Graph Prompt Learning: A Comprehensive Survey and Beyond

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by \url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and \url{https://github.com/sheldonresearch/ProG}, respectively

Clinical Characteristics and Economic Burden of Asthma in China: A Multicenter Retrospective Study

Asthma is a common chronic airway inflammation that produces a healthcare burden on the economy. We aim to obtain a better understanding of the clinical status and disease burden of patients with asthma in China. A retrospective study was carried out based on the computerized medical records in the Jinan Health Medical Big Data Platform between 2011 and 2019 (available data from 38 hospitals). The asthma severity of each patient was assessed retrospectively and categorized as mild, moderate, or severe according to Global Initiative for Asthma 2020 (GINA 2020). The results revealed that the majority (75.0%) of patients suffered from mild asthma. Patients treated with inhaled corticosteroids (ICS)/long-acting beta-agonists (LABA) at emergency department visits had lower frequencies of exacerbations compared with non-ICS/LABA-treated patients. The incidence rates for 1, 2, 3, and 4 exacerbation of the patients treated with ICS/LABA are lower than those treated without ICS/LABA (14.49 vs. 15.01%, 11.94% vs. 19.12%, 6.51% vs.12.92% and 4.10% vs. 9.35%). The difference got a statistical significance Chronic obstructive pulmonary disease (COPD) and gastroesophageal reflux disease (GERD), two comorbidities related to asthma, were risk factors for asthma exacerbation. Finally, patients who suffered from exacerbations produced a heavier economic burden compared to the patients who never suffered exacerbations (mean costs are ￥3,339.67 vs. ￥968.45 separately).  These results provide a reference for clinicians and patients to obtain a better treatment and therapy strategy management for people living with asthma

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF) ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF) graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks)-based classification method

Confning TiO2 Nanotubes in PECVD‑Enabled Graphene Capsules Toward Ultrafast K‑Ion Storage: In Situ TEM/XRD Study and DFT Analysis

© 2020, © 2020, The Author(s). Titanium dioxide (TiO2) has gained burgeoning attention for potassium-ion storage because of its large theoretical capacity, wide availability, and environmental benignity. Nevertheless, the inherently poor conductivity gives rise to its sluggish reaction kinetics and inferior rate capability. Here, we report the direct graphene growth over TiO2 nanotubes by virtue of chemical vapor deposition. Such conformal graphene coatings effectively enhance the conductive environment and well accommodate the volume change of TiO2 upon potassiation/depotassiation. When paired with an activated carbon cathode, the graphene-armored TiO2 nanotubes allow the potassium-ion hybrid capacitor full cells to harvest an energy/power density of 81.2 Wh kg−1/3746.6 W kg−1. We further employ in situ transmission electron microscopy and operando X-ray diffraction to probe the potassium-ion storage behavior. This work offers a viable and versatile solution to the anode design and in situ probing of potassium storage technologies that is readily promising for practical applications.[Figure not available: see fulltext.]

GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications

Group-level personality detection based on text generated networks

Personality analysis has been widely used in various social services such as mental healthcare, recommendation systems and so on because its natural explainability for AI applications in Web intelligence. With the penetration of Web2.0, traditional social researches have gradually turned to online social networks. However, for a long time, personality detection from online social texts has sunk into an embarrassing situation for the lack of large labeled datasets. Limited by supervised learning frameworks and small labeled datasets, prior works mainly detect one’s personality in the individual perspective, which may not well meet the challenges of massive un-labeled data in the near future. In this paper, we present a first look into group-level personality detection and we use an unsupervised feature learning method instead of supervised methods used in most related works. We propose AdaWalk, a new and novel model of group-level personality detection by learning the influence from text generated networks. The model uses different kernels to evaluate how much a given node should decide its walk path locally or globally. The advantage of AdaWalk is three-folded: a) the model is an unsupervised feature learning method, which means it relies less on annotations. b) by traversing the network, we can capture the influence in the group level, thus the analysis of one’s personality is not only based on individual records but also the information in groups. Therefore, AdaWalk can leverage small datasets more comprehensively. c) AdaWalk is scalable and can be easily transformed as distributed algorithms, which means it has more potential, compared with existing personality detection methods, to meet the massive data without annotations. We use AdaWalk to predict users’ Big Five personality scores in FIVE heterogeneous personality datasets. Compared with more than TEN famous related methods, AdaWalk outperforms the others, meanwhile verifying the significance of the group perspective and unsupervised feature learning methods in the application of personality analysis. To make our experiment repeatable, AdaWalk and related datasets are available at https://xiangguosun.strikingly.com

Efficacy and Safety of Eplerenone for Treating Chronic Kidney Disease: A Meta-Analysis

