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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.

Solid organ transplant recipients (SOTR) are at increased risk from COVID-19. Over time, the absolute risk of adverse outcomes after COVID-19 has decreased in both the non-immunosuppressed/immunocompromised (non-ISC) general population, and amongst SOTR. Using the N3C, we examined the absolute risk of mortality, major adverse renal or cardiac events, and hospitalization after COVID-19 diagnosis amongst non-ISC and SOTR populations over five waves of the pandemic (Wave 1: Ancestral COVID; Wave 2: Alpha; Wave 3: Delta; Wave 4: Omicron; Wave 5: Omicron). Within each wave, we determined the relative risk of each outcome for SOTR versus the non-ISC population based on crude event rates, and then used multivariable cox proportional hazards models and logistic regression to determine the adjusted risk of each outcome based on SOT status. Throughout the pandemic, including during the Omicron wave (Wave 5), SOTR were at greater absolute risk for each outcome than non-ISC patients (p-values all

Drought, being one of the most devastating natural disasters, has a far-reaching impact. In the context of global warming, it becomes crucial to quantitatively and scientifically assess the effects of anthropogenic climate change on meteorological drought in China. This assessment not only enhances our understanding of anthropogenic climate change but also aids in formulating more effective strategies for managing the risks associated with meteorological drought. This study employs the standardized precipitation evapotranspiration index (SPEI) to compute drought events by utilizing both observational data and counterfactual climate data (i.e., detrended observations). Subsequently, it analyzes the impact of anthropogenic climate change on the characteristics of drought, including frequency, intensity, duration, and affected area, in China as well as nine major river basins from 1960 to 2019. The outcomes of the analysis reveal that based on observational data, there is a discernible upward trend in the frequency, intensity, duration, and affected area of meteorological droughts in China. Notably, the regions experiencing an increase in frequency, intensity, and duration are primarily situated in the northeastern part of the Northwestern Rivers basin, the central and western parts of the Yellow River basin, the central and northern parts of the Yangtze River basin, the western part of the Southeastern River basins, and the eastern part of the Pearl River basin. Conversely, when considering a counterfactual climate scenario, the frequency and intensity of meteorological droughts in China demonstrate an upward trend, while the duration and affected area exhibit a downward trend. The impact of anthropogenic climate change on China has been evident in the increased frequency, intensity, duration, and affected area of droughts. Specifically, regions located in the northeastern parts of Northwest River basins, the southern part of the Songliao River basin, the northern part of the Haihe River basin, the central-northern part of the Yangtze River basin, the eastern part of the Pearl River basin, and the western part of the Southwest River basins have experienced amplified levels of drought. Anthropogenic climate change is highlighted as the primary factor influencing the observed drought characteristics changes in China, with contribution rates of 84.67%, 75.25%, 190.32%, and 133.99% for changes in the increased drought frequency, intensity, duration, and affected area, respectively. These changes have significant implications for water resource management and agricultural practices in the affected regions.

IntroductionAlcohol intent (the susceptibility to initiating alcohol use) and alcohol sips (the initiation of alcohol) in youth are a multifactorial puzzle with many components. This research aims to examine the connection between genetic and environmental factors across sex, race and ethnicity.MethodsData was obtained from the twin hub of the Adolescent Brain Cognitive Development (ABCD) study at baseline (2016-2018). Variance component models were conducted to dissect the additive genetic (A), common (C) and unique environmental (E) effects on alcohol traits. The proportion of the total alcohol phenotypic variation attributable to additive genetic factors is reported as heritability (h2).ResultsThe sample (n = 1,772) included an approximately equal male-female distribution. The 886 same-sex twin pairs were 60.4% dizygotic (DZ), 39.6% monozygotic (MZ), 65.4% non-Hispanic Whites, 13.9% non-Hispanic Blacks, 10.8% of Hispanics with a mean age of 121.2 months. Overall, genetic predisposition was moderate for alcohol intent (h2 = 28%, p = .006) and low for alcohol initiation (h2 = 4%, p = 0.83). Hispanics (h2 = 53%, p < .0001) and Blacks (h2 = 48%, p < .0001) demonstrated higher alcohol intent due to additive genetic factors than Whites (h2 = 34%, p < .0001). Common environmental factors explained more variation in alcohol sips in females (c2 = 63%, p = .001) than in males (c2 = 55%, p = .003). Unique environmental factors largely attributed to alcohol intent, while common environmental factors explained the substantial variation in alcohol initiation.ConclusionSex and racial/ethnic disparities in genetic and environmental risk factors for susceptibility to alcohol initiation can lead to significant health disparities. Certain populations may be at greater risk for alcohol use due to their genetic and ecological factors at an early age.

Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method.

Addressing systematic measurement errors in self-reported data is a critical challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been utilized for error correction when an objectively measured biomarker is available; however, biomarkers for only a few dietary components have been developed. This paper proposes to use high-dimensional objective measurements to construct biomarkers for many more dietary components and to estimate the diet disease associations. It also discusses the challenges in variance estimation in high-dimensional regression methods and presents a variety of techniques to address this issue, including cross-validation, degrees-of-freedom corrected estimators, and refitted cross-validation (RCV). Extensive simulation is performed to study the finite sample performance of the proposed estimators. The proposed method is applied to the Women's Health Initiative cohort data to examine the associations between the sodium/potassium intake ratio and the total cardiovascular disease.

Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers’ refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user’s nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user’s subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.

Many aquatic ecosystems are seriously threatened by cyanobacteria blooms; gas vesicles enable cyanobacteria to form harmful cyanobacterial blooms rapidly. Many lake managers try to control cyanobacterial blooms by collapsing gas vesicle, but it is still unclear whether gas vesicle recovery will cause this method to fail. Through the culture experiments of three cyanobacteria, it was found that all cyanobacteria with collapsed gas vesicles can rapidly regain buoyancy in a few days under nutrient-sufficient environments, and average gas vesicle content was even 9% higher than initially. In contrast, buoyancy recovery of all cyanobacteria under nutrient-limited environments was significantly worse. After culture experiments, the average gas vesicle content of all cyanobacteria in phosphorus-limited environments only reach 49% of the initial value. The gas vesicle content of two non-nitrogen-fixing cyanobacteria in nitrogen-limited environments only reached 38% of initial value. The buoyancy of cyanobacteria in different tropic levels was similar to the gas vesicle content. These results indicate that collapsing gas vesicles can only control cyanobacterial blooms in the short-term. To control cyanobacterial blooms in the long-term, in deep lakes, lake managers should discharge gas vesicles’ collapsed cyanobacteria into deep water. In shallow lakes, the disruption of gas vesicles must be combined with nutrient control measures to effectively control cyanobacteria blooms.

Background. Coronavirus disease 2019 (COVID-19) has resulted in significant morbidity and mortality in solid organ transplant (SOT) recipients. The National COVID Cohort Collaborative was developed to facilitate analysis of patient-level data for those tested for COVID-19 across the United States. Methods. In this study, we identified a cohort of SOT recipients testing positive or negative for COVID-19 (COVID+ and COVID−, respectively) between January 1, 2020, and November 20, 2020. Univariable and multivariable logistic regression were used to determine predictors of a positive result among those tested. Outcomes following COVID-19 diagnosis were also explored. Results. Of 18 121 SOT patients tested, 1925 were positive (10.6%). COVID+ SOT patients were more likely to have a kidney transplant and be non-White race. Comorbidities were common in all SOT patients but significantly more common in those who were COVID+. Of COVID+ SOT, 42.9% required hospital admission. COVID+ status was the strongest predictor of acute kidney injury (AKI), rejection, and graft failure in the 90 d after testing. A total of 40.9% of COVID+ SOT experienced a major adverse renal or cardiac event, 16.3% experienced a major adverse cardiac event, 35.3% experienced AKI, and 1.5% experienced graft loss. Conclusions. In the largest US cohort of COVID+ SOT recipients to date, we identified patient factors associated with the diagnosis of COVID-19 and outcomes following infection, including a high incidence of major adverse renal or cardiac event and AKI.

