Triple Sequence Learning for Cross-domain Recommendation

Cross-domain recommendation (CDR) aims to leverage the users' behaviors in both source and target domains to improve the target domain's performance. Conventional CDR methods typically explore the dual relations between the source and target domains' behavior sequences. However, they ignore modeling the third sequence of mixed behaviors that naturally reflects the user's global preference. To address this issue, we present a novel and model-agnostic Triple sequence learning for cross-domain recommendation (Tri-CDR) framework to jointly model the source, target, and mixed behavior sequences in CDR. Specifically, Tri-CDR independently models the hidden user representations for the source, target, and mixed behavior sequences, and proposes a triple cross-domain attention (TCA) to emphasize the informative knowledge related to both user's target-domain preference and global interests in three sequences. To comprehensively learn the triple correlations, we design a novel triple contrastive learning (TCL) that jointly considers coarse-grained similarities and fine-grained distinctions among three sequences, ensuring the alignment while preserving the information diversity in multi-domain. We conduct extensive experiments and analyses on two real-world datasets with four domains. The significant improvements of Tri-CDR with different sequential encoders on all datasets verify the effectiveness and universality. The source code will be released in the future.Comment: 11 pages, 5 figures, under revie

Lower incidence of new-onset severe conduction disturbances after transcatheter aortic valve implantation with bicuspid aortic valve in patients with no baseline conduction abnormality: a cross-sectional investigation in a single center in China

BackgroundWith technological advancements, the incidence of most transcatheter aortic valve implantation (TAVI)-related complications, with the exception of conduction disturbances, has decreased. Bicuspid aortic valve (BAV) is also no longer considered a contraindication to TAVI; however, the effect of BAV on postoperative conduction disturbances after TAVI is unknown.MethodsWe collected information on patients who met the indications for TAVI and successfully underwent TAVI at our center between January 2018 and January 2021. Patients with preoperative pacemaker implantation status or conduction disturbances (atrioventricular block, bundle branch block, and intraventricular block) were excluded. Based on imaging data, the patients were categorized into the BAV group and the tricuspid aortic valve (TAV) group. The incidence of new perioperative conduction disturbances was compared between the two groups.ResultsA total of 187 patients were included in this study, 64 (34.2%) of whom had BAV. The incidence of third-degree block in the BAV group was 1.6%, which was lower than that (13.0%) in the TAV group (P < 0.05). Multivariate logistic regression results showed that the risk of third-degree conduction disturbances was 15-fold smaller in the BAV group than that in the TAV group [relative risk (RR) = 0.067, 95% CI = 0.008–0.596, P < 0.05]. The risk of other blocks in the BAV group was about half of that in the TAV group (RR = 0.498, 95% CI = 0.240–1.032); however, the difference was not statistically significant (P > 0.05).ConclusionThe present study found that patients with BAV had a lower rate of third-degree conduction disturbances after TAVI than patients with TAV

Mechanism of Virus Inactivation by Cold Atmospheric-Pressure Plasma and Plasma-Activated Water

ABSTRACT Viruses cause serious pathogenic contamination that severely affects the environment and human health. Cold atmospheric-pressure plasma efficiently inactivates pathogenic bacteria; however, the mechanism of virus inactivation by plasma is not fully understood. In this study, surface plasma in argon mixed with 1% air and plasma-activated water was used to treat water containing bacteriophages. Both agents efficiently inactivated bacteriophages T4, ϕ174, and MS2 in a time-dependent manner. Prolonged storage had marginal effects on the antiviral activity of plasma-activated water. DNA and protein analysis revealed that the reactive species generated by plasma damaged both nucleic acids and proteins, consistent with the morphological examination showing that plasma treatment caused the aggregation of bacteriophages. The inactivation of bacteriophages was alleviated by the singlet oxygen scavengers, demonstrating that singlet oxygen played a primary role in this process. Our findings provide a potentially effective disinfecting strategy to combat the environmental viruses using cold atmospheric-pressure plasma and plasma-activated water. IMPORTANCE Contamination with pathogenic and infectious viruses severely threatens human health and animal husbandry. Current methods for disinfection have different disadvantages, such as inconvenience and contamination of disinfection by-products (e.g., chlorine disinfection). In this study, atmospheric surface plasma in argon mixed with air and plasma-activated water was found to efficiently inactivate bacteriophages, and plasma-activated water still had strong antiviral activity after prolonged storage. Furthermore, it was shown that bacteriophage inactivation was associated with damage to nucleic acids and proteins by singlet oxygen. An understanding of the biological effects of plasma-based treatment is useful to inform the development of plasma into a novel disinfecting strategy with convenience and no by-product

