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Responses to oligogalacturonic acid (OGA) were determined in transgenic Arabidopsis thaliana seedlings expressing the calcium reporter protein aequorin. OGA stimulated a rapid, substantial and transient increase in the concentration of cytosolic calcium ([Ca 2+ ] cyt ) that peaked after ca. 15 s. This increase was dose-dependent, saturating at ca. 50 µg Gal equiv/ml of OGA. OGA also stimulated a rapid generation of H 2 O 2 . A small, rapid increase in H 2 O 2 content was followed by a much larger oxidative burst, with H 2 O 2 content peaking after ca. 60 min and declining thereafter. Induction of the oxidative burst by OGA was also dose-dependent, with a maximum response again being achieved at ca. 50 µg Gal equiv/mL. Inhibitors of calcium fluxes inhibited both increases in [Ca 2+ ] cyt and [H 2 O 2 ], whereas inhibitors of NADPH oxidase blocked only the oxidative burst. OGA increased strongly the expression of the defence-related genes CHS, GST, PAL and PR-1. This induction was suppressed by inhibitors of calcium flux or NADPH oxidase, indicating that increases in both cytosolic calcium and H 2 O 2 are required for OGA-induced gene expression.

Pochonicine, the first naturally occurring polyhydroxylated pyrrolizidine containing an acetamidomethyl group, which was isolated from Pochonia suchlasporia var. suchlasporia TAMA 87, together with its enantiomer and their C-1 and/or C-3 epimers, have been synthesized from the sugar-derived cyclic nitrones 9D and 9L, respectively. An in-depth NMR study showed that both the (1)H and (13)C NMR spectra of the synthetic pochonicines (1D and 1L) matched very well with those of natural pochonicine in D2O, which unequivocally determined the relative configuration of the natural product as 1D or 1L. In addition, comparison of the optical rotations of the synthetic pochonicines and that of the natural product, but more convincingly their glycosidase inhibition profiles, confirmed the absolute configuration of natural pochonicine as 1R,3S,5R,6R,7S,7aR. Thereby, the structure of natural pochonicine was unequivocally determined as (+)-(1R,3S,5R,6R,7S,7aR)-pochonicine (1D). Glycosidase inhibition experiments showed that natural pochonicine 1D and its epimers 2D, 3D, and 4D all are powerful inhibitors of hexosaminidases (five β-N-acetylglucosaminidases and two β-N-acetylgalactosaminidases) while their enantiomers 1L, 2L, 3L, and 4L are much weaker inhibitors of the same enzymes. (-)-3-epi-Pochonicine (2L) was found to be a potent and selective inhibitor of α-l-rhamnosidase. None of the compounds showed any inhibition of α-GalNAcase.

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Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability of labeled data is limited due to the significant human resources and time costs of labeling hyperspectral data. Unsupervised learning for hyperspectral image classification has thus received increasing attention. In this paper, we propose a novel unsupervised framework based on a contrastive learning method and a transformer model for hyperspectral image classification. The experimental results prove that our model can efficiently extract hyperspectral image features in unsupervised situations.

Blinking occurs with nearly all fluorophores including organic dyes, fluorescent proteins, semiconductor quantum dots and carbon dots (CDs). We developed non-blinking and photoresistant fluorescent CDs by introducing multiple aromatic domains...

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time.