A Study on the C-E Translation of Expressions with Chinese Characteristics in 2017 National Government Work Report from the Perspective of Functional Equivalence

Nowadays China plays an increasingly important role in international arena and draws great attention from all over the world. Government Work Report (GWR) reflects the latest and most authoritative work of the present situation in China, which not only summarizes the past work experience, but also puts forward the future work plan. The C-E translation of 2017 GWR is a good reference for people who are interested in C-E translation of political texts and provides a glimpse of China’s national conditions for foreign countries. Considering the linguistic and cultural differences between Chinese and English, the author studies the expressions with Chinese characteristics in the 2017 Government Work Report (2017GWR) from perspective of functional equivalence

Signature of Pseudo Nambu-Goldstone Higgs boson in its Decay

If the Higgs boson is a pseudo Nambu-Goldstone boson (PNGB), the $hZ\gamma$ contact interaction induced by the $\mathcal{O}(p^4)$ invariants of the non-linear sigma model is free from its nonlinearity effects. The process $h\rightarrow Z\gamma$ can be used to eliminate the universal effects of heavy particles, which can fake the nonlinearity effects of the PNGB Higgs boson in the process $h\rightarrow V^*V$ ($V=W^\pm$,\ $Z$). We demonstrate that the ratio of the signal strength of $h\rightarrow Z\gamma$ and $h\rightarrow V^*V$ is good to distinguish the signature of the PNGB Higgs boson from Higgs coupling deviations

A cytoplasmic Cu-Zn superoxide dismutase SOD1 contributes to hyphal growth and virulence of Fusarium graminearum

AbstractSuperoxide dismutases (SODs) are scavengers of superoxide radicals, one of the main reactive oxygen species (ROS) in the cell. SOD-based ROS scavenging system constitutes the frontline defense against intra- and extracellular ROS, but the roles of SODs in the important cereal pathogen Fusarium graminearum are not very clear. There are five SOD genes in F. graminearum genome, encoding cytoplasmic Cu-Zn SOD1 and MnSOD3, mitochondrial MnSOD2 and FeSOD4, and extracellular CuSOD5. Previous studies reported that the expression of SOD1 increased during infection of wheat coleoptiles and florets. In this work we showed that the recombinant SOD1 protein had the superoxide dismutase activity in vitro, and that the SOD1-mRFP fusion protein localized in the cytoplasm of F. graminearum. The Δsod1 mutants had slightly reduced hyphal growth and markedly increased sensitivity to the intracellular ROS generator menadione. The conidial germination under extracellular oxidative stress was significantly delayed in the mutants. Wheat floret infection assay showed that the Δsod1 mutants had a reduced pathogenicity. Furthermore, the Δsod1 mutants had a significant reduction in production of deoxynivalenol mycotoxin. Our results indicate that the cytoplasmic Cu-Zn SOD1 affects fungal growth probably depending on detoxification of intracellular superoxide radicals, and that SOD1-mediated deoxynivalenol production contributes to the virulence of F. graminearum in wheat head infection

Ethyl 5-methyl-4-oxo-3-phenyl-2-propyl­amino-3,4-dihydro­thieno[2,3-d]pyrimidine-6-carboxyl­ate

The title compound, C19H21N3O3S, was synthesized via the aza-Wittig reaction of functionalized imino­phospho­rane with phenyl isocyanate under mild conditions. In the mol­ecule, the fused thienopyrimidine ring system is essentially planar, with a maximum deviation of 0.072 (2) Å, and makes a dihedral angle of 60.11 (9)° with the phenyl ring. An intra­molecular C—H⋯O hydrogen bond is present. The crystal packing is stabilized by inter­molecular N—H⋯O and C—H⋯O hydrogen bonds

Unpaired Caricature-Visual Face Recognition via Feature Decomposition-Restoration-Decomposition

Existing caricature-visual face recognition methods train the models based on caricature-visual image pairs from the same identities. Unfortunately, in many real-world applications, facial caricatures and visual facial images are usually unpaired in the training set due to the difficulty of collecting facial caricatures drawn by artists. In this paper, we study caricature-visual face recognition under the practical setting that only unpaired facial caricature and visual facial images are available as training samples, and define this setting as unpaired caricature-visual face recognition. To this end, we develop a novel feature decomposition-restoration-decomposition method (FDRD), which mainly consists of a backbone network, an identity-oriented feature decomposition module, and a modality-oriented feature restoration module, to extract modality-irrelevant identity features. To effectively train FDRD in the case of limited facial caricature training samples, we develop a two-stage learning framework. In the first stage, we perform single-modality restoration, enabling the model to have the basic ability of feature decomposition and restoration for each modality. In the second stage, we perform cross-modality recognition by exchanging new modality features between the two modalities, facilitating the model to focus on the decoupling of identity features and modality features. Experimental results demonstrate that our method performs favorably against several state-of-the-art face recognition methods and cross-modality methods. Our code is available at https://github.com/Capricorn-Karma/FDRD

Potent Antifungal Activity of Pure Compounds from Traditional Chinese Medicine Extracts against Six Oral Candida Species and the Synergy with Fluconazole against Azole-Resistant Candida albicans

This study was designed to evaluate the in vitro antifungal activities of four traditional Chinese medicine (TCM) extracts. The inhibitory effects of pseudolaric acid B, gentiopicrin, rhein, and alion were assessed using standard disk diffusion and broth microdilution assays. They were tested against six oral Candida species, Candida albicans, Candida glabrata, Candida tropicalis, Candida krusei, Candida dubliniensis, and Candida guilliermondii, including clinical isolates from HIV-negative, HIV-positive, and Sjögren's syndrome patients. It was found that pseudolaric acid B had the most potent antifungal effect and showed similar antifungal activity to all six Candida spp, and to isolates from HIV-negative, HIV-positive, and Sjögren's syndrome patients. The MIC values ranged from 16 to 128 μg/mL. More interestingly, a synergistic effect of pseudolaric acid B in combination with fluconazole was observed. We suggest that pseudolaric acid B might be a potential therapeutic fungicidal agent in treating oral candidiasis

Identifying and Extracting Rare Disease Phenotypes with Large Language Models

Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text and time-consuming to extract manually. While natural language processing (NLP) models can perform named entity recognition (NER) to automate extraction, a major bottleneck is the development of a large, annotated corpus for model training. Recently, prompt learning emerged as an NLP paradigm that can lead to more generalizable results without any (zero-shot) or few labeled samples (few-shot). Despite growing interest in ChatGPT, a revolutionary large language model capable of following complex human prompts and generating high-quality responses, none have studied its NER performance for RDs in the zero- and few-shot settings. To this end, we engineered novel prompts aimed at extracting RD phenotypes and, to the best of our knowledge, are the first the establish a benchmark for evaluating ChatGPT's performance in these settings. We compared its performance to the traditional fine-tuning approach and conducted an in-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the zero- and few-shot settings, respectively). Despite this, ChatGPT achieved similar or higher accuracy for certain entities (i.e., rare diseases and signs) in the one-shot setting (F1 of 0.776 and 0.725). This suggests that with appropriate prompt engineering, ChatGPT has the potential to match or outperform fine-tuned language models for certain entity types with just one labeled sample. While the proliferation of large language models may provide opportunities for supporting RD diagnosis and treatment, researchers and clinicians should critically evaluate model outputs and be well-informed of their limitations