Adaptive sliding mode attitude control of 2-degrees-of-freedom helicopter system with actuator saturation and disturbances

The modelling uncertainties, external disturbance and actuator saturation issues will degrade the performance and even the safety of flight. To improve control performance, this study proposes an adaptive U-model based double sliding control (UDSMC) algorithm combined with a radial basis function neural network (RBFNN) for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system. Firstly, the adaptive RBFNN is designed to approximate the system dynamics with unknown uncertainties. Furthermore, two adaptive laws are designed to deal with unknown external disturbances and actuator saturation errors. The global stability of the proposed helicopter control system is rigorously guaranteed by the Lyapunov stability analysis, realizing precise attitude tracking control. Finally, the comparative experiments with conventional SMC and adaptive SMC algorithms conducted on the Quanser Aero2 platform demonstrate the effectiveness and feasibility of the proposed 2-DOF helicopter control algorithm

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and our code can be obtained from https://github.com/THU-KEG/MAVEN-Argument.Comment: Working in progres

The prognostic value of whole-genome DNA methylation in response to Leflunomide in patients with Rheumatoid Arthritis

ObjectiveAlthough Leflunomide (LEF) is effective in treating rheumatoid arthritis (RA), there are still a considerable number of patients who respond poorly to LEF treatment. Till date, few LEF efficacy-predicting biomarkers have been identified. Herein, we explored and developed a DNA methylation-based predictive model for LEF-treated RA patient prognosis.MethodsTwo hundred forty-five RA patients were prospectively enrolled from four participating study centers. A whole-genome DNA methylation profiling was conducted to identify LEF-related response signatures via comparison of 40 samples using Illumina 850k methylation arrays. Furthermore, differentially methylated positions (DMPs) were validated in the 245 RA patients using a targeted bisulfite sequencing assay. Lastly, prognostic models were developed, which included clinical characteristics and DMPs scores, for the prediction of LEF treatment response using machine learning algorithms.ResultsWe recognized a seven-DMP signature consisting of cg17330251, cg19814518, cg20124410, cg21109666, cg22572476, cg23403192, and cg24432675, which was effective in predicting RA patient’s LEF response status. In the five machine learning algorithms, the support vector machine (SVM) algorithm provided the best predictive model, with the largest discriminative ability, accuracy, and stability. Lastly, the AUC of the complex model(the 7-DMP scores with the lymphocyte and the diagnostic age) was higher than the simple model (the seven-DMP signature, AUC:0.74 vs 0.73 in the test set).ConclusionIn conclusion, we constructed a prognostic model integrating a 7-DMP scores with the clinical patient profile to predict responses to LEF treatment. Our model will be able to effectively guide clinicians in determining whether a patient is LEF treatment sensitive or not

Haplotype-resolved Genome of Sika Deer Reveals Allele-specific Gene Expression and Chromosome Evolution

Despite the scientific and medicinal importance of diploid sika deer (Cervus nippon), its genome resources are limited and haplotype-resolved chromosome-scale assembly is urgently needed. To explore mechanisms underlying the expression patterns of the allele-specific genes in antlers and the chromosome evolution in Cervidae, we report, for the first time, a high-quality haplotype-resolved chromosome-scale genome of sika deer by integrating multiple sequencing strategies, which was anchored to 32 homologous groups with a pair of sex chromosomes (XY). Several expanded genes (RET, PPP2R1A, PPP2R1B, YWHAB, YWHAZ, and RPS6) and positively selected genes (eIF4E, Wnt8A, Wnt9B, BMP4, and TP53) were identified, which could contribute to rapid antler growth without carcinogenesis. A comprehensive and systematic genome-wide analysis of allele expression patterns revealed that most alleles were functionally equivalent in regulating rapid antler growth and inhibiting oncogenesis. Comparative genomic analysis revealed that chromosome fission might occur during the divergence of sika deer and red deer (Cervus elaphus), and the olfactory sensation of sika deer might be more powerful than that of red deer. Obvious inversion regions containing olfactory receptor genes were also identified, which arose since the divergence. In conclusion, the high-quality allele-aware reference genome provides valuable resources for further illustration of the unique biological characteristics of antler, chromosome evolution, and multi-omics research of cervid animals

