Research advances in the molecular classification of gastric cancer.

Gastric cancer (GC) is a malignant tumor with one of the lowest five-year survival rates. Traditional first-line treatment regimens, such as platinum drugs, have limited therapeutic efficacy in treating advanced GC and significant side effects, greatly reducing patient quality of life. In contrast, trastuzumab and other immune checkpoint inhibitors, such as nivolumab and pembrolizumab, have demonstrated consistent and reliable efficacy in treating GC. Here, we discuss the intrinsic characteristics of GC from a molecular perspective and provide a comprehensive review of classification and treatment advances in the disease. Finally, we suggest several strategies based on the intrinsic molecular characteristics of GC to aid in overcoming clinical challenges in the development of precision medicine and improve patient prognosis.

Dimethyl fumarate covalently modifies Cys673 of NLRP3 to exert anti-inflammatory effects.

The NLRP3 inflammasome plays a pivotal role in various chronic inflammation-driven human diseases. However, no drugs specifically targeting NLRP3 inflammasome have been approved by the Food and Drug Administration (FDA) of the United States. In our current study, we showed that dimethyl fumarate (DMF) efficiently suppressed the activation of the NLRP3 inflammasome induced by multiple agonists and covalently modified Cys673 of NLRP3, thereby impeding the interaction between NLRP3 and NEK7. The inhibitory effect of DMF was nullified by anaplerosis of the Cys673 mutant (but not the wild-type) NLRP3 in Nlrp3-/- THP-1 cells. In vivo experiments, DMF demonstrated protective effects in the dextran sodium sulfate (DSS)-induced ulcerative colitis of WT mice, but not in Nlrp3-/- mice. In summary, our study identified DMF as a direct covalent inhibitor of NLRP3 and a potential candidate for the treatment of NLRP3 inflammasome-mediated diseases.

Injectable nanoparticle-crosslinked xyloglucan/ε-poly-l-lysine composite hydrogel with hemostatic, antimicrobial, and angiogenic properties for infected wound healing.

Skin wounds are susceptible to infection, leading to severe inflammatory reactions that can progress to chronic wounds, ultimately causing significant physical and mental distress to the patient. In this study, we propose an injectable composite hydrogel achieved through one-pot gelation of oxidized xyloglucan (OXG), cationic polyamide ε-poly-l-lysine (EPL), and surface amino-rich silicon nanoparticles (SiNPs). OXG exhibits commendable anti-inflammatory properties and provides crosslinking sites. SiNPs serve as mechanically reinforced crosslinkers, facilitating the construction of a dynamic Schiff base network. SiNPs significantly reduced the gelation time to 3 s and tripled the storage modulus of the hydrogels. Additionally, the combination of EPL and SiNPs demonstrated synergistic antimicrobial activity against both S. aureus and E. coli. Notably, the hydrogel effectively halted liver bleeding within 30 s. The hydrogel demonstrated outstanding shear-thinning and self-healing properties, crucial considerations for the design of injectable hydrogels. Furthermore, its efficacy was evaluated as a wound dressing in a mouse model with S. aureus infection. The results indicated that, compared to commercial products, the hydrogel exhibited a shorter wound healing time, decreased inflammation, thinner epithelium, increased hair follicles, enhanced neovascularization, and more substantial collagen deposition. These findings strongly suggest the promising potential of the proposed hydrogel as an effective wound dressing for the treatment of infected wounds.

Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz.

The variational quantum eigensolver (VQE) framework has been instrumental in advancing near-term quantum algorithms. However, parameter optimization remains a significant bottleneck for VQE, requiring a large number of measurements for successful algorithm execution. In this paper, we propose sequential optimization with approximate parabola (SOAP) as an efficient and robust optimizer specifically designed for parameter optimization of the unitary coupled-cluster ansatz on quantum computers. SOAP leverages sequential optimization and approximates the energy landscape as quadratic functions, minimizing the number of energy evaluations required to optimize each parameter. To capture parameter correlations, SOAP incorporates the average direction from previous iterations into the optimization direction set. Numerical benchmark studies on molecular systems demonstrate that SOAP achieves significantly faster convergence and greater robustness to noise compared with traditional optimization methods. Furthermore, numerical simulations of up to 20 qubits reveal that SOAP scales well with the number of parameters in the ansatz. The exceptional performance of SOAP is further validated through experiments on a superconducting quantum computer using a 2-qubit model system.

