driving movements at slower speeds. This silencing occurs both between and within classes of rhythmically-active premotor excitatory interneurons. Thus, unlike motoneurons, there is a continuous shift in the set of cells driving the behavior, even though changes in the speed of the movements and the frequency of the motor pattern appear smoothly graded. We conclude that fundamentally different principles may underlie the recruitment of motoneuron and interneuron pools.
PMCID: PMC2735137  PMID: 18997790
2.  EVA: evaluation of protein structure prediction servers 
Nucleic Acids Research  2003;31(13):3311-3315.
EVA ( is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.
PMCID: PMC169025  PMID: 12824315

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