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A generalised catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2
Background Monitoring trends in multiple infections with SARS-CoV-2, following several pandemic waves, provides insight into the biological characteristics of new variants, but also necessitates methods to understand the risk of multiple reinfections. Objectives We generalised a catalytic model designed to detect increases in the risk of SARS-CoV-2 reinfection, to assess the population-level risk of multiple reinfections. Methods The catalytic model assumes the risk of reinfection is proportional to observed infections and uses a Bayesian approach to fit model parameters to the number of nth infections among individuals that occur at least 90 days after a previous infection. Using a posterior draw from the fitted model parameters, a 95% projection interval of daily nth infections is calculated under the assumption of a constant nth infection hazard coefficient. An additional model parameter was incorporated for the increased reinfection risk detected during the Omicron wave. The generalised model’s performance was then assessed using simulation-based validation. Key findings No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. Using simulation-based validation, we show that the model can successfully detect increases in the risk of third infections under different scenarios. Limitations Even though the generalised model is intended to detect the risk of nth infections, it is validated specifically for third infections, with its applicability for four or more infections being unconfirmed. Furthermore, the method’s sensitivity to low counts of nth infections, limits application in settings with small epidemics, limited testing coverage or early in an outbreak. Conclusions The catalytic model was successfully adapted to detect increases in the risk of nth infections, enhancing our capacity to identify future changes in the risk of nth infections by SARS-CoV-2 or other similar pathogens.
Analysis of user behavior and satisfaction under the elderly adaptation mode of an APP based on the fuzzy-IPA model
Age-related morphological changes of the pubic symphyseal surface: using three-dimensional statistical shape modeling
AbstractComputational analysis of the pubic symphyseal surface is widely used for accurate age estimation, offering quantitative precision through the detection of subtle morphological changes. However, these methods often lack insights into the underlying morphological changes across different age groups. To bridge this gap, the study utilizes statistical shape modeling (SSM), a versatile tool capable of describing diverse morphological variations within populations. This study aimed to elucidate the direction and extent of these morphological changes, identify the contributing factors, and pinpoint key variations crucial for distinguishing between age groups. Computed tomography (CT) scans of 252 subjects from the National Forensic Service of South Korea were employed, subjected to preprocessing and landmark-based alignment. Through this approach, the study visualized and validated significant age-related morphological changes and highlighted the shape variations essential for differentiating between consecutive age groups. This study holds significance in elucidating the intricate nature of age-related morphological changes in the pubic symphyseal surface. Thus, these findings can serve as valuable cornerstones for enhancing age-at-death estimation techniques in forensic anthropology.
Epidemiological characteristics of imported malaria related to international travel in the Republic of Korea from 2009 to 2018
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
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
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.