Awareness of instrument inaccuracies will boost the development of liquid-repellent coatings

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Introduction The brown planthopper (BPH, Nilaparvata lugens Stål, Hemiptera: Delphacidae) is one of the most devastating insect pests of the crucially important cereal crop, rice ( Oryza sativa L.). Currently, multiple BPH-resistant rice varieties have been cultivated and generalized to control BPH. However, the defence metabolic responses and their modes of action against BPH in different rice cultivars remain uncharacterized. Objective We used a non-biased metabolomics approach to explore the differences in metabolite profiles in response to BPH infestation in the susceptible TN1 rice cultivar and two resistant cultivars (IR36 and IR56). Methods The metabolomic detection based on gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) was performed to investigate the content changes of identified metabolites in TN1, IR36 and IR56 rice varieties at various time points (0 h, 24 h, 48 h and 96 h) post BPH feeding. The differentially expressed metabolites were screened and the corresponding metabolic pathways were further enriched. Results The results showed that compared to that in TN1, the content changes of most primary metabolites were more stable, but the concentration alterations of some defence-related metabolites were more acute and persistent in IR36 and IR56. Furthermore, the differentially expressed pathways analysis revealed that cyanoamino acids and lipids metabolism was persistently induced in IR36, but changes in thiamine, taurine and hypotaurine metabolism were more significant in IR56 during BPH infestation. Besides, the contents of quercetin and spermidine which were harmful to BPH fitness, were significantly elevated by BPH in TN1 and IR36, and the quercetin level was significantly decreased during BPH feeding in IR56. Conclusion The results of the differences in metabolite profiles in response to BPH infestation in different rice cultivars were useful to clarify the metabolic mechanism of rice plants during BPH infestation and to provide new resources to control this insect pest. Electronic supplementary material The online version of this article (10.1007/s11306-019-1523-4) contains supplementary material, which is available to authorized users.

This paper describes the application of a large memory storage and retrieval (LAMSTAR) neural network to medical diagnosis and medical information retrieval problems. The network is based on Minsky's knowledge-lines (k-lines) theory of memory storage and retrieval in the central nervous system. It employs arrays of self-organized map modules, such that the k-lines are implemented via link weights (address correlation) that are being updated by learning. The network also employs features of forgetting and of interpolation and extrapolation, thus being able to handle incomplete data sets. It can deal equally well with exact and fuzzy information, thus being specifically applicable to medical diagnosis where the diagnosis is based on exact data, fuzzy patient interview information, patient history, observed images, and test records. Furthermore, the network can be operated in closed loop with Internet search engines to intelligently use data from the Internet in a higher hierarchy of learning. All of the above features are shown to make the LAMSTAR network suitable for medical diagnosis problems that concern large data sets of many categories that are often incomplete and fuzzy. Applications of the network to three specific medical diagnosis problems are described: two from nephrology and one related to an emergency-room drug identification problem. It is shown that the LAMSTAR network is hundreds and thousands times faster in its training than back-propagation-based networks when used for the same problem and with exactly the same information.

Phase shifting structured light illumination for range sensing involves projecting a set of grating patterns where accuracy is determined, in part, by the number of stripes. However, high pattern frequencies introduce ambiguities during phase unwrapping. This paper proposes a process for embedding a period cue into the projected pattern set without reducing the signal-to-noise ratio. As a result, each period of the high frequency signal can be identified. The proposed method can unwrap high frequency phase and achieve high measurement precision without increasing the pattern number. Therefore, the proposed method can significantly benefit real-time applications. The method is verified by theoretical and experimental analysis using prototype system built to achieve 120 fps at 640 × 480 resolution.

Topological nodal-line semimetals are characterized by the line-contact bulk band crossings and the topological surface states. Breaking certain protecting symmetry turns this system into a Dirac semimetal or Weyl semimetal that hosts zero-dimensional isolated nodal points. Recent advances in band theory predicted a topological nodal-line semimetal state possessing a new type of nodal line in AlB 2 -type diborides. Here, we report an experimental realization of nodal-line fermions and associated surface states near the Fermi energy in ZrB 2 by angleresolved photoemission spectroscopy combined with first-principles calculations. The Dirac nodal lines in ZrB 2 wind into two groups of nodal rings, which are linked together along the Γ-K direction. We further observe a distinct surface state connecting to each nodal line, indicative of the nontrivial topological nature of the bulk nodal lines. Therefore, our results provide convincing experimental evidence of the nodal-line semimetal states in ZrB 2 both in the bulk and on the surface, suggesting ZrB 2 as a remarkable platform for discovering unique phenomena induced by nodal-line fermions.

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