Dual Self-Assembly of Puerarin and Silk Fibroin into Supramolecular Nanofibrillar Hydrogel for Infected Wound Treatment.

The treatment of infected wounds remains a challenging biomedical problem. Some bioactive small-molecule hydrogelators with unique rigid structures can self-assemble into supramolecular hydrogels for wound healing. However, they are still suffered from low structural stability and bio-functionality. Herein, a supramolecular hydrogel antibacterial dressing with a dual nanofibrillar network structure is proposed. A nanofibrillar network created by a small-molecule hydrogelator, puerarin extracted from the traditional Chinese medicine Pueraria, is interconnected with a secondary macromolecular silk fibroin nanofibrillar network induced by Ga ions via charge-induced supramolecular self-assembly. The resulting hydrogel features adequate mechanical strength for sustainable retention at wounds. Good biocompatibility and efficient bacterial inhibition are obtained when the Ga ion concentration is 0.05%. Otherwise, the substantial release of Ga ions and puerarin endows the hydrogel with excellent hemostatic and antioxidative properties. In vivo, evaluation of a mouse-infected wound model demonstrates that its healing effect outperformed that of a commercially available silver-containing wound dressing. The experimental group successfully achieves a 100% wound closure rate on day 10. This study sheds new light on the design of nanofibrillar hydrogels based on supramolecular self-assembly of naturally derived bioactive molecules as well as their clinical use for treating chronic infected wounds.

Altitude-associated trends in bacterial communities in ultrahigh-altitude residences.

BACKGROUND: Indoor bacterial communities may change with altitude because their major contributors, outdoor bacterial communities, vary with altitude. People's health effects from bacteria inhalation exposure can also vary with altitude because human respiratory physiology changes with oxygen content in air. Accordingly, adjusting indoor bacterial communities may help to acclimate newcomers from low-altitude environments to ultrahigh-altitude environments. To lay the groundwork for further research, we aimed to first elucidate the bacterial communities in ultrahigh-altitude residences and the effects of altitude on these communities. We collected 187 environmental samples from residential communities at ultrahigh altitudes of 3811-4651 m in Ngari, China and sequenced bacterial 16S rRNA genes. RESULTS: On one hand, when abundant genera in ultrahigh-altitude residences and those reported by previous studies on low-altitude residences were compared, nine genera were shared, whereas other five genera were abundant only at ultrahigh altitudes. On the other hand, when the bacterial communities of residences at different ultrahigh altitudes were further compared, the bacterial composition in indoor surface samples varied significantly with altitude. The relative abundance of five bacterial genera in indoor air samples and 10 genera and three phyla in indoor surface samples varied monotonically with altitude. CONCLUSIONS: Altitude may be a long-neglected factor that shapes residential bacterial communities and thus warrants attention.

Preoperative H. pylori Eradication Therapy Facilitates Precise Delineation in Early Gastric Cancer with Current H. pylori Infection.

INTRODUCTION: Early gastric cancer with current Helicobacter pylori infection (HpC-EGC) is common, but it is still unclear whether H. pylori eradication therapy (Hp-ET) or endoscopic submucosal dissection (ESD) should be performed first. We evaluated Hp-ETs short-term effects on horizontal boundary delineations of HpC-EGC in ESD. METHODS: Prospectively enrolled HpC-EGC patients were randomly assigned to eradication or control groups. Operation scopes of HpC-EGC lesions were delineated with marking dots at 5 mm out of the endoscopic demarcation line by an independent endoscopist, unaware of eradication status, before formal circumferential incision. As representatives, precise delineation rate, the shortest distance of all marking dots to the pathological demarcation line in all slices of one intact resected specimen (Dmin), and negative marking dot specimen rate were examined. RESULTS: Twenty-three HpC-EGC patients (25 lesions) were allocated to eradication group and 26 patients (27 lesions) were allocated to the control group with similar eradication success rates and all were differentiated type. With improving background mucosa inflammation after Hp-ET and similar gastritis-like epithelium rates, 10 lesions (40.0%) in the eradication group were of precise delineation compared to control group with 2 lesions (7.4%) (relative risk = 5.40, 95% CI 1.31-22.28). Dmin of eradication and control groups were 4.17 ± 2.52 mm and 2.67 ± 2.30 mm (p = 0.029), accompanied by 4 (14.8%) and none (0.0%) specimens that exhibited positive marking dots (p = 0.11), respectively. CONCLUSION: For HpC-EGC patients, administrating eradication medication before ESD is beneficial for the precise delineation of lesions and reducing the risk of positive horizontal resection margins.

