Chlorophyll (Chl) content, especially Chl b content, and stomatal conductance (Gs) are the key factors affecting the net photosynthetic rate (Pn). Setaria italica, a diploid C4 panicoid species with ...a simple genome and high transformation efficiency, has been widely accepted as a model in photosynthesis and drought-tolerance research. The current study characterized Chl content, Gs, and Pn of 48 Setaria mutants induced by ethyl methanesulfonate. A total of 24, 34, and 35 mutants had significant variations in Chl content, Gs, and Pn, respectively. Correlation analysis showed a positive correlation between increased Gs and increased Pn, and a weak correlation between decreased Chl b content and decreased Pn was also found. Remarkably, two mutants behaved with significantly decreased Chl b content but increased Pn compared to Yugu 1. Seven mutants behaved with significantly decreased Gs but did not decrease Pn compared to Yugu 1. The current study thus identified various genetic lines, further exploration of which would be beneficial to elucidate the relationship between Chl content, Gs, and Pn and the mechanism underlying why C4 species are efficient at photosynthesis and water saving.
The goal of this research is to design, fabricate, and test an amplifier circuit which minimizes the input bias current over the input direct current operating point of the amplifier. A biasing ...scheme to set the drain‐source voltage (Vds) to null the net gate leakage current of the input transistor is shown. The desired Vds bias is obtained by replicating the Vds of a reference transistor that operates at the same current density as the input transistor and has its gate terminal open‐circuited so that the drain‐gate leakage and the gate‐source leakage must cancel. An implementation of this scheme as a cascode amplifier is described. Measurement results are presented showing > 20× reduced drift rate of a track‐and‐hold circuit when the output buffer amplifier uses the new approach as compared with a conventional buffer amplifier.
Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to ...an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net. A feature encoder is first trained on the base set to learn embedding features of input images in the pre-training phase. Then in the meta-training phase, a new task-adaptive attention module is designed to yield the task-specific attention, which can adaptively select informative embedding features among the whole task. In the end, in the meta-testing phase, the query image derived from the novel set is predicted by the meta-trained model with limited support images. Extensive experiments are carried out on three public remote sensing scene datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification.
As a fundamental task in the field of remote sensing, scene classification is increasingly attracting attention. The most popular way to solve scene classification is to train a deep neural network ...with a large-scale remote sensing dataset. However, given a small amount of data, how to train a deep neural network with outstanding performance remains a challenge. Existing methods seek to take advantage of transfer knowledge or meta-knowledge to resolve the scene classification issue of remote sensing images with a handful of labeled samples while ignoring various class-irrelevant noises existing in scene features and the specificity of different tasks. For this reason, in this paper, an end-to-end graph neural network is presented to enhance the performance of scene classification in few-shot scenarios, referred to as the graph-based embedding smoothing network (GES-Net). Specifically, GES-Net adopts an unsupervised non-parametric regularizer, called embedding smoothing, to regularize embedding features. Embedding smoothing can capture high-order feature interactions in an unsupervised manner, which is adopted to remove undesired noises from embedding features and yields smoother embedding features. Moreover, instead of the traditional sample-level relation representation, GES-Net introduces a new task-level relation representation to construct the graph. The task-level relation representation can capture the relations between nodes from the perspective of the whole task rather than only between samples, which can highlight subtle differences between nodes and enhance the discrimination of the relations between nodes. Experimental results on three public remote sensing datasets, UC Merced, WHU-RS19, and NWPU-RESISC45, showed that the proposed GES-Net approach obtained state-of-the-art results in the settings of limited labeled samples.
The initial outbreak of the coronavirus disease 2019 (COVID-19) pandemic occurred at the end of 2019. Globally, the COVID-19 pandemic has halted the tourism industry, which is facing a critical ...moment of survival due to the government restrictions and tourism warnings in various countries. In Taiwan, despite effective epidemic prevention measures, the pandemic has significantly affected the country’s tourism industry, particularly the travel industry. This study provides an overview of the impact of COVID-19 on the travel industry and discusses the fiscal stimulus measures and vaccinations provided by the Taiwan government to ensure the sustainability of the tourism industry in Taiwan from the start of 2020 to the end of 2021.
Broadly neutralizing antibodies against highly variable pathogens have stimulated the design of vaccines and therapeutics. We report the use of diverse camelid single-domain antibodies to influenza ...virus hemagglutinin to generate multidomain antibodies with impressive breadth and potency. Multidomain antibody MD3606 protects mice against influenza A and B infection when administered intravenously or expressed locally from a recombinant adeno-associated virus vector. Crystal and single-particle electron microscopy structures of these antibodies with hemagglutinins from influenza A and B viruses reveal binding to highly conserved epitopes. Collectively, our findings demonstrate that multidomain antibodies targeting multiple epitopes exhibit enhanced virus cross-reactivity and potency. In combination with adeno-associated virus-mediated gene delivery, they may provide an effective strategy to prevent infection with influenza virus and other highly variable pathogens.
Influenza therapeutics with new targets and mechanisms of action are urgently needed to combat potential pandemics, emerging viruses, and constantly mutating strains in circulation. We report here on ...the design and structural characterization of potent peptidic inhibitors of influenza hemagglutinin. The peptide design was based on complementarity-determining region loops of human broadly neutralizing antibodies against the hemagglutinin (FI6v3 and CR9114). The optimized peptides exhibit nanomolar affinity and neutralization against influenza A group 1 viruses, including the 2009 H1N1 pandemic and avian H5N1 strains. The peptide inhibitors bind to the highly conserved stem epitope and block the low pH–induced conformational rearrangements associated with membrane fusion. These peptidic compounds and their advantageous biological properties should accelerate the development of new small molecule– and peptide-based therapeutics against influenza virus.
This paper presents a class of reduced footprint inline microstrip bandpass filters capable of covering bandwidth up to 20% as well as good flexibility in establishing various cross-couplings for ...creating transmission zeros. The resonating elements are quarter-wave stepped-impedance resonators (SIRs), which have a wide upper stopband in nature. To achieve size miniaturization, the low-impedance segment of each SIR is implemented as a thick-trace ring and configured in a spiral form, and the high-impedance section is deformed to accommodate the low-impedance section. An interlaced coupling structure is proposed to enhance the coupling limited by the downsized coupled segments of adjacent resonators. In addition, by properly routing the associated shorted stubs, the structure can easily establish cross-coupling between nonadjacent resonators. Thus, compact SIR filters in an inline arrangement can be achieved to have either narrow or wide passbands with multiple transmission zeros. Two such circuits with sharp transition bands and upper stopband extension are synthesized, fabricated, and tested. The measured results show good agreement with simulated data. The circuit areas together with the performances of the experimental filters are compared with those in existing literature.
Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote ...sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model’s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets.
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