Relativistic symmetry breaking in light kaonic nuclei

As the experimental data from kaonic atoms and $K^{-}N$ scatterings imply that the $K^{-}$-nucleon interaction is strongly attractive at saturation density, there is a possibility to form $K^{-}$-nuclear bound states or kaonic nuclei. In this work, we investigate the ground-state properties of the light kaonic nuclei with the relativistic mean field theory. It is found that the strong attraction between $K^{-}$ and nucleons reshapes the scalar and vector meson fields, leading to the remarkable enhancement of the nuclear density in the interior of light kaonic nuclei and the manifest shift of the single-nucleon energy spectra and magic numbers therein. As a consequence, the pseudospin symmetry is shown to be violated together with enlarged spin-orbit splittings in these kaonic nuclei.Comment: 15 pages, 7 figure

Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from "Waiters are very friendly and the pasta is simply average" could be ('Waiters', positive, 'friendly'). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.Comment: This paper is accepted in AAAI 202

Molecular cloning of dihydroflavonol 4-reductase gene from grape berry and preparation of an anti-DFR polyclonal antibody

Dihydroflavonol 4-reductase (DFR, EC 1.1.1.219) is a key enzyme of the flavonoid pathway, which synthesizes numerous secondary metabolites to determine the quality of grape berry and wine. The full-length dfr cDNA with 1014 bp was cloned from grape berry, and then introduced into an expressed plasmid pET-30a (+) vector at the EcoR I and Xho I restriction sites. With induction of the isopropyl-β-D-thiogalactoside (IPTG), the pET-dfr was highly expressed in Escherichia coli BL21 (DE3) pLysS cells. A fusion protein with the His-Tag was purified through Ni-NTA His Bind Resin and then used as the antigen to immunize a New Zealand rabbit. The resulting antiserum was further purified precipitated by 50 % saturated ammonium sulfate and DEAE-Sepharose FF chromatography to obtain the immunoglobulin G (IgG) fraction. The resulting polyclonal antibody was found capable of immuno-recognizing the DFR of the crude protein extracts from grape berry. This work undoubtedly provides the possibility for further studies on biological regulation of DFR activity in grape berry.

Association of ABCC2 and CDDP-Resistance in Two Sublines Resistant to CDDP Derived from a Human Nasopharyngeal Carcinoma Cell Line

Cisplatin (CDDP) is one of the most active drugs to treat nasopharyngeal carcinoma (NPC) patients. To further understand the mechanisms of CDDP-resistance in NPC, two CDDP-resistant sublines (CNE2-CDDP and CNE2-CDDP-5Fu) derived from parental NPC cell line CNE2 were established. It was found that at the IC50 level, the resistance of CNE2-CDDP and CNE2-CDDP-5Fu against CDDP was 2.63-fold and 5.35-fold stronger than that of parental CNE2, respectively. Of the four ABC transporters (ABCB1, ABCC1, ABCC2 and ABCG2) related to MDR, only ABCC2 was found to be elevated both in CDDP-resistant sublines, with ABCC2 located in nucleus of CNE2-CDDP-5Fu but not in CNE2-CDDP and parental CNE2. Further research showed that compared to untreated CNE2, the intracellular levels of CDDP were decreased by 2.03-fold in CNE2-CDDP and 2.78-fold in CNE2-CDDP-5Fu. After treatment with PSC833, a modulator of MDR associated transporters including ABCC2, the intracellular level of CDDP was increased in CDDP-resistant sublines, and the resistance to CDDP was partially reversed from 2.63-fold to 1.62-fold in CNE2-CDDP and from 5.35-fold to 4.62-fold in CNE2-CDDP-5Fu. These data indicate that ABCC2 may play an important role in NPC resistant to CDDP

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.Comment: Accepted at COLING 202