Multi-task bioassay pre-training for protein-ligand binding affinity prediction.

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

FDP-Na-induced enhancement of glycolysis impacts larval growth and development and chitin biosynthesis in fall webworm, Hyphantria cunea (Lepidoptera: Arctiidae).

Fructose 1, 6-diphosphate (FDP) is an endogenous intermediate in the glycolytic pathway, as well as an allosteric activator of phosphofructokinase (PFK). Based on the role in promoting glycolysis, FDP has been widely used as a therapeutic agent for mitigating the damage of endotoxemia and ischemia/reperfusion in clinical practice. However, the effect of exogenous FDP-induced glycolysis activation on insect carbohydrate metabolism and chitin synthesis remains largely unclear. Here, we investigated for the first time the effects of FDP-Na, an allosteric activator of PFK, on the growth and development of Hyphantria cunea larvae, a serious defoliator in agriculture and forestry, especially on glycolysis and chitin synthesis. The results showed that FDP-Na significantly restrained the growth and development of H. cunea larvae and resulted in larval lethality. After treatment with FDP-Na, hexokinase (HK), phosphofructokinase (PFK) and pyruvate kinase (PK) were significantly activated, and HcHK2, HcPFK, HcPK were dramatically upregulated, which suggested that FDP-Na enhanced glycolysis in H. cunea larvae. Meanwhile, FDP-Na also distinctly impacted chitin biosynthesis by disturbing transcriptions of genes in the chitin synthesis pathway, resulting in changes of chitin contents in the midgut and epidermis of H. cunea larvae. Therefore, we considered that FDP-Na caused the growth and development arrest, and impacted chitin biosynthesis, probably by disturbing in vivo glycolysis and carbohydrate metabolism in H. cunea larvae. The findings provide a new perspective on the mechanism by which glycolysis regulates insect growth and development, and lay the foundation for exploring the potential application of glycolysis activators in pest control as well.

Fabrication of Co3O4@ZnIn2S4 for photocatalytic hydrogen evolution: Insights into the synergistic mechanism of photothermal effect and heterojunction.

Integration of photothermal materials and photocatalysts can effectively improve photocatalytic hydrogen production. However, the synergistic mechanism of photothermal effect and heterojunction still need to be deeply investigated. Herein, Co3O4@ZnIn2S4 (ZIS) core-shell heterojunction was constructed as a photothermal/ photocatalytic integrated system for photocatalytic hydrogen production. The photothermal effect induced by Co3O4 boosts the surface reaction kinetic of hydrogen evolution with an apparent activation energy decrease from 42.0 kJ⋅mol-1 to 33.5 kJ⋅mol-1. The photothermal effect also increases the charge concentrations of Co3O4@ZIS, which ameliorates the conductivity of Co3O4@ZIS and thus benefits to charge transfer. In addition, a p-n junction forms between Co3O4 and ZIS and provides a built-in electric field to enhance charge separate and prolong charge life time. Benefiting from the synergy of photothermal effect and heterojunction, the photocatalytic performance of Co3O4@ZIS is significantly improved with a highest hydrogen evolution rate of 4515 µmol⋅g-1⋅h-1, which is about 3.5 times higher than that of pure ZIS. This work offers a full perspective to understand the photothermal/photocatalytic integrated conception for solar hydrogen production.

Chemical Modification of Polysaccharides: A Review of Synthetic Approaches, Biological Activity and the Structure-Activity Relationship.

