Sensory modulation is essential for animal sensations, behaviours and survival. Peripheral modulations of nociceptive sensations and aversive behaviours are poorly understood. Here we identify a biased cross-inhibitory neural circuit between ASH and ASI sensory neurons. This inhibition is essential to drive normal adaptive avoidance of a CuSO 4 (Cu 2 þ ) challenge in Caenorhabditis elegans. In the circuit, ASHs respond to Cu 2 þ robustly and suppress ASIs via electro-synaptically exciting octopaminergic RIC interneurons, which release octopamine (OA), and neuroendocrinally inhibit ASI by acting on the SER-3 receptor. In addition, ASIs sense Cu 2 þ and permit a rapid onset of Cu 2 þ -evoked responses in Cu 2 þ -sensitive ADF neurons via neuropeptides possibly, to inhibit ASHs. ADFs function as interneurons to mediate ASI inhibition of ASHs by releasing serotonin (5-HT) that binds with the SER-5 receptor on ASHs. This elaborate modulation among sensory neurons via reciprocal inhibition fine-tunes the nociception and avoidance behaviour.

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Abstract. The area under the ROC curve (AUC) has been widely used to measure ranking performance for binary classification tasks. AUC only employs the classifier's scores to rank the test instances; thus, it ignores other valuable information conveyed by the scores, such as sensitivity to small differences in the score values. However, as such differences are inevitable across samples, ignoring them may lead to overfitting the validation set when selecting models with high AUC. This problem is tackled in this paper. On the basis of ranks as well as scores, we introduce a new metric called scored AUC (sAUC), which is the area under the sROC curve. The latter measures how quickly AUC deteriorates if positive scores are decreased. We study the interpretation and statistical properties of sAUC. Experimental results on UCI data sets convincingly demonstrate the effectiveness of the new metric for classifier evaluation and selection in the case of limited validation data.

search with a method that is inspired by the motion mechanism of the electrons around atomic nucleuses. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks.

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