LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

The extraordinary performance of large language models has not only reshaped the research landscape in the field of NLP but has also demonstrated its exceptional applicative potential in various domains. However, the potential of these models in mining relationships from graph data remains under-explored. Graph neural networks, as a popular research area in recent years, have numerous studies on relationship mining. Yet, current cutting-edge research in graph neural networks has not been effectively integrated with large language models, leading to limited efficiency and capability in graph relationship mining tasks. A primary challenge is the inability of LLMs to deeply exploit the edge information in graphs, which is critical for understanding complex node relationships. This gap limits the potential of LLMs to extract meaningful insights from graph structures, limiting their applicability in more complex graph-based analysis. We focus on how to utilize existing LLMs for mining and understanding relationships in graph data, applying these techniques to recommendation tasks. We propose an innovative framework that combines the strong contextual representation capabilities of LLMs with the relationship extraction and analysis functions of GNNs for mining relationships in graph data. Specifically, we design a new prompt construction framework that integrates relational information of graph data into natural language expressions, aiding LLMs in more intuitively grasping the connectivity information within graph data. Additionally, we introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information in graph data. Our evaluation on real-world datasets demonstrates the framework's ability to understand connectivity information in graph data

Data-Centric Financial Large Language Models

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains

Digital Heritage

Abstract Natural and cultural heritage, the common wealth of human beings, are keys to human understanding of the evolution of our planet and social development. The protection and conservation of natural and cultural heritage is the common responsibility of all mankind. Spatial information technology provides a new applied theory and tool for the protection and utilization of natural and cultural heritage. This chapter is divided into four parts. The first part elaborates the connotation of digital heritage, the differences and connections between digital heritage and physical heritage, the technology of digital heritage formation and the research objectives and content of digital heritage. Parts 2 and 3 discuss the contents and methods of digital natural heritage and cultural heritage, respectively, and some practical case studies. In the fourth part, the future development trends of digital heritage research in protection and utilization are described, as well as six research directions that deserve attention

Visual and refractive outcomes of opposite clear corneal incision combined with rotationally asymmetric multifocal intraocular lens implantation

PurposeTo evaluate the visual and refractive outcomes of astigmatic cataract patients following opposite clear corneal incision (OCCI) combined with rotationally asymmetric multifocal intraocular lens (IOL) implantation.SettingDepartment of Ophthalmology, Zhongshan Hospital (Xiamen), Fudan University, People’s Republic of China.DesignRetrospective cohort study.MethodsThis study comprised 58 cataract eyes of 54 patients with corneal astigmatism who underwent phacoemulsification and rotationally asymmetric multifocal IOL implantation which received either OCCI (OCCI group) or a single clear corneal incision (SCCI group). The follow-up period was 3 months after surgery. Distance, intermediate and near visual acuity, refractive outcomes, and corneal anterior keratometry were compared between the two groups. Vector analysis was used to evaluate astigmatism correction.ResultsThree months after surgery, the distance, intermediate and near visual acuity, and sphere remained comparable between the two groups, but a significant difference was detected in residual astigmatism and anterior corneal keratometric astigmatism. In the OCCI group, the residual astigmatism and keratometric astigmatism were −0.60 ± 0.29 D and 0.59 ± 0.28 D, respectively, which were lower than those in SCCI groups (−1.18 ± 0.47 D and 1.15 ± 0.45 D, both p < 0.05). In vector analysis, the difference vector (DV), angle of error (AoE), absolute AoE, index of success (IoS) and correction index (CI) were statistically significantly different between the two groups (p < 0.05).ConclusionOCCI combined with rotationally asymmetric multifocal intraocular lens implantation showed predictable and desirable efficacy in treating cataract patients with astigmatism

Manganese oxide integrated catalytic ceramic membrane for degradation of organic pollutants using sulfate radicals

Membrane separation and advanced oxidation processes (AOPs) have been respectively demonstrated to be effective for a variety of water and/or wastewater treatments. Innovative integration of membrane with catalytic oxidation is thus expected to be more competing for more versatile applications. In this study, ceramic membranes (CMs) integrated with manganese oxide (MnO2) were designed and fabricated via a simple one-step ball-milling method with a high temperature sintering. Functional membranes with different loadings of MnO2 (1.67%, 3.33% and 6.67% of the total membrane mass) were then fabricated. The micro-structures and compositions of the catalytic membranes were investigated by a number of advanced characterisations. It was found that the MnO2 nanocatalysts (10–20 nm) were distributed uniformly around the Al2O3 particles (500 nm) of the membrane basal material, and can provide a large amount of active sites for the peroxymonosulfate (PMS) activation which can be facilitated within the pores of the catalytic membrane. The catalytic degradation of 4-hydroxylbenzoic acid (HBA), which is induced by the sulfate radicals via PMS activation, was investigated in a cross-flow membrane unit. The degradation efficiency slightly increased with a higher MnO2 loading. Moreover, even with the lowest loading of MnO2 (1.67%), the effectiveness of HBA degradation was still prominent, shown by that a 98.9% HBA degradation was achieved at the permeated side within 30 min when the initial HBA concentration was 80 ppm. The stability and leaching tests revealed a good stability of the catalytic membrane even after the 6th run. Electron paramagnetic resonance (EPR) and quenching tests were used to investigate the mechanism of PMS activation and HBA degradation. Both sulfate radicals (SO4˙−) and hydroxyl radicals (˙OH) were generated in the catalytic membrane process. Moreover, the contribution from non-radical process was also observed. This study provides a novel strategy for preparing a ceramic membrane with the function of catalytic degradation of organic pollutants, as well as outlining into future integration of separation and AOPs