Natural polysaccharides are macromolecular substances with great potential owing to their wide biological activity and low toxicity. However, not all polysaccharides have significant pharmacodynamic activity; hence, appropriate chemical modification methods can be selected according to the unique structural characteristics of polysaccharides to assist in enhancing and promoting the presentation of their biological activities. This review summarizes research progress on modified polysaccharides, including common chemical modification methods, the change in biological activity following modification, and the factors affecting the biological activity of chemically modified polysaccharides. At the same time, the difficulties and challenges associated with the structural modification of natural polysaccharides are also outlined in this review. Thus, research on polysaccharide structure modification is critical for improving the development and utilization of sugar products.

MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction.

Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (multi-fidelity modeling with subgraph pooling for pKa prediction), a novel pKa prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pKa prediction. To overcome the scarcity of accurate pKa data, low-fidelity data (computational pKa) was used to fit the high-fidelity data (experimental pKa) through transfer learning. The final MF-SuP-pKa model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pKa achieves superior performances to the state-of-the-art pKa prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

High-Throughput Sequencing Reveals N6-Methyladenosine-modified LncRNAs as Potential Biomarkers in Mice with Liver Fibrosis.

BACKGROUND: N6-methyladenosine (m6A) is the most frequent internal modification in eukaryotic RNA. Long noncoding RNAs (lncRNAs) are a new type of noncoding regulatory molecule with multiple cellular functions. Both are closely related to the occurrence and development of liver fibrosis (LF). However, the role of m6A-methylated lncRNAs in the progression of LF remains largely unknown. METHODS: In this study, HE and Masson staining were used to observe pathological changes in the liver, m6A-modified RNA immunoprecipitation sequencing (m6A-seq) was performed to systematically evaluate the m6A modification level of lncRNAs in LF mice, meRIP-qPCR and RT-qPCR were used to detect the m6A methylation level and RNA expression level of the target lncRNAs. RESULTS: A total of 415 m6A peaks were detected in 313 lncRNAs in liver fibrosis tissues. There were 98 significantly different m6A peaks in LF, which were located on 84 lncRNAs, of which 45.2% of the lncRNA length was between 200-400 bp. At the same time, the first three chromosomes of these methylated lncRNAs were chromosomes 7, 5 and 1. RNA sequencing identified 154 differentially expressed lncRNAs in LF. The joint analysis of m6A-seq and RNA-seq found that there were three lncRNAs with significant changes in m6A methylation and RNA expression levels: lncRNA H19, lncRNA Gm16023 and lncRNA Gm17586. Subsequently, the verification results showed that the m6A methylation levels of lncRNA H19 and lncRNA Gm17586 were significantly increased, while that of lncRNA Gm16023 was significantly decreased, and the RNA expression of three lncRNAs was significantly decreased. Through the establishment of a lncRNA-miRNA-mRNA regulatory network, the possible regulatory relationships of lncRNA H19, lncRNA Gm16023 and lncRNA Gm17586 in LF were revealed. CONCLUSION: This study revealed the unique m6A methylation pattern of lncRNAs in LF mice, suggesting that the m6A methylation modification of lncRNAs is related to the occurrence and development of LF.

A mutation-induced drug resistance database (MdrDB).

Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.

TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era.

TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high-performance in the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provides algorithms for variational quantum dynamics. TenCirChem's capabilities are demonstrated through various examples, such as the calculation of the potential energy curve of H2O with a 6-31G(d) basis set using a 34-qubit quantum circuit, the examination of the impact of quantum gate errors on the variational energy of the H2 molecule, and the exploration of the Marcus inverted region for charge transfer rate based on variational quantum dynamics. Furthermore, TenCirChem is capable of running real quantum hardware experiments, making it a versatile tool for both simulation and experimentation in the field of quantum computational chemistry.

Insights into the Oncogenic, Prognostic, and Immunological Role of BRIP1 in Pan-Cancer: A Comprehensive Data-Mining-Based Study.

