Is Y(4008)Y(4008) possibly a 1−−1^{--} ψ(33S1)\psi(3^3S_1) state?

The strong decays of the radially excited $\psi(3^3S_1)$ state are studied within the $^3P_0$ model. As a believed $\psi(3^3S_1)$, some strong decay widths and relevant ratios of $\psi(4040)$ are calculated in the model. The theoretical results are consistent with experiments. In a similar way, as a possible $\psi(3^3S_1)$, the same strong decay widths and relevant ratios of $Y(4008)$ are presented. Our study indicates that $Y(4008)$ is hard to be identified with a $\psi(3^3S_1)$ charmonium once it is confirmed under the $D^*\bar{D}^*$ threshold, but it is very possibly a $\psi(3^3S_1)$ charmonium once it is confirmed above the $D^*\bar{D}^*$ threshold by experiment.Comment: 6 pages, 3 figures, 6 tables, RevTe

Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection

Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks. Existing approaches typically follow a "Train-from-Scratch" paradigm, which is fundamentally bounded by the availability of large-scale annotated data. While expressive pre-trained language models (PLMs) have been adapted in a "Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and downstream objectives also requires costly task-specific supervision. In this paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for few-shot fake news detection that jointly leverages the pre-trained knowledge in PLMs and the social context topology. Our approach mitigates label scarcity by wrapping the news article in a task-related textual prompt, which is then processed by the PLM to directly elicit task-specific knowledge. To supplement the PLM with social context without inducing additional training overheads, motivated by empirical observation on user veracity consistency (i.e., social users tend to consume news of the same veracity type), we further construct a news proximity graph among news articles to capture the veracity-consistent signals in shared readerships, and align the prompting predictions along the graph edges in a confidence-informed manner. Extensive experiments on three real-world benchmarks demonstrate that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.Comment: Accepted to CIKM 2023 (Full Paper

Potential Uses of Wild Germplasms of Grain Legumes for Crop Improvement

Challenged by population increase, climatic change, and soil deterioration, crop improvement is always a priority in securing food supplies. Although the production of grain legumes is in general lower than that of cereals, the nutritional value of grain legumes make them important components of food security. Nevertheless, limited by severe genetic bottlenecks during domestication and human selection, grain legumes, like other crops, have suffered from a loss of genetic diversity which is essential for providing genetic materials for crop improvement programs. Illustrated by whole-genome-sequencing, wild relatives of crops adapted to various environments were shown to maintain high genetic diversity. In this review, we focused on nine important grain legumes (soybean, peanut, pea, chickpea, common bean, lentil, cowpea, lupin, and pigeonpea) to discuss the potential uses of their wild relatives as genetic resources for crop breeding and improvement, and summarized the various genetic/genomic approaches adopted for these purposes.Instituto de Fisiología y Recursos Genéticos VegetalesFil: Muñoz, Nacira Belen. Chinese University of Hong Kong. Centre for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology and School of Life Sciences; China. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fisiología y Recursos Genéticos Vegetales; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Cátedra de Fisiología Vegetal; ArgentinaFil: Ailin, Liu. Chinese University of Hong Kong. Centre for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology and School of Life Sciences; ChinaFil: Leo, Kan. Chinese University of Hong Kong. Centre for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology and School of Life Sciences; ChinaFil: Man-Wah, Li. Chinese University of Hong Kong. Centre for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology and School of Life Sciences; ChinaFil: Hon-Ming, Lam. Chinese University of Hong Kong. Centre for Soybean Research of the Partner State Key Laboratory of Agrobiotechnology and School of Life Sciences; Chin

MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.Comment: Accepted by AAAI 202

Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively.Comment: Accepted to AAAI2023, Ora

0^-+ Trigluon Glueball and its Implication for a Recent BES Observation

We calculate the mass of $0^{-+}$ triple-valence-gluon resonance, the trigluon glueball, with QCD sum rules. Its mass is found to be approximately in the region between 1.9 GeV and 2.7 GeV with some theoretical uncertainties. Moreover, it is likely that the new BES measurement of the $p \bar{p}$ enhancement near threshold in the $J/\psi$ decays exhibits the behavior of this trigluon state. Our analyzes favor the baryonium-gluonium mixing picture for the BES observation.Comment: 14 text pages; 2 eps-form figures.To appear in Phys.Lett.

