Turbulent heat and momentum transfer processes due to thermal convection cover many scales and are of great importance for several natural and technical flows. One consequence is that a fully resolved three-dimensiona...
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This paper describes an experience of the data exchange software platform practical use supporting the modern trends of Digital Economy. The platform was initially designed for the suppliers and customers of data sour...
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RR Lyrae (RRL) are old, low-mass radially pulsating variable stars in their core helium burning phase. They are popular stellar tracers and primary distance indicators, since they obey to well defined period-luminosit...
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Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vas...
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Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is ...
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Preeclampsia is a heterogeneous and multiorgan cardiovascular disorder of pregnancy. Here, we report the development of a novel strip-based lateral flow assay (LFA) using lanthanide-doped upconversion nanoparticles co...
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Preeclampsia is a heterogeneous and multiorgan cardiovascular disorder of pregnancy. Here, we report the development of a novel strip-based lateral flow assay (LFA) using lanthanide-doped upconversion nanoparticles conjugated to antibodies targeting two different biomarkers for detection of preeclampsia. We first measured circulating plasma FKBPL and CD44 protein concentrations from individuals with early-onset preeclampsia (EOPE), using ELISA. We confirmed that the CD44/FKBPL ratio is reduced in EOPE with a good diagnostic potential. Using our rapid LFA prototypes, we achieved an improved lower limit of detection: 10 pg ml −1 for FKBPL and 15 pg ml −1 for CD44, which is more than one order lower than the standard ELISA method. Using clinical samples, a cut-off value of 1.24 for CD44/FKBPL ratio provided positive predictive value of 100 % and the negative predictive value of 91 %. Our LFA shows promise as a rapid and highly sensitive point-of-care test for preeclampsia.
The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biom...
The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti
The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, ...
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The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, we report a universal sub-nanoreactor strategy for synthesis of yolk-shell MoS 2 supported single atom electrocatalysts with dual-anchored microenvironment of vacancy-enriched MoS 2 and intercalation carbon toward robust hydrogen-evolution reaction. Theoretical calculations reveal that the “E-Lock” and “E-Channel” are conducive to stabilize and activate metal single atoms. A group of SACs is subsequently produced with the assistance of sulfur vacancy and intercalation carbon in the yolk-shell sub-nanoreactor. The optimized C−Co−MoS 2 yields the lowest overpotential (η 10 =17 mV) compared with previously reported MoS 2 -based electrocatalysts to date, and also affords a 5–9 fold improvement in activity even comparing with those as-prepared single-anchored analogues. Theoretical results and in situ characterizations unveil its active center and durability. This work provides a universal pathway to design efficient catalysts for electro-refinery.
It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for att...
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International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
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