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Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Remote sensing is a powerful technique for classifying and quantifying objects. However, the elaborate classification of objects in coastal waters with complex structures is still challenging due to the high possibility of class mixing. The classification through the hyperspectral images can be a reasonable alternative to problems related to such precise classification work because it has high spectral resolution over a wide bandwidth. This study introduced the results of the case study using a novel method to classify green algae on an artificial structure based on hyperspectral data and deep-learning models. The spectral characteristics of the attached green algae on the artificial structure were observed using a ground-based hyperspectral camera. The observed image had a total of three classes (concrete, dense green algae, and sparse green algae). A certain area of the image was used as learning data to create classification models for three classes. The classification models were created from one machine-learning (support vector machine, SVM) and two deep-learning models (convolutional neural network, CNN; and dense convolutional network, DenseNet). As a result, the performance for the classification results of green algae predicted from two deep-learning models was higher than that of the machine-learning model. Additionally, the deep-learning model successfully classified the interface area between concrete and green algae. This study suggests that the combination of hyperspectral data and deep learning could enable more precise classification of objects in coastal areas.

In this study, we designed FogFusionNet, a multimodal sea fog prediction model, that used closed-circuit television (CCTV) images and multivariate time series observation (MTSO) data to predict three visibility classes—Normal visibility, Low visibility, and Sea fog—at 1-h intervals from the current time to 6-h in the future for a specific region. We applied weighted sampling and weighted loss to overcome the imbalance of each visibility class, and additionally evaluated the effect of replacing missing MTSO data. A total of 4 years of data regarding Incheon Port, which faces the Yellow Sea and is prone to sea fog, were collected for training and verifying FogFusionNet. Of these, 3 years of data was used for training FogFusionNet, and the remaining 1 year of data were used for verifying the performance of FogFusionNet. The prediction performance of FogFusionNet at 1-h intervals was 86.2% (0-h), 79.1% (1-h), 73.4% (2-h), 70.7% (3-h), 64.7% (4-h), 59.6% (5-h), and 49.3% (6-h), showing an average prediction performance of 69.0%. FogFusioneNet is expected to promote coastal safety and reduce economic losses due to coastal sea fog.

This study investigates the atmospheric interactions between two closely located typhoons in 2019. Typhoons in the Western Pacific significantly impact Eastern and Southeastern Asian countries, leading to various damages. As global warming is expected to increase typhoon intensity, accurate track forecasting becomes crucial for coastal disaster management. Despite the existing knowledge about the influence of typhoon activities on the atmospheric background, limited research addresses the atmospheric response between two typhoons. The study focuses on the cases of LEKIMA and KROSA, occurring simultaneously in 2019, and utilizes the Weather Research and Forecasting (WRF) model for simulations. The experimental setup involves comparing two scenarios: one with both typhoons and one with LEKIMA removed. Results reveal LEKIMA-induced distinctive atmospheric responses, including the closure of the western North Pacific subtropical high (WNPSH) boundary and the formulation of a wave train, influencing KROSA’s stagnation. The absence of LEKIMA allows KROSA to move more freely along the steering flow. Furthermore, the study highlights the potential of atmospheric models for understanding typhoon effects at regional to mesoscale levels. A comprehensive analysis of similar cases could enhance typhoon predictions, contributing to better damage mitigation strategies.

Abstract The significance of long-term teleconnections derived from the anomalous climatic conditions of El Niño has been a highly debated topic, where the remote response of coastal hydrodynamics and marine ecosystems to El Niño conditions is not completely understood. The 14-year long data from a ship-borne acoustic Doppler current profiler was used to examine the El Niño’s impact, in particular, 2009 and 2015 El Niño events, on oceanic and biological processes in coastal regions across the Korea/Tsushima Strait. Here, it was revealed that the summer volume transport could be decreased by 8.7% (from 2.46 ± 0.39 to 2.24 ± 0.26 Sv) due to the anomalous northerly winds in the developing year of El Niño. Furthermore, the fall mean volume backscattering strength could be decreased by 1.8% (from − 97.09 ± 2.14 to − 98.84 ± 2.10 dB) due to the decreased surface solar radiation after the El Niño events. Overall, 2009 and 2015 El Niño events remotely affected volume transport and zooplankton abundance across the Korea/Tsushima Strait through climatic teleconnections.

Summary: Background: Obesity is a heterogeneous condition, which includes a subset of individuals that can be classified as having metabolically healthy obesity (MHO), but there is no consensus on what constitutes MHO. Thus, the objective of the study is to examine the prevalence and predictors of prevalent MHO in adolescents using various definitions of MHO. Methods: Cross‐sectional data from the 1999–2010 National Health and Nutrition Examination Surveys were used. Participants included 316 male and 316 female adolescents aged 12–19 years with a BMI ≥ 95th percentile. Two definitions were used to define MHO. First, MHO was defined as having ≤1 metabolic syndrome criteria (excluding waist) and being free of type 2 diabetes, hypertension and dyslipidemia. Second, MHO was defined as being free of all metabolic syndrome criteria, insulin resistance and inflammation. Results: The prevalence of MHO was 42% (male) and 74% (female) using the first definition and 7% (male) and 12% (female) using the second more conservative definition. Lower abdominal obesity (waist circumference) and lower insulin resistance predicted prevalent MHO in male and female adolescents for both definitions (p < 0.01). Associations between dietary components and MHO were weak and inconsistent, while physical activity and inflammation were not associated with MHO in male and female adolescents for both definitions (p > 0.05). Conclusions: The prevalence of MHO in adolescents varied across definitions, with lower levels of abdominal obesity and insulin resistance as the most consistent predictors of prevalent MHO status. [ABSTRACT FROM AUTHOR]

Background Prolonged sitting is linked to various deleterious health outcomes. The alterability of the sitting time (ST)-health relationship is not fully established however and warrants study within populations susceptible to high ST. Methods We assessed the mortality rates of post-menopausal women from the Women's Health Initiative (WHI) observational study, a 15-year prospective study of post-menopausal women aged 50-79 years, according to their change in ST between baseline and year six. A total of 77 801 participants had information at both times on which to be cross-classified into the following: (i) high ST at baseline and follow-up; (ii) low ST at baseline and follow-up; (iii) increased ST and (iv) decreased ST. Cox regression was used to assess the relationship between all-cause, CVD and cancer mortality with change in ST. Results At the end of follow-up, there were 1855 deaths. Compared with high ST maintainers, low ST maintainers had a 51 and 48% lower risk of all-cause and cancer mortality, respectively. Reducing sitting also resulted in a protective rate of 29% for all-cause and 27% for cancer mortality. Conclusions These results highlight not only the benefit of maintaining minimal ST, but also the utility of decreasing ST in older women, if current levels are high. [ABSTRACT FROM AUTHOR]