| 11. |
- van de Vegte, Yordi, et al.
(author)
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Genetic insights into resting heart rate and its role in cardiovascular disease
- 2023
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Journal article (peer-reviewed)abstract
- The genetics and clinical consequences of resting heart rate (RHR) remain incompletely understood. Here, the authors discover new genetic variants associated with RHR and find that higher genetically predicted RHR decreases risk of atrial fibrillation and ischemic stroke. Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.
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| 12. |
- Wang, Daqin, et al.
(author)
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Inventory stacking with partial information
- 2023
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In: International Journal of Production Research. - : TAYLOR & FRANCIS LTD. - 0020-7543 .- 1366-588X.
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Journal article (peer-reviewed)abstract
- An inventory stacking decision assigns positions to items which are stacked vertically, such as containers in container terminals and steel plates in steel plants. The performance of stacking decisions is greatly affected by the arrival and departure information on items. We study an inventory stacking problem with partial information based on industrial observation in a steel plant. On the inbound side, we investigate three levels of information on future arriving items and their effect on performance. On the outbound side, we study the impact of the retrieval sequence, which is often random. We develop models incorporating different availabilities of information and determine stacking strategies. The study shows that the stacking strategy and stacking performance depend highly on information quality and space utilisation. Especially, when the space utilisation is high, low-quality information deteriorates the performance and such information should be ignored. This contradicts the general belief that more information should bring better performance. The study further proposes a time window allocation approach to reduce the uncertainty in retrieval, and it is effective in improving stacking performance.
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| 13. |
- Wang, Maoze, et al.
(author)
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Iontophoresis-Driven Microneedle Arrays Delivering Transgenic Outer Membrane Vesicles in Program that Stimulates Transcutaneous Vaccination for Cancer Immunotherapy
- 2023
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In: SMALL SCIENCE. - : Wiley-VCH Verlagsgesellschaft. - 2688-4046. ; 3
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Journal article (peer-reviewed)abstract
- Transdermal delivery of antigen and chemokine proteins that activates the maturation of skin dendritic cells (DCs) and direct the migration of activated DCs to lymph and spleen is an important alternative to conventional vaccines. However, stratum corneum forms a barrier to skin penetration. The poor cellular uptake of free antigens and chemokines also limits transcutaneous immunization efficacy. In this work, a pair of iontophoresis-driven microneedle patches is constructed, of which, two kinds of outer membrane vesicles (OMVs) derived from Escherichia coli transformed by plasmid encoding gp100 (IPMN-G) and chemokine ligand 21 (IPMN-C) are incorporated within microneedles, respectively. The topical application of IPMN-G and IPMN-C shows the effectiveness of transdermally delivering gp100 and CCL21 secreting vesicles to skin DCs. With iontophoresis as a driving generator, the release and uptake of transgenic OMVs in target cells are significantly enhanced, with transcutaneous immunization initiated. The in vivo applications of IPMN-G and IPMN-C with a 12 h interval retard the progression and prevent the occurrence of tumor spheroids. IPMN-GC is shown as a promising triplatform in engineering transgenic OMV-incorporated microneedles, driven by iontophoresis into a transcutaneous vaccine, providing a noninvasive system for the transdermal delivery of antigen and chemokine proteins for transcutaneous vaccination-meditated immunotherapy.
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| 14. |
- Yang, Jianfei, et al.
(author)
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EfficientFi : Towards Large-Scale Lightweight WiFi Sensing via CSI Compression
- 2022
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:15, s. 13086-13095
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Journal article (peer-reviewed)abstract
- WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device-free, cost-effective and privacy-preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this paper, we firstly analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely EfficientFi. The EfficientFi works with edge computing at WiFi APs and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi Channel State Information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized auto-encoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first IoT-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368Mb/s to 0.768Kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for human activity recognition.
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