The intake of water is important for the survival of all animals and drinking water can be used as a reward in thirsty animals. Here we found that thirsty Drosophila melanogaster can associate drinking water with an odour to form a protein-synthesis-dependent water-reward long-term memory (LTM). Furthermore, we found that the reinforcement of LTM requires water-responsive dopaminergic neurons projecting to the restricted region of mushroom body (MB) β′ lobe, which are different from the neurons required for the reinforcement of learning and short-term memory (STM). Synaptic output from α′β′ neurons is required for consolidation, whereas the output from γ and αβ neurons is required for the retrieval of LTM. Finally, two types of MB efferent neurons retrieve LTM from γ and αβ neurons by releasing glutamate and acetylcholine, respectively. Our results therefore cast light on the cellular and molecular mechanisms responsible for processing water-reward LTM in Drosophila.

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In this study, electrolyte-insulator-semiconductor (EIS) capacitors with Sb2O3/SiO2 double stacked sensing membranes were fabricated with pH sensing capability. The results indicate that Sb2O3/SiO2 double stacked membranes with appropriate annealing had better material quality and sensing performance than Sb2O3 membranes did. To investigate the influence of double stack and annealing, multiple material characterizations and sensing measurements on membranes including of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) were conducted. These analyses indicate that double stack could enhance crystallization and grainization, which reinforced the surface sites on the membrane. Therefore, the sensing capability could be enhanced, Sb2O3/SiO2-based with appropriate annealing show promises for future industrial ion sensing devices.

With the incoming of information era, internet has been developed rapidly and offered more and more services. However, intrusions, viruses and worms follow with the grown of internet, spread widely all over the world within high speed network. Although many kinds of intrusion detection systems (IDSs) are developed, they have some disadvantages in that they focus on low-level attacks or anomalies, and raise alerts independently.In this paper, we give a formal description about attack patterns, attack transition states and attack scenarios. We proposed the system architecture to generate an attack scenario database correctly and completely. We first classify and extract attack patterns, then, correlate attack patterns with pre/post conditions matching and. Moreover, the approach, Attack Scenario Generation with Casual Relationship (ASGCR), is proposed to build an attack scenario database Finally, we present the combination of our attack scenario database with security operation center (SOC) to implement the related components concerning alert integrations and correlations. It is shown that our method is better than CAML [4] since we can generate more attack scenarios effectively and correctly to help system managers to maintain network security.

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