Background: BRCA1 interacting helicase 1 (BRIP1), an ATP-dependent DNA helicase which belongs to an Iron-Sulfur (Fe-S) helicase cluster family with a DEAH domain, plays a key role in DNA damage and repair, Fanconi anemia, and development of several cancers including breast and ovarian cancer. However, its role in pan-cancer remains largely unknown. Methods: BRIP1 expression data of tumor and normal tissues were downloaded from the Cancer Genome Atlas, Genotype-Tissue Expression, and Human Protein Atlas databases. Correlation between BRIP1 and prognosis, genomic alterations, and copy number variation (CNV) as well as methylation in pan-cancer were further analyzed. Protein-protein interaction (PPI) and gene set enrichment and variation analysis (GSEA and GSVA) were performed to identify the potential pathways and functions of BRIP1. Besides, BRIP1 correlations with tumor microenvironment (TME), immune infiltration, immune-related genes, tumor mutation burden (TMB), microsatellite instability (MSI), and immunotherapy as well as antitumor drugs were explored in pan-cancer. Results: Differential analyses showed an increased expression of BRIP1 in 28 cancer types and its aberrant expression could be an indicator for prognosis in most cancers. Among the various mutation types of BRIP1 in pan-cancer, amplification was the most common type. BRIP1 expression had a significant correlation with CNV and DNA methylation in 23 tumor types and 16 tumor types, respectively. PPI, GSEA, and GSVA results validated the association between BRIP1 and DNA damage and repair, cell cycle, and metabolism. In addition, the expression of BRIP1 and its correlation with TME, immune-infiltrating cells, immune-related genes, TMB, and MSI as well as a variety of antitumor drugs and immunotherapy were confirmed. Conclusions: Our study indicates that BRIP1 plays an imperative role in the tumorigenesis and immunity of various tumors. It may not only serve as a diagnostic and prognostic biomarker but also can be a predictor for drug sensitivity and immunoreaction during antitumor treatment in pan-cancer.

Endoscopic mucosal resection using cold snare versus hot snare in treatment for 10-19 mm non-pedunculated colorectal polyps: protocol of a non-inferiority randomised controlled study.

INTRODUCTION: Cold polypectomy has the advantages of simple operation, less time-consuming and fewer complications. Guidelines have recommended cold snare polypectomy (CSP) to resect small polyps sized ≤5 mm and sessile polyps sized 6-9 mm. However, evidence is scarce regarding cold resection for non-pedunculated polyps sized ≥10 mm. Cold snare endoscopic mucosal resection (CS-EMR) combining CSP and submucosal injection was designed to improve the complete resection rate and reduce adverse events. We hypothesise that CS-EMR is non-inferior to conventional hot snare endoscopic mucosal resection (HS-EMR) in the resection of 10-19 mm non-pedunculated colorectal polyps. METHODS AND ANALYSIS: This study is a prospective, randomised, open-label, non-inferiority, single-centre trial. Outpatients scheduled to undergo a colonoscopy and present eligible polyps will be randomised to receive either CS-EMR or HS-EMR. The primary endpoint is the complete resection. Considering that HS-EMR of 10-19 mm colorectal polyps will yield a complete resection rate of at least 92% and a non-inferiority margin of -10%, a total of 232 polyps will be included (one-sided α, 2.5%; ß, 20%). The analyses are intended to evaluate first non-inferiority (lower limit 95% CI greater than -10% for group difference) and then superiority (lower limit 95% CI>0%) if non-inferiority is achieved. Secondary endpoints include en-bloc resection, the occurrence of adverse events, the use of endoscopic clips, resection time and cost. ETHICS AND DISSEMINATION: The study has been approved by the institutional review board of the Peking Union Medical College Hospital (No. K2203). All participants in the trial will provide written informed consent. The results of this trial will be published in an open-access way. TRIAL REGISTRATION NUMBER: NCT05545787.

Endoscopic ultrasound-guided tissue acquisition with or without rapid on-site evaluation for solid pancreatic lesions: five years of experience from a single center.

