Graph Homomorphism Revisited for Graph Matching

In a variety of emerging applications one needs to decide whether a graph G matches another G p , i.e. , whether G has a topological structure similar to that of G p . The traditional notions of graph homomorphism and isomorphism often fall short of capturing the structural similarity in these applications. This paper studies revisions of these notions, providing a full treatment from complexity to algorithms. (1) We propose p-homomorphism (p -hom) and 1-1 p -hom, which extend graph homomorphism and subgraph isomorphism, respectively, by mapping edges from one graph to paths in another, and by measuring the similarity of nodes . (2) We introduce metrics to measure graph similarity, and several optimization problems for p -hom and 1-1 p -hom. (3) We show that the decision problems for p -hom and 1-1 p -hom are NP-complete even for DAGs, and that the optimization problems are approximation-hard. (4) Nevertheless, we provide approximation algorithms with provable guarantees on match quality. We experimentally verify the effectiveness of the revised notions and the efficiency of our algorithms in Web site matching, using real-life and synthetic data. </jats:p

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error

Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects

The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications

Specific fungi associated with response to capsulized fecal microbiota transplantation in patients with active ulcerative colitis

ObjectiveFecal microbiota transplantation (FMT) is a novel microbial treatment for patients with ulcerative colitis (UC). In this study, we performed a clinical trial of capsulized FMT in UC patients to determine the association between the gut fungal community and capsulized FMT outcomes.DesignThis study recruited patients with active UC (N = 22) and healthy individuals (donor, N = 9) according to the criteria. The patients received capsulized FMT three times a week. Patient stool samples were collected before (week 0) and after FMT follow-up visits at weeks 1, 4, and 12. Fungal communities were analysed using shotgun metagenomic sequencing.ResultsAccording to metagenomic analysis, fungal community evenness index was greater in samples collected from patients, and the overall fungal community was clustered among the samples collected from donors. The dominant fungi in fecal samples collected from donors and patients were Ascomycota and Basidiomycota. However, capsulized FMT ameliorated microbial fungal diversity and altered fungal composition, based on metagenomic analysis of fecal samples collected before and during follow-up visits after capsulized FMT. Fungal diversity decreased in samples collected from patients who achieved remission after capsulized FMT, similar to samples collected from donors. Patients achieving remission after capsulized FMT had specific enrichment of Kazachstania naganishii, Pyricularia grisea, Lachancea thermotolerans, and Schizosaccharomyces pombe compared with patients who did not achieve remission. In addition, the relative abundance of P. grisea was higher in remission fecal samples during the follow-up visit. Meanwhile, decreased levels of pathobionts, such as Candida and Debaryomyces hansenii, were associated with remission in patients receiving capsulized FMT.ConclusionIn the metagenomic analysis of fecal samples from donors and patients with UC receiving capsulized FMT, shifts in gut fungal diversity and composition were associated with capsulized FMT and validated in patients with active UC. We also identified the specific fungi associated with the induction of remission. ClinicalTrails.gov (NCT03426683)

Key technologies for medium and low voltage DC distribution system

Development of the medium and low voltage DC distribution system is of great significance to a regional transmission of electric energy, increasing a penetration rate of new energy, and enhancing a safety of the operation of the AC/DC interconnected grid. This paper first summarizes the medium and low voltage DC distribution system schemes and plans put forward by many countries, and then elaborate status of under-construction medium and low voltage DC distribution system project cases in China. Based on these project cases, this paper analyzes key issues involved in the medium and low voltage DC distribution system topologies, equipment, operation control technologies and DC fault protections, in order to provide theoretical and technical reference for future medium and low voltage DC distribution system-related projects. Finally, this paper combines a current China research status to summarize and give a prediction about the future research direction of medium and low voltage DC distribution system, which can provide reference for the research of medium and low voltage DC distribution system

Autoantibodies against eukaryotic translation elongation factor 1 delta in two patients with autoimmune cerebellar ataxia

