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Extraction of compression indices from maternal-fetal heart rate simultaneous signals
Intrapartum asphyxia is responsible for approximately 900 000 deaths per year worldwide. These numbers show the urgency of investing in the quality of fetal health care. The heart rate signal is a complex signal and sometimes behaves unpredictably. Thus, it becomes relevant to study approaches that take into account their complexity, namely non-linear compression-based methods. In this work, feature extraction was based on two approaches: univariate and bivariate. The univariate approach is concerned with the extraction of fetal, maternal and maternal-fetal compression ratios and the bivariate approach aims to extract compression indices from maternal-fetal heart rate simultaneous signals and of each of the signals individually over time. To understand how the features calculated in this work can be useful in distinguishing acidemic and non-acidemic cases, a classifier was applied. Three different classifiers were tested, and the one that proved to be more effective was the Support-Vector Machine. Furthermore, it was also possible to conclude that the input set of variables that provides a better performance (f1-score = 0.793) of the classifier is composed of the variables of maternal-fetal compression ratio, maternal-fetal normalized relative compression and maternal-fetal normalized compression distance, obtained through trend and residual signal, which indicates that slow and fast fluctuations on the heart rate time series are important in acidemia assessment.
Model-free current control solution employing intelligent control for enhanced motor drive performance
Perspective from NHANES data: synergistic effects of visceral adiposity index and lipid accumulation products on diabetes risk
A pre-clinical MRI-guided all-in-one focused ultrasound system for murine brain studies
Orthologous marker groups reveal broad cell identity conservation across plant single-cell transcriptomes
A sedimentary ancient DNA perspective on human and carnivore persistence through the Late Pleistocene in El Mirón Cave, Spain
Abstract Caves are primary sites for studying human and animal subsistence patterns and genetic ancestry throughout the Palaeolithic. Iberia served as a critical human and animal refugium in Europe during the Last Glacial Maximum (LGM), 26.5 to 19 thousand years before the present (cal kya). Therefore, it is a key location for understanding human and animal population dynamics during this event. We recover and analyse sedimentary ancient DNA (sedaDNA) data from the lower archaeological stratigraphic sequence of El Mirón Cave (Cantabria, Spain), encompassing the (1) Late Mousterian period, associated with Neanderthals, and (2) the Gravettian (c. 31.5 cal kya), Solutrean (c. 24.5–22 cal kya), and Initial Magdalenian (d. 21–20.5 cal kya) periods, associated with anatomically modern humans. We identify 28 animal taxa including humans. Fifteen of these taxa had not been identified from the archaeozoological (i.e., faunal) record, including the presence of hyenas in the Magdalenian. Additionally, we provide phylogenetic analyses on 70 sedaDNA mtDNA genomes of fauna including the densest Iberian Pleistocene sampling of C. lupus. Finally, we recover three human mtDNA sequences from the Solutrean levels. These sequences, along with published data, suggest mtDNA haplogroup continuity in Iberia throughout the Solutrean/Last Glacial Maximum period.
Optimal vein access selection in adrenal vein sampling via upper extremity approach: a retrospective analysis of 325 cases
Prevalence of metal implants among US adults aged 40 years and older
AbstractMetal implants are commonly used in clinical practice. However, little is known regarding the prevalence of metal implants. Therefore, this study aimed to evaluate the prevalence of metal implants in the United States (US) among individuals aged ≥ 40 years. This study conducted a serial cross-sectional analysis of US adults aged ≥ 40 years who participated in the National Health and Nutrition Examination Survey (NHANES) (2015–2016 and 2017–March 2020). Self-reported questionnaires were used to assess whether the participants had metal implants inside their bodies. The primary outcome was the prevalence of metal implants among adults aged 40 years and older. Furthermore, weighted logistic regression analysis was employed to determine the changes in the prevalence of metal implants from 2015 to March 2020. Moreover, this study investigated the variation in metal implant prevalence by demographic factors based on the pooled NHANES cycles. All analyses were conducted based on 3,736 participants from the NHANES 2015–2016 and 6,387 participants from the NHANES 2017–March 2020. This study observed a high prevalence of metal implants among adults aged 40 and older (2015–2016: 27.23%; 2017–March 2020: 31.53%). Moreover, the results of the weighted logistic regression analysis showed that the prevalence of metal implants significantly increased from 2015 to March 2020, especially among older individuals, men, and White individuals. In addition, the results of the weighted logistic regression analysis indicated that the metal implant prevalence differed by age and race/ethnicity, in which older individuals and White individuals showed a significantly higher prevalence of metal implants than younger individuals and non-White individuals, respectively. There was a high prevalence of metal implants among US adults aged 40 and older, and the prevalence of metal implants significantly increased from 2015 to March 2020. Therefore, more attention needs to be paid to this special population, and it may be necessary to ensure accessibility and affordability and assess the potential long-term health impacts of metal implants, considering the increased prevalence of metal implants.
Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source
The giant genome of lily provides insights into the hybridization of cultivated lilies
The role of pangenomics in orphan crop improvement
Association of Mycobacterium tuberculosis aerosolization and HIV coinfection in the index case with T cell responses in household contacts
Effect of ambient O3 on respiratory mortality and synergies with meteorological factors in Shenyang, China
Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss
AbstractIn this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy of the solution, different multi-objective case studies are scrutinised based on the combinations of (i) HC and Loss, (ii) HC and VDev, (iii) Loss and VDev, (iv) HC Loss and VDev simultaneously with the effect of demand response. The multi-objective research problem is formulated as non-linear and non-convex programming approach. To solve this complex problem, the modified crow search optimization (MCSO) is proposed. The MCSO achieved the 0.0714 MW of network loss with the optimal integration of distributed generation and is comparable to the well-established optimization algorithms available in literature. From the simulation results, it is found that HC is 3322.31 kW, VDev is 0.4982 p.u and system losses is 1314.86 kWh with demand response program when all the objectives are simultaneously optimized. The simulation outcomes highlight the superiority of the MCSO over others. The application results show the benefits and the beauty of proposed research work.
ImageDoubler: image-based doublet identification in single-cell sequencing
Globally scalable glacier mapping by deep learning matches expert delineation accuracy
AbstractAccurate global glacier mapping is critical for understanding climate change impacts. Despite its importance, automated glacier mapping at a global scale remains largely unexplored. Here we address this gap and propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery. Assessing the spatial, temporal and cross-sensor generalisation shows that our best strategy achieves intersection over union >0.85 on previously unobserved images in most cases, which drops to >0.75 for debris-rich areas such as High-Mountain Asia and increases to >0.90 for regions dominated by clean ice. A comparative validation against human expert uncertainties in terms of area and distance deviations underscores GlaViTU performance, approaching or matching expert-level delineation. Adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available. The calibrated confidence for glacier extents is reported making the predictions more reliable and interpretable. We also release a benchmark dataset that covers 9% of glaciers worldwide. Our results support efforts towards automated multitemporal and global glacier mapping.