Distinct roles of NMB and GRP in itch transmission

A key question in our understanding of itch coding mechanisms is whether itch is relayed by dedicated molecular and neuronal pathways. Previous studies suggested that gastrin-releasing peptide (GRP) is an itch-specific neurotransmitter. Neuromedin B (NMB) is a mammalian member of the bombesin family of peptides closely related to GRP, but its role in itch is unclear. Here, we show that itch deficits in mice lacking NMB or GRP are non-redundant and Nmb/Grp double KO (DKO) mice displayed additive deficits. Furthermore, both Nmb/Grp and Nmbr/Grpr DKO mice responded normally to a wide array of noxious stimuli. Ablation of NMBR neurons partially attenuated peripherally induced itch without compromising nociceptive processing. Importantly, electrophysiological studies suggested that GRPR neurons receive glutamatergic input from NMBR neurons. Thus, we propose that NMB and GRP may transmit discrete itch information and NMBR neurons are an integral part of neural circuits for itch in the spinal cord

Network-Based Analysis of Genetic Variants Associated with Hippocampal Volume in Alzheimer’S Disease: A Study of Adni Cohorts

Background: Alzheimer’s disease (AD) is a neurodegenerative disease that causes dementia. While molecular basis of AD is not fully understood, genetic factors are expected to participate in the development and progression of the disease. Our goal was to uncover novel genetic underpinnings of Alzheimer’s disease with a bioinformatics approach that accounts for tissue specificity. Findings: We performed genome-wide association studies (GWAS) for hippocampal volume in two Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. We used these GWAS in a subsequent tissue-specific network-wide association study (NetWAS), which applied nominally significant associations in the initial GWAS to identify disease relevant patterns in a functional network for the hippocampus. We compared prioritized gene lists from NetWAS and GWAS with literature curated AD-associated genes from the Online Mendelian Inheritance in Man (OMIM) database. In the ADNI-1 GWAS, where we also observed an enrichment of low p-values, NetWAS prioritized disease-gene associations in accordance with OMIM annotations. This was not observed in the ADNI-2 dataset. We provide source code to replicate these analyses as well as complete results under permissive licenses. Conclusions: We performed the first analysis of hippocampal volume using NetWAS, which uses machine learning algorithms applied to tissue-specific functional interaction network to prioritize GWAS results. Our findings support the idea that tissue-specific networks may provide helpful context for understanding the etiology of common human diseases and reveal challenges that network-based approaches encounter in some datasets. Our source code and intermediate results files can facilitate the development of methods to address these challenges

Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening

Background: Small cell lung cancer (SCLC) is characterized by extreme invasiveness and lethality. There have been very few developments in its diagnosis and treatment over the past decades. It is urgently needed to explore potential novel biomarkers and drug targets for SCLC.Methods: Two-sample Mendelian Randomization (MR) was performed to investigate causal associations between SCLC and plasma proteins using genome-wide association studies (GWAS) summary statistics of SCLC from Transdisciplinary Research Into Cancer of the Lung Consortium (nCase = 2,791 vs. nControl = 20,580), and was validated in another cohort (nCase = 2,664 vs. nControl = 21,444). 734 plasma proteins and their genetic instruments of cis-acting protein quantitative trait loci (pQTL) were used, whereas external plasma proteome data was retrieved from deCODE database. Bidirectional MR, Steiger filtering and phenotype scanning were applied to further verify the associations.Results: Seven significant (p < 6.81 × 10−5) plasma protein-SCLC pairs were identified by MR analysis, including ACP5 (OR = 0.76, 95% CI: 0.67–0.86), CPB2 (OR = 0.90, 95% CI: 0.86–0.95), GSTM3 (OR = 0.45, 95% CI: 0.33–0.63), SHMT1 (OR = 0.74, 95% CI: 0.64–0.86), CTSB (OR = 0.79, 95% CI: 0.71–0.88), NTNG1 (OR = 0.81, 95% CI: 0.74–0.90) and FAM171B (OR = 1.40, 95% CI: 1.21–1.62). The external validation confirmed that CPB2, GSTM3 and NTNG1 had protective effects against SCLC, while FAM171B increased SCLC risk. However, the reverse causality analysis revealed that SCLC caused significant changes in plasma levels of most of these proteins, including decreases of ACP5, CPB2, GSTM3 and NTNG1, and the increase of FAM171B.Conclusion: This integrative analysis firstly suggested the causal associations between SCLC and plasma proteins, and the identified several proteins may be promising novel drug targets or biomarkers for SCLC