Investigation of the tetraquark states QqQˉqˉQq\bar{Q} \bar{q} in the improved chromomagnetic interaction model

In the framework of the improved chromomagnetic interaction model, we complete a systematic study of the $S$-wave tetraquark states $Qq\bar{Q}\bar{q}$ ($Q=c,b$, and $q=u,d,s$) with different quantum numbers, $J^{PC}=0^{+(+)}$, $1^{+(\pm)}$, and $2^{+(+)}$. The mass spectra of tetraquark states are predicted and the possible decay channels are analyzed by considering both the angular momentum and $\mathcal{C}$-parity conservation. The recently observed hidden-charm tetraquark states with strangeness, such as $Z_{cs}(3985)^-$, $X(3960)$, and $Z_{cs}(4220)^+$, can be well explained in our model. Besides, based on the wave function of each tetraquark state, we find that the low-lying states of each $Qq\bar{Q}\bar{q}$ configuration have a large overlap to the $Q\bar Q$ and $q\bar q$ meson basis, instead of $Q\bar q$ and $q\bar Q$ meson basis. This indicates one can search these tetraquark states in future experiments via the channel of $Q\bar Q$ and $q\bar q$ mesons.Comment: 11 pages, 9 figures, and 4 tables; accepted for publication in Chinese Physics

First Characterization of Sphingomyeline Phosphodiesterase Expression in the Bumblebee, Bombus lantschouensis

  The bumblebee (Bombus lantschouensis Vogt) is an important pollinator of wild plants. Sphingomyelin phosphodiesterase (SMPD) is a hydrolase that plays a major role in sphingolipid metabolism reactions. We report the preparation and characterization of a polyclonal antibody for bumblebee SMPD. We then use the polyclonal antiserum to detect the SMPD protein at different development stages and in different tissues. Our results showed that a 1228bp fragment homologous with the B. terrestris SMPD gene was successfully amplified. The molecular weight of the fusion protein was about 70 kDa by SDS-PAGE. An effective polyclonal antibody against SMPD was also obtained from mice and found to have a higher specificity for bumblebee SMPD. Western blotting detection showed that SMPD was expressed at a high level in queen ovaries, although expression was lower in the midgut and venom gland. SMPD expression decreased from the egg stage until the pdd stage. We interpret our results as showing that the development of an effective polyclonal antiserum for the SMPD protein of a bumblebee, which provides a tool for exploring the function of the SMPD gene. In addition, the work has confirmed that SMPD should be considered as an important enzyme during bumblebee egg and larval stages

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension

The conversational machine reading comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic in recent years because of its wide applications. However, existing CMRC benchmarks in which each conversation is assigned a static passage are inconsistent with real scenarios. Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably. To this end, we propose the first Chinese CMRC benchmark Orca and further provide zero-shot/few-shot settings to evaluate model's generalization ability towards diverse domains. We collect 831 hot-topic driven conversations with 4,742 turns in total. Each turn of a conversation is assigned with a response-related passage, aiming to evaluate model's comprehension ability more reasonably. The topics of conversations are collected from social media platform and cover 33 domains, trying to be consistent with real scenarios. Importantly, answers in Orca are all well-annotated natural responses rather than the specific spans or short phrase in previous datasets. Besides, we implement three strong baselines to tackle the challenge in Orca. The results indicate the great challenge of our CMRC benchmark. Our datatset and checkpoints are available at https://github.com/nuochenpku/Orca.Comment: 14 page

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community

Comprehensive analysis of PRPF19 immune infiltrates, DNA methylation, senescence-associated secretory phenotype and ceRNA network in bladder cancer

BackgroundPre-mRNA processing factor 19 (PRPF19) is an E3 ligase that plays a crucial role in repairing tumor-damaged cells and promoting cell survival. However, the predictive value and biological function of PRPF19 in bladder urothelial carcinoma (BLCA) require further investigation.MethodsIn this study, we utilized transcriptomic data and bladder cancer tissue microarrays to identify the high expression of PRPF19 in BLCA, suggesting its potential as a prognostic biomarker. To gain a better understanding of the role of PRPF19 in the immune microenvironment of BLCA, we performed single cell analysis and employed the LASSO method. Additionally, we examined the methylation profiles of PRPF19 using the SMART website. Our investigation confirmed the correlation between PRPF19 and BLCA cell senescence and stemness. Furthermore, we constructed a PRPF19-miR-125a-5p-LINC02693-MIR4435-2HG ceRNA network using the ENCORI and miRWALK databases.ResultsOur comprehensive analysis reveals that PRPF19 can serve as a prognostic marker for BLCA and is significantly associated with various immune-infiltrating cells in BLCA. Moreover, our findings suggest that PRPF19 influences cellular senescence through the regulation of stemness. Finally, we developed a ceRNA network that has the potential to predict the prognosis of BLCA patients.ConclusionWe confirmed the prognostic value and multiple biological functions of PRPF19 in BLCA. Furthermore, the specific ceRNA network can be used as a potential therapeutic target for BLCA