Identification and investigation of depression-related molecular subtypes in inflammatory bowel disease and the anti-inflammatory mechanisms of paroxetine

BackgroundUp to 40 per cent of people with active inflammatory bowel disease (IBD) also suffer from mood disorders such as anxiety and depression. Notwithstanding, the fundamental biological pathways driving depression in IBD remain unknown.MethodsWe identified 33 core genes that drive depression in IBD patients and performed consensus molecular subtyping with the NMF algorithm in IBD. The CIBERSORT were employed to quantify the immune cells. Metabolic signature was characterized using the “IOBR” R package. The scoring system (D. score) based on PCA. Pre-clinical models are constructed using DSS.ResultsUsing transcriptome data from the GEO database of 630 IBD patients, we performed a thorough analysis of the correlation between IBD and depression in this research. Firstly, the samples were separated into two different molecular subtypes (D. cluster1 and D. cluster2) based on their biological signatures. Moreover, the immunological and metabolic differences between them were evaluated, and we discovered that D. cluster2 most closely resembled IBD patients concomitant with depression. We also developed a scoring system to assess the IBD-related depression and predict clinical response to anti-TNF- therapy, with a higher D. score suggesting more inflammation and worse reaction to biological therapies. Ultimately, we also identified through animal experiments an antidepressant, paroxetine, has the added benefit of lowering intestinal inflammation by controlling microorganisms in the digestive tract.ConclusionsThis study highlights that IBD patients with or without depression show significant variations and antidepressant paroxetine may help reduce intestinal inflammation

Minor envelope proteins from GP2a to GP4 contribute to the spread pattern and yield of type 2 PRRSV in MARC-145 cells

In China, porcine reproductive and respiratory syndrome virus (PRRSV) vaccines are widely used. These vaccines, which contain inactivated and live attenuated vaccines (LAVs), are produced by MARC-145 cells derived from the monkey kidney cell line. However, some PRRSV strains in MARC-145 cells have a low yield. Here, we used two type 2 PRRSV strains (CH-1R and HuN4) to identify the genes responsible for virus yield in MARC-145 cells. Our findings indicate that the two viruses have different spread patterns, which ultimately determine their yield. By replacing the viral envelope genes with a reverse genetics system, we discovered that the minor envelope proteins, from GP2a to GP4, play a crucial role in determining the spread pattern and yield of type 2 PRRSV in MARC-145 cells. The cell-free transmission pattern of type 2 PRRSV appears to be more efficient than the cell-to-cell transmission pattern. Overall, these findings suggest that GP2a to GP4 contributes to the spread pattern and yield of type 2 PRRSV

Increasing Upstream Chromatin Long–Range Interactions May Favor Induction of Circular RNAs in LysoPC-Activated Human Aortic Endothelial Cells

Circular RNAs (circRNAs) are non-coding RNAs that form covalently closed continuous loops, and act as gene regulators in physiological and disease conditions. To test our hypothesis that proatherogenic lipid lysophosphatidylcholine (LPC) induce a set of circRNAs in human aortic endothelial cell (HAEC) activation, we performed circRNA analysis by searching our RNA-Seq data from LPC-activated HAECs, and found: (1) LPC induces significant modulation of 77 newly characterized cirRNAs, among which 47 circRNAs (61%) are upregulated; (2) 34 (72%) out of 47 upregulated circRNAs are upregulated when the corresponding mRNAs are downregulated, suggesting that the majority of circRNAs are upregulated presumably via LPC-induced “abnormal splicing” when the canonical splicing for generation of corresponding mRNAs is suppressed; (3) Upregulation of 47 circRNAs is temporally associated with mRNAs-mediated LPC-upregulated cholesterol synthesis-SREBP2 pathway and LPC-downregulated TGF-β pathway; (4) Increase in upstream chromatin long-range interaction sites to circRNA related genes is associated with preferred circRNA generation over canonical splicing for mRNAs, suggesting that shifting chromatin long-range interaction sites from downstream to upstream may promote induction of a list of circRNAs in lysoPC-activated HAECs; (5) Six significantly changed circRNAs may have sponge functions for miRNAs; and (6) 74% significantly changed circRNAs contain open reading frames, suggesting that putative short proteins may interfere with the protein interaction-based signaling. Our findings have demonstrated for the first time that a new set of LPC-induced circRNAs may contribute to homeostasis in LPC-induced HAEC activation. These novel insights may lead to identifications of new therapeutic targets for treating metabolic cardiovascular diseases, inflammations, and cancers