| 1. |
- Fan, Qunping, 1989, et al.
(författare)
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A Non-Conjugated Polymer Acceptor for Efficient and Thermally Stable All-Polymer Solar Cells
- 2020
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Ingår i: Angewandte Chemie - International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 59:45, s. 19835-19840
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Tidskriftsartikel (refereegranskat)abstract
- A non-conjugated polymer acceptor PF1-TS4 was firstly synthesized by embedding a thioalkyl segment in the mainchain, which shows excellent photophysical properties on par with a fully conjugated polymer, with a low optical band gap of 1.58 eV and a high absorption coefficient >105 cm−1, a high LUMO level of −3.89 eV, and suitable crystallinity. Matched with the polymer donor PM6, the PF1-TS4-based all-PSC achieved a power conversion efficiency (PCE) of 8.63 %, which is ≈45 % higher than that of a device based on the small molecule acceptor counterpart IDIC16. Moreover, the PF1-TS4-based all-PSC has good thermal stability with ≈70 % of its initial PCE retained after being stored at 85 °C for 180 h, while the IDIC16-based device only retained ≈50 % of its initial PCE when stored at 85 °C for only 18 h. Our work provides a new strategy to develop efficient polymer acceptor materials by linkage of conjugated units with non-conjugated thioalkyl segments.
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| 2. |
- Ghalwash, Mohamed, et al.
(författare)
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Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood : a prospective cohort study
- 2023
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Ingår i: The Lancet Child and Adolescent Health. - 2352-4642. ; 7:4, s. 261-268
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Tidskriftsartikel (refereegranskat)abstract
- Background: Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10−18 years with an increased risk of developing type 1 diabetes. Methods: Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. Findings: Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5–20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34–0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50–0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1–1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6–9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86–95) sensitive, with a positive predictive value of 66% (60–72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89–97) but lowered the positive predictive value to 55% (49–60). Interpretation: Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. Funding: JDRF International.
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| 3. |
- Liu, Zhanju, et al.
(författare)
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Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries
- 2023
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Ingår i: Nature Genetics. - : Nature Portfolio. - 1061-4036 .- 1546-1718. ; 55:5, s. 796-806
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Tidskriftsartikel (refereegranskat)abstract
- Inflammatory bowel diseases (IBDs) are chronic disorders of the gastrointestinal tract with the following two subtypes: Crohn's disease (CD) and ulcerative colitis (UC). To date, most IBD genetic associations were derived from individuals of European (EUR) ancestries. Here we report the largest IBD study of individuals of East Asian (EAS) ancestries, including 14,393 cases and 15,456 controls. We found 80 IBD loci in EAS alone and 320 when meta-analyzed with similar to 370,000 EUR individuals (similar to 30,000 cases), among which 81 are new. EAS-enriched coding variants implicate many new IBD genes, including ADAP1 and GIT2. Although IBD genetic effects are generally consistent across ancestries, genetics underlying CD appears more ancestry dependent than UC, driven by allele frequency (NOD2) and effect (TNFSF15). We extended the IBD polygenic risk score (PRS) by incorporating both ancestries, greatly improving its accuracy and highlighting the importance of diversity for the equitable deployment of PRS. Genome-wide association analyses across individuals of East Asian and European ancestries identify new risk loci for inflammatory bowel diseases. A polygenic risk score derived from the combined datasets shows improved prediction accuracy.
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| 4. |
- Meng, Yuan, et al.
(författare)
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Influence of land use type and urbanization level on the distribution of pharmaceuticals and personal care products and risk assessment in Beiyun River, China
- 2021
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Ingår i: Chemosphere. - : Pergamon Press. - 0045-6535 .- 1879-1298. ; 287:Pt 1
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Tidskriftsartikel (refereegranskat)abstract
- Influence of land use type and urbanization level on the distribution of pharmaceuticals and personal care products (PPCPs) from the developed regions of Beijing-Tianjin-Hebei in the northern China was evaluated. The seasonal and spatial variations of the 22 target PPCPs were analyzed in the 63 sampling sites along the whole Beiyun River Basin. Results showed that the total PPCPs concentration had a wide variation range, from 132 ng L-1 to 25474 ng L-1. Spatial interpolation analysis showed that agricultural land presented higher PPCPs contamination level than build-up land (p < 0.05) and the concentration was negatively correlated with urbanization level. Source apportionment showed the untreated sewage source contributed to 34%-53% of the PPCPs burden in the Beiyun River. Risk assessment indicated that diethyltoluamide, carbamazepine, octocrylene, gemfibrozil and triclocarban had high risks (RQ > 1), and small tributaries had the highest mixed risk (MRQ = 34). Species sensitivity distribution combined with the safety threshold method showed that PPCPs would have potential risk on aquatic organisms even at very low concentrations and triclocarban posed the highest risk in the Beiyun River.
