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Harmful cyanobacterial blooms (CyanoHABs) pose a significant threat to global water quality. Although eutrophication and climate change are recognized as key drivers of CyanoHABs proliferation, their synergistic effects remain elusive, hindering effective mitigation strategies. Here, we present a causal inference framework that leverages state-space reconstruction and empirical dynamic modeling to unravel the complex, nonlinear interactions governing CyanoHABs dynamics. Focusing on Microcystis blooms dynamics in Dianchi Lake (China), our approach uniquely integrates causal inference with time-series embedding, reconstructing the ecosystem's hidden dynamics in a higher-dimensional geometric space. This foundation enables us to rigorously quantify causal drivers-such as nutrient loading and temperature-while overcoming the limitations of traditional correlation-based analyses. Our causal network analysis reveals distinct nonlinear responses of chlorophyll-a (Chl-a) concentration and Microcystis density to different nutrient drivers. Specifically, we found that in-lake total phosphorus (TP) exerts a stronger causal influence on overall algal dynamics than total nitrogen (TN). In contrast, external nutrient loading shows greater influence over Microcystis density compared to in-lake nutrients. Through scenario simulations, we further demonstrate that rising air temperatures amplify Chl-a concentration and Microcystis biomass through increased water temperatures, whereas precipitation-induced nutrient changes preferentially stimulate Chl-a production over Microcystis growth. Notably, we identified contrasting seasonal response patterns, with Chl-a exhibiting greater sensitivity to dry-season conditions while Microcystis density responded more strongly to wet-season drivers. By bridging mechanistic understanding and predictive modeling, our work offers a transformative tool for forecasting and managing CyanoHABs in changing climates.
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http://dx.doi.org/10.1016/j.hal.2025.102911 | DOI Listing |
Clin Anat
September 2025
Department of Communication Disorders and Sciences, Rush University Medical Center, Chicago, Illinois, USA.
This research sought to examine the prevalence and severity of hyperostosis frontalis interna (HFI) in the Chicagoland anatomical body donor population. The study further aimed to elucidate potential demographic risk factors for HFI, including sex, age at death, and structural vulnerability index (SVI), as well as any common comorbidities, as gleaned from death certificates. HFI is an irregular bony overgrowth of the endocranial surface of the frontal bone.
View Article and Find Full Text PDFHum Reprod Open
August 2025
Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Study Question: Do social determinants of health (SDoH) influence the age at menopause among women?
Summary Answer: In our study, adverse SDoH, particularly family low income-to-poverty ratio (PIR), low education level, and the marital status of being widowed, are associated with earlier age at menopause.
What Is Known Already: Some prior studies have considered certain SDoH variables (such as educational attainment and marital status) as potential factors influencing age at menopause, but systematic evidence clearly defining the relationship between multidimensional SDoH and menopausal age remains lacking.
Study Design Size Duration: This cross-sectional analysis included 6083 naturally menopausal women from 10 cycles (1999-2018) of the United States National Health and Nutrition Examination Survey (NHANES) and excluded cases of surgical menopause.
Bayesian Anal
January 2025
Department of Statistics, University of Washington, Seattle, USA.
We introduce the BREASE framework for the Bayesian analysis of randomized controlled trials with binary treatment and outcome. Approaching the problem from a causal inference perspective, we propose parameterizing the likelihood in terms of the aseline isk, fficacy, and dverse ide ffects of the treatment, along with a flexible, yet intuitive and tractable jointly independent beta prior distribution on these parameters, which we show to be a generalization of the Dirichlet prior for the joint distribution of potential outcomes. Our approach has a number of desirable characteristics when compared to current mainstream alternatives: (i) it naturally induces prior dependence between expected outcomes in the treatment and control groups; (ii) as the baseline risk, efficacy and risk of adverse side effects are quantities commonly present in the clinicians' vocabulary, the hyperparameters of the prior are directly interpretable, thus facilitating the elicitation of prior knowledge and sensitivity analysis; and (iii) we provide analytical formulae for the marginal likelihood, Bayes factor, and other posterior quantities, as well as an exact posterior sampling algorithm and an accurate and fast data-augmented Gibbs sampler in cases where traditional MCMC fails.
View Article and Find Full Text PDFJ Appl Stat
January 2025
Department of Sociology, University of Pennsylvania, Philadelphia, PA, USA.
This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes.
View Article and Find Full Text PDFFront Psychol
August 2025
Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
Psychology's crises (e.g., replicability, generalisability) are currently believed to derive from Questionable Research Practices (QRPs), thus scientific misconduct.
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