BACKGROUND: Endoscopic ultrasound (EUS)-guided tissue acquisition (TA) by EUS-guided fine needle aspiration (FNA) or fine needle biopsy (FNB) is a standard diagnostic procedure for solid pancreatic lesions. Whether rapid on-site evaluation (ROSE) should be used to support EUS-TA remains controversial. Here we assessed the diagnostic performance of EUS-TA with or without self-ROSE for solid pancreatic masses. METHODS: Three hundred and seventy EUS-TA cases with self-ROSE and 244 cases without ROSE were retrospectively enrolled between August 2018 and June 2022. All procedures including ROSE were performed by the attending endoscopist. Clinical data, EUS characteristics, and diagnostic performance for distinguishing benign from malignant solid pancreatic masses including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were compared between groups. RESULTS: Self-ROSE improved the diagnostic accuracy of solid pancreatic lesions by 16.7% in the EUS-TA group (p < 0.001) and by 18.9% in the EUS-FNA alone group (p < 0.001). Self-ROSE also improved the diagnostic sensitivity by 18.6% in the EUS-TA group (p < 0.001) and by 21.2% in the EUS-FNA alone group (p < 0.001). Improvements in the diagnostic accuracy by self-ROSE in the EUS-FNB group were not significant. 2.2 ± 0.7, 2.4 ± 0.9, 2.3 ± 0.7, 2.5 ± 0.9, 2.1 ± 0.6, and 2.1 ± 0.7 needle passes were required in the EUS-TA, EUS-FNA, and EUS-FNB with or without self-ROSE groups, respectively. CONCLUSIONS: Self-ROSE significantly improved the accuracy and sensitivity of EUS-FNA alone and EUS-TA diagnosis of solid pancreatic lesions and helped to reduce needle passes during the procedure. Whether self-ROSE benefits EUS-FNB and whether EUS-FNB alone is comparable to EUS-FNA with self-ROSE require further clarification.

Personalized Latent Structure Learning for Recommendation.

In recommender systems, users' behavior data are driven by the interactions of user-item latent factors. To improve recommendation effectiveness and robustness, recent advances focus on latent factor disentanglement via variational inference. Despite significant progress, uncovering the underlying interactions, i.e., dependencies of latent factors, remains largely neglected by the literature. To bridge the gap, we investigate the joint disentanglement of user-item latent factors and the dependencies between them, namely latent structure learning. We propose to analyze the problem from the causal perspective, where a latent structure should ideally reproduce observational interaction data, and satisfy the structure acyclicity and dependency constraints, i.e., causal prerequisites. We further identify the recommendation-specific challenges for latent structure learning, i.e., the subjective nature of users' minds and the inaccessibility of private/sensitive user factors causing universally learned latent structure to be suboptimal for individuals. To address these challenges, we propose the personalized latent structure learning framework for recommendation, namely PlanRec, which incorporates 1) differentiable Reconstruction, Dependency, and Acyclicity regularizations to satisfy the causal prerequisites; 2) Personalized Structure Learning (PSL) which personalizes the universally learned dependencies through probabilistic modeling; and 3) uncertainty estimation which explicitly measures the uncertainty of structure personalization, and adaptively balances personalization and shared knowledge for different users. We conduct extensive experiments on two public benchmark datasets from MovieLens and Amazon, and a large-scale industrial dataset from Alipay. Empirical studies validate that PlanRec discovers effective shared/personalized structures, and successfully balances shared knowledge and personalization via rational uncertainty estimation.

Discrete Time Crystal Enabled by Stark Many-Body Localization.

Discrete time crystals (DTCs) have recently attracted increasing attention, but most DTC models and their properties are only revealed after disorder average. In this Letter, we propose a simple disorder-free periodically driven model that exhibits nontrivial DTC order stabilized by Stark many-body localization (MBL). We demonstrate the existence of the DTC phase by analytical analysis from perturbation theory and convincing numerical evidence from observable dynamics. The new DTC model paves a new promising way for further experiments and deepens our understanding of DTCs. Since the DTC order does not require special quantum state preparation and the strong disorder average, it can be naturally realized on the noisy intermediate-scale quantum hardware with much fewer resources and repetitions. Moreover, in addition to the robust subharmonic response, there are other novel robust beating oscillations in the Stark-MBL DTC phase that are absent in random or quasiperiodic MBL DTCs.