Quartz Crystal Microbalance Humidity Sensors Based on Structured Graphene Oxide Membranes with Magnesium Ions: Design, Mechanism and Performance

The application of graphene oxide (GO)-based membranes combined with a quartz crystal microbalance (QCM) as a humidity sensor has attracted great interest over the past few years. Understanding the influence of the structure of the GO membrane (GOM) on the adsorption/desorption of water molecules and the transport mechanism of water molecules in the membrane is crucial for development of applications using GOM-based humidity sensors. In this paper, by investigating the effects of oxygen-containing groups, flake size and interlayer spacing on the performance of humidity sensing, it was found that humidity-sensing performance could be improved by rational membrane-structure design and the introduction of magnesium ions, which can expand the interlayer spacing. Therefore, a novel HGO&GO&Mg2+ structure prepared by uniformly doping magnesium ions into GO&HGO thin composite membranes was designed for humidity sensing from 11.3% RH to 97.3% RH. The corresponding sensor exhibits a greatly improved humidity sensitivity (~34.3 Hz/%RH) compared with the original pure GO-based QCM sensor (~4.0 Hz/%RH). In addition, the sensor exhibits rapid response/recovery times (7 s/6 s), low hysteresis (~3.2%), excellent repeatability and good stability. This research is conducive to understanding the mechanism of GOM-based humidity sensors. Owing to its good humidity-sensing properties, the HGO&GO&Mg2+ membrane-based QCM humidity sensor is a good candidate for humidity sensing

Modeling of Wind Farm Output Considering Wind Speed Spatiotemporal Distribution and Wind Turbine Operational Statuses

This paper proposes a methodology to model the external characteristics of a wind farm in service or in planning stage. For system operators, the interested external characteristics of a wind farm are mainly the aggregated available output that the wind farm can feed into power system. In order to accurately and efficiently model the wind production, the wind speed spatiotemporal distribution is characterized. The proposed model takes wake effect and time delay into account, which can improve the spatiotemporal resolutions of the input data provided by numerical weather prediction (NWP). In addition, the layouts and the mechanical characteristics of wind turbine generators (WTGs) are considered in the wind farm output model. How the operational statuses of WTGs influence the wind farm output is discussed. The proposed methodology is validated using the measurements and NWP data of an actual wind farm in China. The results demonstrate the effectiveness of the proposed method for modeling the external characteristics of a wind farm. The wind farm outputs provided by the proposed model can be used as the references for the generation scheduling or for planning of new wind farms

Increasing cure rates of solid tumors by immune checkpoint inhibitors

Abstract Immunotherapy has become the central pillar of cancer therapy. Immune checkpoint inhibitors (ICIs), a major category of tumor immunotherapy, reactivate preexisting anticancer immunity. Initially, ICIs were approved only for advanced and metastatic cancers in the salvage setting after or concurrent with chemotherapy at a response rate of around 20–30% with a few exceptions. With significant progress over the decade, advances in immunotherapy have led to numerous clinical trials investigating ICIs as neoadjuvant and/or adjuvant therapies for resectable solid tumors. The promising results of these trials have led to the United States Food and Drug Administration (FDA) approvals of ICIs as neoadjuvant or adjuvant therapies for non-small cell lung cancer, melanoma, triple-negative breast cancer, and bladder cancer, and the list continues to grow. This therapy represents a paradigm shift in cancer treatment, as many early-stage cancer patients could be cured with the introduction of immunotherapy in the early stages of cancer. Therefore, this topic became one of the main themes at the 2021 China Cancer Immunotherapy Workshop co-organized by the Chinese American Hematologist and Oncologist Network, the China National Medical Products Administration and the Tsinghua University School of Medicine. This review article summarizes the current landscape of ICI-based immunotherapy, emphasizing the new clinical developments of ICIs as curative neoadjuvant and adjuvant therapies for early-stage disease