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| 5. |
- Muhammed, Alemu Jorgi, et al.
(författare)
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Resource Allocation for Energy-Efficient NOMA System in Coordinated Multi-Point Networks
- 2021
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Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 70:2, s. 1577-1591
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies user scheduling and power allocation problem to maximize the energy efficiency (EE) for non-orthogonal multiple access (NOMA) in downlink Coordinated Multi-Point networks. In the proposed framework, a more practical scenario the imperfect channel state information, imperfect successive interference cancellation and data outage are investigated. To address the considered problem, the optimization problem is formulated constrained by the total power and the outage probability requirements. However, the EE objective function is with a non-convex structure. Accordingly, we first convert the optimization problem to make the objective function concave and analytically tractable. Furthermore, we split the joint optimization problem to find a suboptimal solutions to the original problem. As a result, we first propose a suboptimal user-scheduling algorithm to improve the system's EE. Due to the non-convex function of the transmit power, we invoke a sequential successive convex approach to address the non-convex problem by its lower bound concave function. Besides, the fractional objective function is converted to its equivalent subtractive form. Finally, we derive a power control scheme to address the proposed framework. Simulation results endorse the effectiveness of the proposed algorithm and their performance gains in terms of EE compared to both NOMA and OFDMA variants.
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| 6. |
- Qu, Zhiguo, et al.
(författare)
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DTQFL : A Digital Twin-Assisted Quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network
- 2023
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Ingår i: IEEE journal of biomedical and health informatics. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultane-ously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized train-ing, the VQNN can achieve higher accuracy than that with-out personalized training.
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| 7. |
- Qu, Zhiguo, et al.
(författare)
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IoMT-based smart healthcare detection system driven by quantum blockchain and quantum neural network
- 2024
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Ingår i: IEEE journal of biomedical and health informatics. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 28:6, s. 3317-3328
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Tidskriftsartikel (refereegranskat)abstract
- Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things (IoMT). The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system named QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network nameded HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack. © IEEE
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| 8. |
- Qu, Zhiguo, et al.
(författare)
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QB-IMD : A secure medical data processing system with privacy protection based on quantum blockchain for IoMT
- 2024
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Ingår i: IEEE Internet of Things Journal. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:1, s. 40-49
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Tidskriftsartikel (refereegranskat)abstract
- Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). Blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. Blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This paper innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy. © IEEE
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| 9. |
- Qu, Zhiguo, et al.
(författare)
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Quantum detectable Byzantine agreement for distributed data trust management in blockchain
- 2023
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Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 637
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Tidskriftsartikel (refereegranskat)abstract
- No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain. © 2023 The Authors
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| 10. |
- Wu, Chuanyan, et al.
(författare)
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PEPRF : Identification of Essential Proteins by Integrating Topological Features of PPI Network and Sequence-based Features via Random Forest
- 2021
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Ingår i: Current Bioinformatics. - : Bentham Science Publishers Ltd.. - 1574-8936. ; 16:9, s. 1161-1168
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Tidskriftsartikel (refereegranskat)abstract
- Background: Essential proteins play an important role in the process of life, which can be identified by experimental methods and computational approaches. Experimental approaches to identify essential proteins are of high accuracy but with the limitation of time and resource-consuming. Objective: Herein, we present a computational model (PEPRF) to identify essential proteins based on machine learning. Methods: Different features of proteins were extracted. Topological features of Protein-Protein Interaction (PPI) network-based are extracted. Based on the protein sequence, graph theory-based features, in-formation-based features, composition and physichemical features, etc., were extracted. Finally, 282 features are constructed. In order to select the features that contributed most to the identification, Re-liefF-based feature selection method was adopted to measure the weights of these features. Results: As a result, 212 features were curated to train random forest classifiers. Finally, PEPRF get the AUC of 0.71 and an accuracy of 0.742. Conclusion: Our results show that PEPRF may be applied as an efficient tool to identify essential pro-teins.
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