BackgroundAutoantibodies are useful biomarkers for the early detection and diagnosis of autoimmune cerebellar ataxia (ACA).ObjectiveTo identify novel autoantibody candidates in ACA patients.MethodsPatients with cerebellar ataxia of unknown cause were recruited from July 2018 to February 2023. Anti-neural autoantibodies in patient samples were detected by tissue-based indirect immunofluorescence assay (TBA) on rat cerebellum sections. TBA-positive samples were further screened for well-established anti-neural autoantibodies using commercial kits. Tissue-immunoprecipitation (TIP) and subsequent mass spectrometric (MS) analysis were used to explore the target antigens of autoantibodies in samples that were TBA-positive but negative for known autoantibodies. The specific binding between autoantibodies and the identified target antigen was confirmed by neutralization experiments, recombinant cell-based indirect immunofluorescence assay (CBA), and western blotting experiments.ResultsThe eukaryotic translation elongation factor 1 delta (EEF1D) protein was identified as a target antigen of autoantibodies in samples from a 43-year-old female ACA patient, while the specific binding of autoantibodies and EEF1D was confirmed by subsequent experiments. A second anti-EEF1D autoantibody-positive ACA patient, a 59-year-old female, was detected in simultaneous screening. The main clinical manifestations in each of the two patients were cerebellar syndrome, such as unsteady walking and limb ataxia. Both patients received immunotherapy, including corticosteroids, intravenous immunoglobulin, and mycophenolate mofetil. Their outcomes provided evidence to support the effectiveness of immunotherapy, but the cerebellar atrophy that occurred before treatment may be irreversible.ConclusionIn the current study, we identified anti-EEF1D autoantibody as a novel autoantibody candidate in ACA. Its pathological roles and diagnostic value need to be further verified in larger-scale studies

Acupuncture for Lateral Epicondylitis: A Systematic Review

Objective. This systematic review aimed to assess the effectiveness and safety of acupuncture for lateral epicondylitis (LE). Methods. Seven databases and the WHO International Clinical Trials Registry Platform Search Portal were searched to identify relevant studies. The data were extracted and assessed by two independent authors, and Review Manager Software (V.5.3) was used for data synthesis with effect estimate presented as standard mean difference (SMD) and mean difference (MD) with a 95% confidence interval. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to assess the level of evidence. Results. Four RCTs with 309 participants were included with poor methodological quality. Participants who received acupuncture and acupuncture plus moxibustion with material insulation were likely to have an improvement in elbow functional status and/or myodynamia. The overall quality rated by GRADE was from very low to low. Two studies reported that the needle pain would be the main reason for the dropout. Conclusion. For the small number of included studies with poor methodological quality, no firm conclusion can be drawn regarding the effect of acupuncture of elbow functional status and myodynamia for LE. This trial is registered with CRD42015016199

Leveraging Fecal Bacterial Survey Data to Predict Colorectal Tumors

Colorectal cancer (CRC) ranks second in cancer-associated mortality and third in the incidence worldwide. Most of CRC follow adenoma-carcinoma sequence, and have more than 90% chance of survival if diagnosed at early stage. But the recommended screening by colonoscopy is invasive, expensive, and poorly adhered to. Recently, several studies reported that the fecal bacteria might provide non-invasive biomarkers for CRC and precancerous tumors. Therefore, we collected and uniformly re-analyzed these published fecal 16S rDNA sequencing datasets to verify the association and identify biomarkers to classify and predict colorectal tumors by random forest method. A total of 1674 samples (330 CRC, 357 advanced adenoma, 141 adenoma, and 846 control) from 7 studies were analyzed in this study. By random effects model and fixed effects model, we observed significant differences in alpha-diversity and beta-diversity between individuals with CRC and the normal colon, but not between adenoma and the normal. We identified various bacterial genera with significant odds ratios for colorectal tumors at different stages. Through building random forest model with 10-fold cross-validation as well as new test datasets, we classified individuals with CRC, advanced adenoma, adenoma and normal colon. All approaches obtained comparable performance at entire OTU level, entire genus level, and the common genus level as measured using AUC. When combined all samples, the AUC of random forest model based on 12 common genera reached 0.846 for CRC, although the predication performed poorly for advance adenoma and adenoma