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119. Renewable quantile regression for streaming data sets

Author

Jiang, R and Yu, K

Subjects
quantile regression, optimisation algorithm, Artificial Intelligence, Cognitive Neuroscience, streaming data, variable selection, online updating, Computer Science Applications
Abstract

Copyright © 2022 The Author(s). Online updating is an important statistical method for the analysis of big data arriving in streams due to its ability to break the storage barrier and the computational barrier under certain circumstances. The quantile regression, as a widely used regression model in many fields, faces challenges in model fitting and variable selection with big data arriving in streams. Chen et al. (2019, Annals of Statistics) has proposed a quantile regression method for streaming data, but a strong additional condition is required. In this paper, renewable optimized objective functions for regression parameter estimation and variable selection in a quantile regression are proposed. The proposed methods are illustrated using current data and the summary statistics of historical data. Theoretically, the proposed statistics are shown to have the same asymptotic distributions as the standard version computed on an entire data stream with the data batches pooled into one data set, without additional condition. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods. This research is supported by the National Social Science Foundation of China (Series number: 21BTJ040)

Published
2022

120. Role of low-density lipoprotein in mediating the effect of air pollution on coronary heart disease: a two-step multivariate Mendelian randomization study.

Author

SUN, C., JIANG, R., ZHANG, X.-Y., ZHA, L., LIU, D.-Y., and JIN, E.-Z.

Abstract

OBJECTIVE: Air pollution is affecting the health of millions of people all over the world. The causal correlations of PM2.5, PM10, and nitrogen dioxide (NOx), as the main fine particulate matter, and coronary heart disease (CHD) are yet to be explored. Low-density lipoprotein (LDL) has been a principal factor in the pathogenesis of CHD. It is an interesting issue to consider whether LDL mediates the effect of air pollutants in CHD pathogenesis. MATERIALS AND METHODS: A genome-wide association study (GWAS) on the European population, followed up from 2010 to 2018, involving over 400,000 participants, was based on a land-use regression model. The annual mean concentrations of major air pollutant particles, PM2.5 (n=423,796), PM10 (n=423,796), and NOx (n=456,380), were recorded. The large GWAS database of CHD covered over ten million SNPs with independent single nucleotide polymorphisms (SNPs). LDL database collected major biochemical blood parameters from over 400,000 patients (n=440,546). Taken together, we conducted independent two-sample Mendelian randomization (MR) analyses for the causality between air pollutants (PM2.5, PM10, and NOx) and CHD. Multivariate MR analysis was conducted using causal relationships to determine the direct effects of exposure on outcome. The fixed-effect inverse variance weighted (IVW2) method was mainly employed to assess this relationship, with a confi- dence interval of 95% for the odds ratio (OR). Also, MR-Egger, weighted median, maximum likelihood ratio method, and random-effects inverse variance-weighted (IVW1) method were adopted as supplementary methods. RESULTS: T wo-sample M R r esults b ased o n the IVW2 method suggested positive correlations between PM2.5 and CHD [OR 1.875 (1.279-2.748), p=0.001], PM10 and CHD [OR 2.586 (1.479-4.523), p=0.001], and NOx and CHD [OR 2.991 (2.021- 4.427), p=4.37E-08]. The direct effect and mediating proportion were calculated using multivariable Mendelian randomization (MVMR). Lastly, the mediating proportions of LDL in the regulatory roles of PM2.5, PM10, and NOx in CHD were 2.82%, 4.73%, and 9.54%, respectively. CONCLUSIONS: PM2.5, PM10, and NOx share direct causal associations with CHD, and LDL performs a mediating role in this pathogenic process. Early prevention against air pollution (such as increasing green areas and reducing large-scale industrial dust emissions) and early lipid-lowering treatment can effectively prevent the occurrence of CHD. [ABSTRACT FROM AUTHOR]

Published
2024

125. Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

Author

Liu, H, Dong, Z, Jiang, R, Deng, J, Chen, Q, Song, X, Liu, H, Dong, Z, Jiang, R, Deng, J, Chen, Q, and Song, X

Abstract

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

Published
2023

126. Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

Author

Deng, J, Jiang, R, Song, X, Deng, J, Jiang, R, and Song, X

Abstract

Tensor time series TTS data a generalization of one dimensional time series on a high dimensional space is ubiquitous in real world scenarios especially in monitoring systems involving multi source spatio temporal data e g transportation demands and air pollutants Compared to modeling time series or multivariate time series which has received much attention and achieved tremendous progress in recent years tensor time series has been paid less effort Properly coping with the tensor time series is a much more challenging task due to its high dimensional and complex inner structure In this paper we develop a novel TTS forecasting framework which seeks to individually model each heterogeneity component implied in the time the location and the source variables We name this framework as GMRL short for Gaussian Mixture Representation Learning Experiment results on two real world TTS datasets verify the superiority of our approach compared with the state of the art baselines Code and data are published on https github com beginner sketch GMRL 2023 International Joint Conferences on Artificial Intelligence All rights reserved

Published
2023

127. TTS-Norm: Forecasting Tensor Time Series via Multi-Way Normalization

Author

Deng, J, Yin, D, Jiang, R, Song, X, Deng, J, Yin, D, Jiang, R, and Song, X

Abstract

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world applications. Compared to modeling time series or multivariate time series, which has received much attention and achieved tremendous progress in recent years, tensor time series has been paid less effort. However, properly coping with the TTS is a much more challenging task, due to its high-dimensional and complex inner structure. In this article, we start by revealing the structure of TTS data from afn statistical view of point. Then, in line with this analysis, we perform Tensor Time Series forecasting via a proposed Multi-way Normalization (TTS-Norm), which effectively disentangles multiple heterogeneous low-dimensional substructures from the original high-dimensional structure. Finally, we design a novel objective function for TTS forecasting, accounting for the numerical heterogeneity among different low-dimensional subspaces of TTS. Extensive experiments on two real-world datasets verify the superior performance of our proposed model.1

Published
2023

128. HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation

Author

Li, Y, Fan, Z, Yin, D, Jiang, R, Deng, J, Song, X, Li, Y, Fan, Z, Yin, D, Jiang, R, Deng, J, and Song, X

Abstract

Friend recommendation from user trajectory is a vital real-world application of location-based social networks (LBSN) services. Previous statistical analysis indicated that social network relationships could explain 10% to 30% of human movement, especially long-distance travel. Therefore, it is necessary to recognize patterns from human mobility to assist the friend recommendation. However, previous works either modelled friendships and check-in records by simple graphs with only one connection between any two nodes or ignored a large amount of vital spatio-temporal information and semantic information in raw LBSN data. To overcome the limitation of the simple graph commonly seen in previous works, we leverage heterogeneous multigraph to model LBSN data and define various semantic connections between nodes. Against this background, we propose a Heterogeneous Multigraph Contrastive Learning (HMGCL) model to capture spatio-temporal characteristics of human trajectories for user node embedding learning. Extensive experiments show that our method outperforms the state-of-the-art approaches in six real-world city datasets.

Published
2023

129. Role of wound microbiome, strategies of microbiota delivery system and clinical management.

Author

Tang, Q, Xue, N, Ding, X, Tsai, KH-Y, Hew, JJ, Jiang, R, Huang, R, Cheng, X, Yee Cheng, Y, Chen, J, Wang, Y, Tang, Q, Xue, N, Ding, X, Tsai, KH-Y, Hew, JJ, Jiang, R, Huang, R, Cheng, X, Yee Cheng, Y, Chen, J, and Wang, Y

Abstract

Delayed wound healing is one of the most global public health threats affecting nearly 100 million people each year, particularly the chronic wounds. Many confounding factors such as aging, diabetic disease, medication, peripheral neuropathy, immunocompromises or arterial and venous insufficiency hyperglycaemia are considered to inhibit wound healing. Therapeutic approaches for slow wound healing include anti-infection, debridement and the use of various wound dressings. However, the current clinical outcomes are still unsatisfied. In this review, we discuss the role of skin and wound commensal microbiota in the different healing stages, including inflammation, cell proliferation, re-epithelialization and remodelling phase, followed by multiple immune cell responses to commensal microbiota. Current clinical management in treating surgical wounds and chronic wounds was also reviewed together with potential controlled delivery systems which may be utilized in the future for the topical administration of probiotics and microbiomes. This review aims to introduce advances, novel strategies, and pioneer ideas in regulating the wound microbiome and the design of controlled delivery systems.

Published
2023

132. Gelanxinning capsule improves coronary microvascular dysfunction by inhibiting inflammation and restoring endothelial function.

Author

DONG, M.-Y., JIANG, H.-Y., WANG, Y.-Y., ZHAO, H., JIANG, R.-J., LIANG, C.-Y., ZHANG, J., JIA, S., HSU, B.-L., FANG, W., and CHENG, G.

Abstract

OBJECTIVE: Gelanxinning capsule (GXSC) is a Chinese medicine to cure coronary artery disease (CAD) and a compound of Pueraria lobata, hawthorn extract, and gypenosides. However, whether GXSC could improve coronary microvascular dysfunction (CMD) is unknown. We aimed to demonstrate the therapeutic effect of GXSC on CMD and its underlying mechanisms in CAD patients. PATIENTS AND METHODS: This was a single-center, randomized control trial. A total of 78 patients diagnosed by selective coronary angiography (CAG) participated in this study. Patients' demographics, medical history, medications, and results of laboratory testing were collected. The index of microcirculatory resistance (IMR) and coronary flow reserve (CFR) were obtained by CAG and single-photon emission computed tomography (SPECT) separately. Fasting blood samples were obtained on the morning following the admission day. Concentrations of several molecules of inflammation, endothelial function, and coronary microvascular function were measured by ELISA. Patients were followed-up two months after discharge and fasting blood samples were also acquired. RESULTS: All patients were randomly divided into 2 groups: GXSC, 38 (48.7%), and control, 40 (51.3%). The intergroup comparison revealed no significant differences with respect to all baseline variables. As for inflammation biomarkers, proinflammatory NOD-like receptor thermal protein domain associated protein 3 (NLRP3) and interleukin (IL)-1 were significantly decreased in GXSC compared with the control group (0.71±0.08 vs. 1.04±0.07, p<0.01 and 7.16±0.59 vs. 10.93±1.04, p<0.01). Anti-inflammatory adropin was increased in the GXSC group (7.75±0.59 vs. 5.71±0.68, p=0.03). As for indexes of endothelial function, the concentrations of syndecan (SDC) 1, SDC4 and heparan sulphates (HS) were significantly downregulated in 2 months GXSC treatment (3.31±0.28 vs. 4.85±0.43, p<0.01, 3.79±0.56 vs. 5.69±0.68, p=0.03 and 21.31±2.79 vs. 35.18±4.11 p<0.01). In addition, the level of SIRTUIN 1 (SIRT1), which is a vascular protective protein, was upregulated in GXSC group (5.63±0.30 vs. 4.22±0.37, p<0.01). As for molecules of coronary microvascular function, endocan, soluble urokinase plasminogen activator receptor (suPAR), and growth differentiation factor (GDF)-15 were significantly decreased consistently in GXSC compared with the control group (0.09±0.01 vs. 0.19±0.03, p<0.01, 4.44±0.40 vs. 5.73±0.40, p=0.03 and 2.08±0.17 vs. 2.69±0.18, p=0.02). CONCLUSIONS: In conclusion, GXSC could improve CMD by inhibiting inflammation and restoring endothelial function. GXSC might be an effective drug in CAD patients without obstructive epicardial coronary arteries but suffering from angina. [ABSTRACT FROM AUTHOR]

Published
2023

133. lncRNA H19 facilitates vascular neointima formation by targeting miR-125a-3p/FLT1 axis

Author

Jiang Rengui, He Xuyu, Chen Weidong, Cai Huoying, Su Zhaohai, Xie Zheng, Zhang Bilong, Yang Jiangyong, Wang Yueting, Huang Ling, Cao Gang, Zhong Xiutong, Xie Hui, Zhu Hengqing, Cao Jun, and Lu Weiling

Subjects
vascular smooth muscle cell, neointima formation, lncRNA H19, miR-125a-3p, FLT1, Biochemistry, QD415-436, Genetics, QH426-470
Abstract

The aberrant proliferation and migration of vascular smooth muscle cells (VSMCs) contribute to the development of neointima formation in vascular restenosis. This study aims to explore the function of the long noncoding RNA H19 in neointima formation. A mouse carotid ligation model was established, and human vascular smooth muscle cells (VSMCs) were used as a cell model. lncRNA H19 overexpression promoted VSMC proliferation and migration. Moreover, miR-125a-3p potentially bound to lncRNA H19, and Fms-like tyrosine kinase-1 (FLT1) might be a direct target of miR-125a-3p in VSMCs. Upregulation of miR-125a-3p alleviated lncRNA H19-enhanced VSMC proliferation and migration. Furthermore, rescue experiments showed that enhanced expression of miR-125a-3p attenuated lncRNA H19-induced FLT1 expression in VSMCs. In addition, the overexpression of lncRNA H19 significantly exacerbated neointima formation in a mouse carotid ligation model. In summary, lncRNA H19 stimulates VSMC proliferation and migration by acting as a competing endogenous RNA (ceRNA) of miR-125a-3p. lncRNA H19 may be a therapeutic target for restenosis.