Background. In recent years, a large amount of clinical evidence and animal experiments have demonstrated the unique advantages of mineralocorticoid receptor antagonists (MRA) for treating chronic kidney disease (CKD). Aims. Accordingly, the present study aimed to systematically assess the second-generation selective MRAs eplerenone’s safety and effectiveness for treating CKD. Methods. Four databases (PubMed, The Cochrane Library, Embase, and Web of Science) were searched for randomized controlled trials (RCT) correlated with eplerenone for treating CKD up to September 21, 2022. By complying with the inclusion and exclusion criteria, literature screening, and data extraction were conducted. Results. A total of 19 randomized controlled articles involving 4501 cases were covered. As suggested from the meta-analysis, significant differences were reported with the 24-h urine protein (MD = −42.23, 95% confidence interval [CI] = -76.72 to −7.73, P = 0.02), urinary albumin-creatinine ratio (UACR) (MD = −23.57, 95% CI = −29.28 to −17.86, P < 0.00001), the systolic blood pressure (SBP) (MD = −2.73, 95% CI = −4.86 to −0.59, P = 0.01), and eGFR (MD = −1.56, 95% CI = −2.78 to −0.34, P = 0.01) in the subgroup of eplerenone vs placebo. The subgroups of eplerenone vs placebo (MD = 0.13, 95% CI = 0.07 to 0.18, P < 0.00001) and eplerenone vs thiazide diuretic (MD = 0.18, 95% CI = 0.13 to 0.23, P < 0.00001) showed the significantly increased potassium levels. However, no statistical significance was reported between the eplerenone treatment groups and the control in the effect exerted by serum creatinine (MD=0.03, 95% CI = −0.01 to 0.07, P = 0.12) and diastolic blood pressure (DBP) (MD = 0.11, 95% CI = −0.41 to 0.63, P = 0.68). Furthermore, significant risks of hyperkalemia were reported in the eplerenone group (K+ ≥ 5.5 mmol/l, RR = 1.70, 95%CI = 1.35 to 2.13, P =< 0.00001; K+ ≥ 6.0 mmol/l, RR = 1.61, 95% CIs = 1.06 to 2.44, P = 0.02), respectively. Conclusions. Eplerenone has beneficial effects on CKD by reducing urinary protein and the systolic blood pressure, but it also elevates the risk of hyperkalemia

Down-regulation of cellular FLICE-inhibitory protein (Long Form) contributes to apoptosis induced by Hsp90 inhibition in human lung cancer cells

<p>Abstract</p> <p>Background</p> <p>Cellular FLICE-Inhibitory Protein (long form, c-FLIP_L) is a critical negative regulator of death receptor-mediated apoptosis. Overexpression of c-FLIP_L has been reported in many cancer cell lines and is associated with chemoresistance. In contrast, down-regulation of c-FLIP may drive cancer cells into cellular apoptosis. This study aims to demonstrate that inhibition of the heat shock protein 90 (Hsp90) either by inhibitors geldanamycin/17-N-Allylamino-17-demethoxygeldanamycin (GA/17-AAG) or siRNA technique in human lung cancer cells induces c-FLIP_L degradation and cellular apoptosis through C-terminus of Hsp70-interacting protein (CHIP)-mediated mechanisms.</p> <p>Methods</p> <p>Calu-1 and H157 cell lines (including H157-c-FLIP_L overexpressing c-FLIP_L and control cell H157-lacZ) were treated with 17-AAG and the cell lysates were prepared to detect the given proteins by Western Blot and the cell survival was assayed by SRB assay. CHIP and Hsp90 α/β proteins were knocked down by siRNA technique. CHIP and c-FLIP_L plasmids were transfected into cells and immunoprecipitation experiments were performed to testify the interactions between c-FLIP_L, CHIP and Hsp90.</p> <p>Results</p> <p>c-FLIP_L down-regulation induced by 17-AAG can be reversed with the proteasome inhibitor MG132, which suggested that c-FLIP_L degradation is mediated by a ubiquitin-proteasome system. Inhibition of Hsp90α/β reduced c-FLIP_L level, whereas knocking down CHIP expression with siRNA technique inhibited c-FLIP_L degradation. Furthermore, c-FLIP_L and CHIP were co-precipitated in the IP complexes. In addition, overexpression of c-FLIP_L can rescue cancer cells from apoptosis. When 17-AAG was combined with an anti-cancer agent celecoxib(CCB), c-FLIP_L level declined further and there was a higher degree of caspase activation.</p> <p>Conclusion</p> <p>We have elucidated c-FLIP_L degradation contributes to apoptosis induced by Hsp90 inhibition, suggesting c-FLIP and Hsp90 may be the promising combined targets in human lung cancer treatment.</p