Abstract Illegal wildlife trade is gaining prominence as a threat to biodiversity, but addressing it remains challenging. To help inform proactive policy responses in the face of uncertainty, in 2018 we conducted a horizon scan of significant emerging issues. We built upon existing iterative horizon scanning methods, using an open and global participatory approach to evaluate and rank issues from a diverse range of sources. Prioritized issues related to three themes: developments in biological, information, and financial technologies; changing trends in demand and information; and socioeconomic, geopolitical shifts and influences. The issues covered areas ranging from changing demographic and economic factors to innovations in technology and communications that affect illegal wildlife trade markets globally; the top three issues related to China, illustrating its vital role in tackling emerging threats. This analysis can support national governments, international bodies, researchers, and nongovernmental organizations as they develop strategies for addressing the illegal wildlife trade.

The probiotic Enterococcus faecium HDRsEf1 (Ef1) has been shown to have positive effects on piglet diarrhoea, but the mechanism has not yet been elucidated. In this study, using the IPEC-J2 cell line to mimic intestinal epithelial cells and enterotoxigenic Escherichia coli (ETEC) K88ac as a representative intestinal pathogen, the mechanism underlying Ef1 protection against an enteropathogen was investigated. The results demonstrated that Ef1 was effective in displacing K88ac from the IPEC-J2 cell layer. Moreover, Ef1 and its cell-free supernatant (S-Ef1) modulate IL-8 released by IPEC-J2 cells. Ef1 and its cell-free supernatant showed the potential to protect enterocytes from an acute inflammatory response. In addition, Ef1 and its cell-free supernatant increased the transepithelial electrical resistance (TEER) of the enterocyte monolayer, thus strengthening the intestinal barrier against ETEC. These results may contribute to the development of therapeutic interventions using Ef1 in intestinal disorders of piglets.

Sirolimus (SRL) is a mammalian target of rapamycin (mTOR) inhibitor. The whole blood concentration of SRL is routinely monitored to tailor dosage and prevent toxicity. Currently, the enzyme multiplied immunoassay technique (EMIT) is often applied to perform therapeutic drug monitoring (TDM) of SRL, but the cross-reactivity with various metabolites is of great concern. A more specific method is required, such as liquid chromatography–tandem mass spectrometry (LC-MS/MS). However, no study on the method comparison of the EMIT and LC-MS/MS for the measurement of whole blood SRL concentration in children with vascular anomalies has been reported. This study developed a simple and sensitive LC-MS/MS assay for the determination of SRL. Meanwhile, consistency between LC-MS/MS and the EMIT was evaluated by linear regression and Bland–Altman analysis. Whole blood samples were deproteinized with methanol for erythrocyte lysis, and the resulting solution was injected into the LC-MS/MS system using the positive electrospray ionization mode. The multiple reaction monitoring transitions of m/z 931.7 → 864.6 and m/z 934.7 → 864.6 were used for SRL and SRL-d3 as the internal standards, respectively. The analytes were separated on a C18 column with a gradient mobile phase (0.1 mM formic acid and 0.05 mM ammonium acetate in methanol/ultrapure water). Blood samples collected from children with vascular anomalies undergoing SRL therapy were tested by EMIT and by LC-MS/MS. The linear range of LC-MS/MS was 0.500–50.0 ng/ml and that of the EMIT was 3.50–30.0 ng/ml. A significant positive correlation between the two assays was established with a regression equation described as [EMIT] = 1.281 × [LC−MS/MS] + 2.450 (r = 0.8361). Bland–Altman plots showed a mean concentration overestimation of 4.7 ng/ml [95% CI: (−3.1, 12.6)] and a positive bias of 63.1% [95% CI: (−36.1, 162.3)] generated by the EMIT more than that of by LC-MS/MS. In conclusion, the two methods were closely correlated, indicating that switching between the two methods is feasible. Considering the overestimation nature of the EMIT assay, switching from the EMIT to the LC-MS/MS method deserves close attention and necessary re-evaluation for the target therapeutic reference range, may be required when methods are switched within the same clinical laboratory or results are compared between different laboratories.