Efficacy of transcranial direct current stimulation for improving postoperative quality of recovery in elderly patients undergoing lower limb major arthroplasty: a randomized controlled substudy

BackgroundPrevious studies have demonstrated improvements in motor, behavioral, and emotional areas following transcranial direct current stimulation (tDCS), but no published studies have reported the efficacy of tDCS on postoperative recovery quality in patients undergoing lower limb major arthroplasty. We hypothesized that tDCS might improve postoperative recovery quality in elderly patients undergoing lower limb major arthroplasty.MethodsNinety-six patients (≥65 years) undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) were randomized to receive 2 mA tDCS for 20 min active-tDCS or sham-tDCS. The primary outcome was the 15-item quality of recovery (QoR-15) score on postoperative day one (Т2). Secondary outcomes included the QoR-15 scores at the 2nd hour (T1), the 1st month (Т3), and the 3rd month (Т4) postoperatively, numeric rating scale scores, and fatigue severity scale scores.ResultsNinety-six elderly patients (mean age, 71 years; 68.7% woman) were analyzed. Higher QoR-15 scores were found in the active-tDCS group at T2 (123.0 [114.3, 127.0] vs. 109.0 [99.3, 115.3]; median difference, 13.0; 95% CI, 8.0 to 17.0; p < 0.001). QoR-15 scores in the active-tDCS group were higher at T1 (p < 0.001), T3 (p = 0.001), and T4 (p = 0.001). The pain scores in the active-tDCS group were lower (p < 0.001 at motion; p < 0.001 at rest). The fatigue degree scores were lower in the active-tDCS group at T1 and T2 (p < 0.001 for each).ConclusiontDCS may help improve the quality of early recovery in elderly patients undergoing lower limb major arthroplasty.Clinical trial registrationThe trial was registered at the China Clinical Trial Center (ChiCTR2200057777, https://www.chictr.org.cn/showproj.html?proj=162744)

Hadronization of Heavy Quarks

Heavy-flavor hadrons produced in ultra-relativistic heavy-ion collisions are a sensitive probe for studying hadronization mechanisms of the quark-gluon-plasma. In this work, we survey how different transport models for the simulation of heavy-quark diffusion through a quark-gluon plasma in heavy-ion collisions implement hadronization and how this affects final-state observables. Utilizing the same input charm-quark distribution in all models at the hadronization transition, we find that the transverse-momentum dependence of the nuclear modification factor of various charm hadron species has significant sensitivity to the hadronization scheme. In addition, the charm-hadron elliptic flow exhibits a nontrivial dependence on the elliptic flow of the hadronizing partonic medium.Comment: 21 pages, 12 figure

Standard-Dose Proton Pump Inhibitors in the Initial Non-eradication Treatment of Duodenal Ulcer: Systematic Review, Network Meta-Analysis, and Cost-Effectiveness Analysis

Background: Short-term use of standard-dose proton pump inhibitors (PPIs) is the first-line initial non-eradication treatment for duodenal ulcer (DU), but the choice on individual PPI drug is still controversial. The purpose of this study is to compare the efficacy, safety, and cost-effectiveness of standard-dose PPI medications in the initial non-eradication treatment of DU.Methods: We searched PubMed, Embase, Cochrane Library, Clinicaltrials.gov, China National Knowledge Infrastructure, VIP database, and the Wanfang database from their earliest records to September 2017. Randomized controlled trials (RCTs) evaluating omeprazole (20 mg/day), pantoprazole (40 mg/day), lansoprazole (30 mg/day), rabeprazole (20 mg/day), ilaprazole (10 mg/day), ranitidine (300 mg/day), famotidine (40 mg/day), or placebo for DU were included. The outcomes were 4-week ulcer healing rate (4-UHR) and the incidence of adverse events (AEs). A network meta-analysis (NMA) using a Bayesian random effects model was conducted, and a cost-effectiveness analysis using a decision tree was performed from the payer’s perspective over 1 year.Results: A total of 62 RCTs involving 10,339 participants (eight interventions) were included. The NMA showed that all the PPIs significantly increased the 4-UHR compared to H2 receptor antagonists (H2RA) and placebo, while there was no significant difference for 4-UHR among PPIs. As to the incidence of AEs, no significant difference was observed among PPIs, H2RA, and placebo during 4-week follow-up. Based on the costs of both PPIs and management of AEs in China, the incremental cost-effectiveness ratio per quality-adjusted life year (in US dollars) for pantoprazole, lansoprazole, rabeprazole, and ilaprazole compared to omeprazole corresponded to $5134.67,$17801.67, $25488.31, and$44572.22, respectively.Conclusion: Although the efficacy and tolerance of different PPIs are similar in the initial non-eradication treatment of DU, pantoprazole (40 mg/day) seems to be the most cost-effective option in China

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce