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- Zhou, J. M., et al.
(författare)
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Digoxin is associated with worse outcomes in patients with heart failure with reduced ejection fraction
- 2020
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Ingår i: Esc Heart Failure. - : Wiley. - 2055-5822. ; 7:1, s. 139-147
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Tidskriftsartikel (refereegranskat)abstract
- Aims The aim of this study was to investigate the impact of digoxin use on the outcomes of patients with heart failure with reduced ejection fraction (HFrEF) and its possible interaction with atrial fibrillation or use of currently guideline-recommended treatments in the real world in China. Methods and results Patients hospitalized with HFrEF from 45 hospitals participating in the China National Heart Failure Registration Study (CN-HF) were enrolled to assess the all-cause mortality, HF mortality, all-cause re-hospitalization, and HF re-hospitalization associated with digoxin use. Eight hundred eighty-two eligible HFrEF patients in the CN-HF registry were included: 372 patients with digoxin and 510 patients without digoxin. Among them, 794 (90.0%) patients were followed up for the endpoint events, with a median follow-up of 28.6 months. Kaplan-Meier survival analysis showed that the all-cause mortality (P < 0.001) and all-cause re-hospitalization (P = 0.020) were significantly higher in digoxin group than non-digoxin group, while HF mortality (P = 0.232) and HF re-hospitalization (P = 0.098) were similar between the two groups. The adjusted Cox proportional-hazards regression analysis demonstrated that digoxin use remained as an independent risk factor for increased all-cause mortality [hazard ratio (HR) 1.76; 95% confidence interval (CI) 1.27-2.44; P = 0.001] and all-cause re-hospitalization (HR 1.27; 95% CI 1.03-1.57; P = 0.029) in HFrEF patients and the predictive value of digoxin for all-cause mortality irrespective of rhythm or in combination with other guideline-recommended therapies. Conclusions Digoxin use is independently associated with increased risk of all-cause mortality and all-cause re-hospitalization in HFrEF patients.
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| 2. |
- Mullins, N., et al.
(författare)
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Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology
- 2021
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 53, s. 817-829
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies. Genome-wide association analyses of 41,917 bipolar disorder cases and 371,549 controls of European ancestry provide new insights into the etiology of this disorder and identify novel therapeutic leads and potential opportunities for drug repurposing.
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| 6. |
- Fan, B.-B., et al.
(författare)
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Dry/wet cycling reduces spore germination and viability in six peatland bryophytes
- 2023
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Ingår i: Plant Biology. - : German Society for Plant Sciences; Royal Botanical Society of the Netherlands. - 1435-8603 .- 1438-8677. ; 25:3, s. 440-447
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Tidskriftsartikel (refereegranskat)abstract
- Dry/wet cycling driven by water level fluctuation in wetlands may strongly influence the destiny of seeds. However, how dry/wet cycling affects spore survival and germinability in peatland bryophytes is poorly understood.Six peatland bryophytes, three hummock- and three hollow-dwelling Sphagnum species, were chosen as study species. We tested the effects of dry (60% air RH)/wet (waterlogging) cycle frequency (once per 12, 8 or 4 days for low, medium or high, respectively) and ratio (3:1, 1:1 or 1:3 dry:wet time per cycle) on spore germinability, viability, dormancy percentage and protonema development.Dry/wet cycling significantly reduced spore germination percentage and viability and slowed protonema development in all Sphagnum species, being more pronounced with higher dry/wet cycling frequencies. The hummock species S. capillifolium and S. fuscum had higher spore germination percentage after the continuous dry treatment, while the hollow species S. angustifolium, S. squarrosum and S. subsecundum showed the opposite response, compared to the continuously wet treatment. Except for S. squarrosum, spore viability was higher after the dry than after the wet treatment. Spore viability and dormancy percentage were higher after a dry/wet ratio of 1:3 than after ratios of 3:1 and 1:1.Our study shows that both germinability and viability of bryophyte spores are reduced by dry/wet cycling (especially when frequent) in peatlands. This emphasizes the need to ensure constant water levels and low frequencies of water level fluctuation, which are relevant in connection with wetland restoration, to promote Sphagnum spore survival and establishment in peatlands after disturbances.
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| 7. |
- Jia, S., et al.
(författare)
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PS-64QAM-OFDM THz photonic-wireless transmission with 2×300 Gbit/s line rate
- 2020
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Ingår i: Optics InfoBase Conference Papers. - : The Optical Society.
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Konferensbidrag (refereegranskat)abstract
- THz photonic-wireless transmission of a record 612.65 Gbit/s line rate is successfully demonstrated in the 320-380-GHz band by employing THz orthogonal polarization dual-antenna, PS-64QAM-OFDM modulation and advanced nonlinear-digital reception techniques.
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| 8. |
- Lv, Z., et al.
(författare)
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Blockchain-Based Communication for Digital Twins
- 2022
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Ingår i: Advances in Blockchain Technology for Cyber Physical Systems. - Cham : Springer International Publishing. - 2199-1073. ; , s. 193-210
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Bokkapitel (refereegranskat)abstract
- Blockchain technology can safely and reliably track the creation process of digital twins. To ensure the data security in the case of untrusted multiple parties sharing data and to improve users’ satisfaction with the use of digital assets in daily transactions, in this article, the digital twin technology is combined with the blockchain technology, and for multiple scenarios in blockchain networks, a credible and efficient edge computing resource allocation method is proposed based on deep reinforcement learning (DRL) theory. The log storage system of the blockchain is mainly used as the interface for writing and reading data operation logs, and a hybrid storage strategy is put forward for the log storage system. As for the trusted resource allocation under the decentralized model of the blockchain network, to prevent the system from directly offloading the computing tasks submitted by the user each time to the edge server, first, the task is submitted to the system, and then a more reasonable resource allocation strategy is implemented with the user satisfaction as the standard. The simulation experiment results show that the improved environment after the introduction of the log storage system reduces the storage overhead by about 75% compared with the blockchain benchmark environment. When the number of servers is greater than 5, compared with other resource allocation algorithms, user satisfaction of the resource allocation algorithm based on DRL theory has been significantly improved, and compared with Q-learning algorithm, its user satisfaction has increased by 15%. In conclusion, the digital twins use credible source data as input. In this process, the blockchain ensures the security of data management, and finally, the data analysis is performed to predict events and evaluate related factors. The resource allocation method based on DRL realizes credible resource utilization based on recorded data on the blockchain.
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| 9. |
- Pang, S., et al.
(författare)
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DCTGM : A Novel Dual-channel Transformer Graph Model for miRNA-disease Association Prediction
- 2022
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964.
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Tidskriftsartikel (refereegranskat)abstract
- Studies have shown that as non-coding RNAs, miRNAs regulate all levels of life activities and most pathological processes. Therefore, identifying disease-related miRNAs is essential for disease diagnosis and treatment. However, traditional biological experiments are highly uncertain and time-consuming. Hence, advanced intelligent computational models are needed to address this problem. We propose a dual-channel transformer graph model, named DCTGM, to learn multi-scale representations for miRNA-disease association prediction. Specifically, DCTGM includes a transformer encoder (TE) and GraphSAGE encoder (GE). The TE intensely captures the important interaction information between miRNA-disease pairs, and the GE aggregates multi-hop neighbor information of miRNA-disease association heterograph to enrich node features. Then, an attention module is proposed to aggregate the dual-channel interactive representations, and we adopt a multi-layer perceptron (MLP) to predict the miRNA-disease association scores. The fivefold cross-validation experimental results demonstrate that our proposed DCTGM achieves the AP of 92.735%, F1 of 84.430%, accuracy of 85.255%, and ROC of 93.012%. In addition, we conduct case studies on brain neoplasms, kidney neoplasms, and breast neoplasms. The extensive experiments show that the dbDEMC database validates 100% of the top 20 predicted miRNAs associated with these diseases. This model can effectively predict the potential mirNA-disease association. Experiments have shown that miRNA associated with a new disease can also be predicted. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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| 10. |
- Qiao, L., et al.
(författare)
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A blockchain-based decentralized collaborative learning model for reliable energy digital twins
- 2023
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Ingår i: Internet of Things and Cyber-Physical Systems. - : KeAi Communications Co.. - 2667-3452. ; 3, s. 45-51
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a blockchain-based decentralized collaborative learning method for the Industrial Internet environment to solve the trust and security issues in Federated Learning. Deploy a decentralized network for collaborative learning based on the alliance chain, design a block data structure suitable for asynchronous learning, and model three stages of computing event triggering, computing task distribution, and computing result integration for cross-domain device collaborative learning. List the critical steps for network deployment, including inspection, tearing down old networks, creating organizational encryption material, creating channels, and deploying chaincode. It also introduces the development of crucial chaincode such as initialization, creation, query, and modification. Finally, the correlation between the number of data pieces of the network, the number of communications, and the time of communications are analyzed through experiments. This paper also proposes a decentralized asynchronous collaborative learning algorithm, develops chaincode middleware between the blockchain network and Artificial Intelligence training, and conducts experimental analysis on the industrial steam volume prediction data set in thermal power generation. The performance on the data set, and the experimental results prove that the asynchronous collaborative learning algorithm proposed in this paper can achieve a good convergence effect. It is also compared with the single-machine single-card regression prediction algorithm, proving that the proposed model has better generalization. © 2023 The Authors
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