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Machine learning model for prediction of coronavirus disease 2019 within 6 months after three doses of BNT162b2 in Hong Kong
Get PDFAbstractIntroduction: We aimed to develop a machine learning (ML) model to predict the risk of coronavirus disease 2019 (COVID-19) among three-dose BNT162b2 vaccine recipients in Hong Kong.Methods: A total of 304 individuals who had received three doses of BNT162b2 were recruited from three vaccination centres in Hong Kong between May and August 2021. The dataset was randomly divided into training (n=184) and testing (n=120) sets in a 6:4 ratio. Demographics, co-morbidities and medications, blood tests (complete blood count, liver and renal function tests, glycated haemoglobin level, lipid profile, and presence of hepatitis B surface antigen), and controlled attenuation parameter (CAP) were used to develop six ML models (logistic regression, linear discriminant analysis, random forest, naïve Bayes, neural network [NN], and extreme gradient boosting models) to predict COVID-19 risk. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV).Results: Among the study population (median age: 50.9 years [interquartile range=43.6-57.8]; men: 30.9% [n=94]), 27 participants (8.9%) developed COVID-19 within 6 months. Fifteen clinical variables were used to train the models. The NN model achieved the best performance, with an AUC of 0.74 (95% confidence interval [95% CI]=0.60-0.88). Using the optimal cut-off value based on the maximised Youden index, sensitivity, specificity, PPV, and NPV were 90% (95% CI=55%-100%), 58% (95% CI=48%-68%), 16% (95% CI=8%-29%), and 98% (95% CI=92%-100%), respectively. The top predictors in the NN model include age, prediabetes/diabetes, CAP, alanine aminotransferase level, and aspartate aminotransferase level.Conclusion: An NN model integrating 15 clinical variables effectively identified individuals at low risk of COVID-19 following three doses of BNT162b2.published_or_final_versio
Enhanced Influenza Vaccines Extend A(H3N2) Antibody Reactivity in Older Adults but Prior Vaccination Effects Persist
Get PDFBackgroundInfluenza vaccine effectiveness can be reduced in older adults and among repeatedly vaccinated groups. Results from year 1 of “PIVOT,” a randomized trial among adults aged ≥65 years in Hong Kong, showed that adjuvanted (Adj), high-dose (HD), and recombinant hemagglutinin (rHA) vaccines induced greater antibody responses against vaccine viruses than standard-dose (SD) influenza vaccine. Here, we examine the breadth of A(H3N2)-reactive antibodies induced during the first 2 study years (2017/2018, 2018/2019), and compare participants who received influenza vaccination annually, or not at all, for 5 years preceding enrollment.Methods14–20 PIVOT participants per vaccine and prior vaccination group (0/5 or 5/5 prior years) who provided sera on days 0, 30, and 182 in year 1 and days 0 and 30 in year 2 were assessed. Hemagglutination inhibition (HAI) antibody titers were measured against 30 viruses spanning 1968 to 2018.ResultsIn year 1, rHA and Adj but not HD vaccines induced titers ≥40 and titer rises ≥4-fold (seroconversion) against significantly more strains than SD vaccine among participants vaccinated 0/5 prior years. Only rHA and Adj vaccines induced titers ≥40 against post-vaccine strains. Antibody responses were poor among participants vaccinated 5/5 compared with 0/5 prior years and only rHA increased the breadth of seroconversion compared with the SD vaccine in this group. Antibody responses were weaker across groups in year 2.ConclusionsThe results suggest that Adj and particularly rHA vaccines may improve the breadth of protection against A(H3N2) viruses but may not overcome attenuating effects of repeated vaccination in older adults.published_or_final_versio
Exploring the potential clinical application of CEST MRI in the diagnosis of Alzheimer's disease
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Measuring gender and racial biases in large language models: Intersectional evidence from automated resume evaluation
No full textIn traditional decision-making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women and racial/ethnic minorities (1–4). Recently, the growing popularity of large language model (LLM)-based AI signals a potential shift from human to AI-based decision-making. How would this transition affect the distributional outcomes across social groups? Here, we investigate the gender and racial biases of a number of commonly used LLMs, including OpenAI's GPT-3.5 Turbo and GPT-4o, Google's Gemini 1.5 Flash, Anthropic AI's Claude 3.5 Sonnet, and Meta's Llama 3-70b, in a high-stakes decision-making setting of assessing entry-level job candidates from diverse social groups. Instructing the models to score ∼361,000 resumes with randomized social identities, we find that the LLMs award higher assessment scores for female candidates with similar work experience, education, and skills, but lower scores for black male candidates with comparable qualifications. These biases may result in ∼1–3 percentage-point differences in hiring probabilities for otherwise similar candidates at a certain threshold and are consistent across various job positions and subsamples. Meanwhile, many models are biased against black male candidates. Our results indicate that LLM-based AI systems demonstrate significant biases, varying in terms of the directions and magnitudes across different social groups. Further research is needed to comprehend the root causes of these outcomes and develop strategies to minimize the remaining biases in AI systems. As AI-based decision-making tools are increasingly employed across diverse domains, our findings underscore the necessity of understanding and addressing the potential unequal outcomes to ensure equitable outcomes across social groups.</p
Interdependency in transportation system resilience: Review and discussion
