440 research outputs found
Relativistic symmetry breaking in light kaonic nuclei
As the experimental data from kaonic atoms and scatterings imply
that the -nucleon interaction is strongly attractive at saturation
density, there is a possibility to form -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 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
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