Published
2024
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134. Ventilatory pressure parameters impact the association between acute gastrointestinal injury and all-cause mortality in mechanically ventilated patients

Author

Shao Ziqiang, Li Jiale, Sun Renhua, Wu Aiping, Ni Yin, Liu Jingquan, Guo Feng, Ying Lijun, Ge Guoping, Ding Aijun, Shi Yunchao, Liu Changwen, Xu Lei, Jiang Ronglin, Lu Jun, Lin Ronghai, Zhu Yannan, Wu Weidong, Xie Bo, and Hu Bangchuan

Subjects
Mechanical ventilation, Acute gastrointestinal injury, All-cause mortality, Plateau pressure, Driving pressure, Positive end-expiratory pressure, Medicine, Science
Abstract

Abstract Acute gastrointestinal injury (AGI) is common in mechanically ventilated (MV) patients, but the potential association between ventilatory pressure parameters and AGI grade and their impact on mortality remains unclear. This study aimed to explore the association between ventilatory pressure parameters and AGI grade, and their interaction on all-cause mortality in MV patients. This study was a secondary analysis of a multicenter, prospective, observational study that enrolled adult patients with an expected duration of mechanical ventilation ≥ 48 h from 14 general intensive care units in Zhejiang Province between March and August 2014. The AGI grade was assessed daily on the basis of gastrointestinal symptoms, intra-abdominal pressures, and feeding intolerance in the first week of admission to the ICU. This study included 331 patients (69.2% men; mean age, 64.6 ± 18.9 years). Multivariate regression analysis showed that plateau pressure (Pplat) (OR 1.044, 95% CI 1.009–1.081, P = 0.013), serum creatinine (OR 1.003, 95% CI 1.001–1.006, P = 0.042) and APACHE II score (OR 1.035, 95% CI 1.021–1.072, P = 0.045) were independently associated with global AGI grade III/IV within 7 days of ICU admission. Moreover, global AGI grade (HR 2.228, 95% CI 1.561–3.182, P

Published
2024
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147. Progress on improving agricultural nitrogen use efficiency: UK-China virtual joint centers on nitrogen agronomy

Author

Misselbrook, T. H., Bai, Z., Cai, Z., Cao, W., Carswell, A. M., Cowan, N., Cui, Z., Chadwick, D. R., Emmett, B., Goulding, K. W. T., Jiang, R., Jones, D. L., Ju, X., Liu, H., Lu, Y., Ma, L., Powlson, D. S., Rees, R. M., Skiba, U., Smith, P., Sylvester-Bradley, R., Williams, J., Wu, L., Xu, M., Xu, W., Zhang, F., Zhang, J., Zhou, J., and Liu, X.

Subjects
Nitrogen use efficiency, General Veterinary, Agriculture and Soil Science, CINAg, General Agricultural and Biological Sciences, N-CIRCLE, Sustainable production, Biotechnology, Reactive nitrogen
Abstract

Two virtual joint centers for nitrogen agronomy were established between the UK and China to facilitate collaborative research aimed at improving nitrogen use efficiency (NUE) in agricultural production systems and reducing losses of reactive N to the environment. Major focus areas were improving fertilizer NUE, use of livestock manures, soil health, and policy development and knowledge exchange. Improvements to fertilizer NUE included attention to application rate in the context of yield potential and economic considerations and the potential of improved practices including enhanced efficiency fertilizers, plastic film mulching and cropping design. Improved utilization of livestock manures requires knowledge of the available nutrient content, appropriate manure processing technologies and integrated nutrient management practices. Soil carbon, acidification and biodiversity were considered as important aspects of soil health. Both centers identified a range of potential actions that could be taken to improve N management, and the research conducted has highlighted the importance of developing a systems-level approach to assessing improvement in the overall efficiency of N management and avoiding unintended secondary effects from individual interventions. Within this context, the management of fertilizer emissions and livestock manure at the farm and regional scales appear to be particularly important targets for mitigation.

Published
2022

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