No full textThe growing interconnectivity of transportation infrastructures amplifies system interdependencies and significantly influences system resilience during disruptive events. While these dependencies can offer backup routes and ensure service continuity, they also heighten the risk of widespread disruptions following a single failure. This paper provides a thorough examination of the literature on interdependency and resilience in transportation systems. The discussion begins with an exploration of the forms of interdependency in both single-modal and multi-modal transportation systems, delving into their definitions and the methodologies employed for their modeling. Subsequently, the paper analyzes the dual-edged impact of interdependency on transportation resilience, considering both its potential to enhance robustness and its propensity to exacerbate the consequences of disruptions. The review concludes with an assessment of the current limitations in understanding and addressing these issues, proposing future research directions aimed at developing more resilient transportation systems
Untangling the relationship between teacher self-efficacy, job satisfaction, and occupational commitment
No full textWhile existing studies have substantially confirmed a three-dimensional occupational commitment model comprising affective, normative, and continuance components, previous studies of the antecedents of occupational commitment have mostly viewed such commitment as a broad, undifferentiated construct. Applying the three-dimensional model, this study examined how classroom-teaching teacher self-efficacy (TSE), teacher–student TSE, school decision-making TSE, and job satisfaction are related to teachers’ affective, normative, and continuance commitment. Structural equation modelling of survey data from 1,424 teachers in mainland China indicated that normative commitment had a closer relationship with teacher self-efficacy than the other two types of commitment did. Teacher-student TSE had the strongest association with affective commitment, and school decision-making TSE had the strongest association with normative commitment. Further, it was found that job satisfaction had a much greater mediating effect on affective and normative commitment than on continuance commitment
Keyframe-Guided Creative Video Inpainting
No full textVideo inpainting, which aims to fill missing regions with visually coherent content, has emerged as a crucial technique for creative applications such as editing. While existing approaches achieve visual consistency or text-guided generation, they often struggle to balance coherence and creative diversity. In this work, we introduce VideoRepainter, a twostage framework that allows users to inpaint a keyframe using established image-level techniques, then propagate the changes to other frames. Our approach can leverage stateof-the-art image models for keyframe manipulation, thereby easing the burden of the video-inpainting process. To this end, we integrate an image-to-video model with a symmetric condition mechanism to address ambiguity caused by direct mask downsampling. We further explore efficient strategies for mask synthesis and parameter tuning to reduce costs in data processing and model training. Evaluations demonstrate our method achieves superior results in both visual fidelity and content diversity compared to existing approaches, providing a practical solution for creative video manipulation. See our Project Page for more details.</p
Constraining the z ∼ 1 Initial Mass Function with HST and JWST Lensed Stars in MACS J0416.1−2403
No full textOur understanding of galaxy properties and evolution is contingent on knowing the initial mass function (IMF), and yet to date the IMF is constrained only to local galaxies. Individual stars are now becoming routinely detected at cosmological distances, where luminous stars such as supergiants in background galaxies strongly lensed by galaxy clusters are temporarily further magnified by huge factors (up to 104) by intracluster stars, thus being detected as transients. The detection rate of these events depends on the abundance of luminous stars in the background galaxy and is thus sensitive to the IMF and the star formation history (SFH), especially for the blue supergiants detected as transients in the rest-frame ultraviolet/optical filters. As a proof of concept, we use simple SFH and IMF models constrained by spectral energy distributions (SEDs) to see how well we can predict the Hubble Space Telescope and James Webb Space Telescope transient detection rate in a lensed arc dubbed “Spock” (z = 1.0054). We find that demanding a simultaneous fit of the SED and the transient detection rate places constraints on the IMF, independent of the assumed simple SFH model. We conclude that our likelihood analysis indicates that the data definitively prefers the “Spock” galaxy to have a Salpeter IMF (α = 2.35) rather than a top-heavy IMF (α = 1)—which is thought to be the case in the early universe—with no clear excess of supergiants above the standard IMF
Interparental Hostility and Cooperative Interparental Conflict Interact to Predict Chinese Adolescents' Problematic Smartphone Use Through Adolescents' Conflict Appraisals
No full textInterparental hostility is a risk factor for adolescents' problematic smartphone use (PSU). However, the mechanisms implicated in this association remain unclear. Using three-wave, multi-informant data from 364 Chinese parent–child dyads over the junior high school years (grades 7–9, Mchild age = 12.00, SD = 0.38, 58.8% boys), this study examined how parents' reports of interparental hostility at Grade 7 related to adolescents' reports of PSU at Grade 9, with adolescents' reports of conflict appraisals at Grade 8 (i.e., threat, self-blame, low coping efficacy) tested as potential mediators and parents' reports of interparental cooperative conflict at Grade 7 (i.e., constructive problem solving, marital warmth, and effective conflict resolution) as possible moderators. Results demonstrated that when interparental constructive problem solving at Grade 7 was low, interparental hostility at Grade 7 positively predicted adolescents' PSU at Grade 9 through a positive association with adolescents' threat at Grade 8. In contrast, when interparental constructive problem solving was high, interparental hostility at Grade 7 negatively predicted adolescents' PSU at Grade 9 through a negative association with adolescents' threat at Grade 8. Furthermore, interparental hostility at Grade 7 was negatively associated with adolescents' self-blame at Grade 8 when interparental effective conflict resolution at Grade 7 was low. These findings shed some light on the complexity in the underlying mechanisms for the links between interparental hostility and adolescents' PSU. These findings highlight the need for efforts among parents, clinicians, and policymakers to mitigate adolescents' PSU by addressing interparental conflict and adolescents' conflict appraisals.</p