Charmoniumlike resonant explanation on the newly observed X(3960)X(3960)

Stimulated by the observation of the newly $X(3960)$ observed by the LHCb collaboration, we adopt the one-boson-exchange model and consider the $S-D$ wave mixing effects to study the $D_s\bar{D}_s/D^*\bar{D}^*/D_s^*\bar{D}_s^*$ interactions with $I(J^{PC})=0(0^{++})$. After producing the phase shifts of this coupled channel systems, our results show that there can exist a charmoniumlike resonance, whose obtained mass and width can both well match with the experimental data of the newly observed $X(3960)$. We also find that the $D^*\bar{D}^*$ system plays an important role in the formation of the newly observed $X(3960)$ as a charmoniumlike resonance, and the $D_s^*\bar{D}_s^*$ system makes a significant contribution to the resonant width. As a byproduct, we perform a coupled channel analysis on the $D^*\bar{D}^*/D_s\bar{D}_s^*/D_s^*\bar{D}_s^*$ interactions with $I(J^{PC})=0(1^{+-})$, our results can predict the existence of the $D_s\bar{D}_s^*$ molecule with $1^{+-}$ and the $D_s^*\bar{D}_s^*$ molecule with $1^{+-}$. Their widths are around several and several to several tens MeV, respectively. Experimental searches for these two possible charmoniumlike molecular candidates can be helpful to verify our proposal.Comment: 7 pages, 5 figures, Physics Letters B846, 13825

Possible open charm molecular pentaquarks from ΛcK(∗)/ΣcK(∗)\Lambda_cK^{(*)}/\Sigma_cK^{(*)} interactions

In this work, we adopt the one-boson-exchange model to study the $Y_cK^{(*)} (Y_c=\Lambda_c, \Sigma_c)$ interactions. After considering both of the $S-D$ wave mixing effects and the coupled channel effects, we can predict several possible open-charm molecular pentaquarks, i.e., the single $\Sigma_cK^*$ molecular states with $I(J^P)=1/2(1/2^-)$, $1/2(3/2^-)$ and $3/2(1/2^-)$, the coupled $\Lambda_cK^*/\Sigma_cK^*$ molecular states with $1/2(1/2^-)$ and $1/2(3/2^-)$, and the coupled $\Sigma_cK/\Lambda_cK^*/\Sigma_cK^*$ molecular state with $1/2(1/2^-)$. Meanwhile, we extend our study to the $Y_c\bar{K}^{(*)}$ interactions, our results suggest the $\Sigma_c\bar{K}$ system with $I(J^P)=1/2(1/2^-)$, the $\Sigma_c\bar{K}^*$ systems with $1/2(1/2^-)$, $1/2(3/2^-)$, and $3/2(3/2^-)$, the coupled $\Lambda_c\bar{K}^*/\Sigma_c\bar K^*$ system with $1/2(1/2^-)$, and the $\Sigma_c\bar{K}/\Lambda_c\bar{K}^*/\Sigma_c\bar K^*$ system with $1/2(1/2^-)$ can be the prime molecular candidates.Comment: 9 pages, 1 figure. arXiv admin note: text overlap with arXiv:2208.1019

Sequence Dependent Repair of 1,N6-Ethenoadenine by DNA Repair Enzymes ALKBH2, ALKBH3, and AlkB

Mutation patterns of DNA adducts, such as mutational spectra and signatures, are useful tools for diagnostic and prognostic purposes. Mutational spectra of carcinogens derive from three sources: adduct formation, replication bypass, and repair. Here, we consider the repair aspect of 1,N6-ethenoadenine (εA) by the 2-oxoglutarate/Fe(II)-dependent AlkB family enzymes. Specifically, we investigated εA repair across 16 possible sequence contexts (5′/3′ flanking base to εA varied as G/A/T/C). The results revealed that repair efficiency is altered according to sequence, enzyme, and strand context (ss- versus ds-DNA). The methods can be used to study other aspects of mutational spectra or other pathways of repair

TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence

Creating an essay based on a few given topics is a challenging NLP task. Although several effective methods for this problem, topic-to-essay generation, have appeared recently, there is still much room for improvement, especially in terms of the coverage of the given topics and the coherence of the generated text. In this paper, we propose a novel approach called TegFormer which utilizes the Transformer architecture where the encoder is enriched with domain-specific contexts while the decoder is enhanced by a large-scale pre-trained language model. Specifically, a \emph{Topic-Extension} layer capturing the interaction between the given topics and their domain-specific contexts is plugged into the encoder. Since the given topics are usually concise and sparse, such an additional layer can bring more topic-related semantics in to facilitate the subsequent natural language generation. Moreover, an \emph{Embedding-Fusion} module that combines the domain-specific word embeddings learnt from the given corpus and the general-purpose word embeddings provided by a GPT-2 model pre-trained on massive text data is integrated into the decoder. Since GPT-2 is at a much larger scale, it contains a lot more implicit linguistic knowledge which would help the decoder to produce more grammatical and readable text. Extensive experiments have shown that the pieces of text generated by TegFormer have better topic coverage and higher text coherence than those from SOTA topic-to-essay techniques, according to automatic and human evaluations. As revealed by ablation studies, both the Topic-Extension layer and the Embedding-Fusion module contribute substantially to TegFormer's performance advantage