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An entropy-initiated coupled-trait ODE framework for modeling longitudinal cohort dynamics
This work introduces a minimal, information-theoretic dynamical framework for modeling longitudinal cohort data using an entropy-initiated system of coupled-trait ordinary differential equations (ECTO). For each survey wave, item-level Likert responses are compressed into a normalized Shannon entropy index that summarizes cross-sectional dispersion; this index is used to initialize the low-dimensional state variables of the autonomous ODE system. ECTO then tracks the interactions among a primary trait-like state, a secondary coupled state, and a latent environmental-stress component through phenomenological terms representing generic self-limitation, trade-offs, and feedback. Using data from the Swedish Adoption/Twin Study on Aging (SATSA), the framework reproduces broad cohort-level trajectories and is evaluated with leave-one-wave-out forecasting and comparisons against simple statistical baselines. A second longitudinal dataset of U.S. dental student data provides an external validation test, demonstrating that low-dimensional dynamics initialized from entropy measures can generalize across cohorts with different measurement instruments, demographic compositions, and timescales. Across both datasets, ECTO achieves stable out-of-sample performance, indicating that major cohort-level trends can be captured without assuming complex latent-variable models or time-varying causal inputs. Entropy here functions as a compact summary of population heterogeneity rather than a dynamical driver, and the coupled ODEs supply an interpretable alternative to high-dimensional or black box machine-learning approaches. This framework establishes a concise, transparent method for linking information-theoretic preprocessing with cohort-level dynamical modeling and provides a foundation for future multivariate or multi-cohort extensions.
AI and the PhD student: friend or foe?
Quantitative prediction and degradation mechanism of CFRP–TC4 adhesive joints under hygrothermal aging
FGF1 orchestrates circadian hepatic triglyceride secretion
Hypochloremia, non-medication related associated factors, and impact on clinical outcomes in patients with acute heart failure: Insights from resource limited setups
Background Electrolyte disturbances such as hypochloremia are common in patients with acute heart failure (AHF) and may worsen clinical outcomes. However, data from low-resource settings remain limited. Therefore, the aim of this study is to determine the prevalence of hypochloremia among AHF patients, assess its association with length of hospital stay and in-hospital mortality, and identify factors independently associated with its occurrence. Objective To determine the prevalence of hypochloremia among patients admitted with acute heart failure, to assess its non-medication association with length of hospital stay and in-hospital mortality, and to identify factors independently associated with hypochloremia in a resource-limited setting. Methods A retrospective observational cohort study of hospitalized patients with acute heart failure, with serum chloride measured at admission was conducted among 260 patients aged ≥16. Data were analysed using SPSS version 26.0. The association between hypochloremia and clinical outcomes, including in-hospital mortality and length of hospital stay, was assessed using the Chi-square test and the Mann–Whitney U test, respectively. Multivariable logistic regression analysis was performed to identify independent predictors of hypochloremia. A p-value of <0.05 was considered statistically significant. Results The prevalence of hypochloremia was 33.1% (95% CI: 27.4%–39.2%), and hypochloremic patients had significantly longer hospital stays (median: 12 days vs. 8.5 days; p = 0.001) and higher in-hospital mortality (χ² = 8.58; p = 0.003). Multivariate analysis showed that NYHA class IV heart failure [AOR = 6.96; 95% CI: 1.49–32.4; p = 0.014], history of COPD [AOR = 4.94; 95% CI: 1.36–17.9; p = 0.001], hyponatremia [AOR = 2.20; 95% CI: 1.8–9.5; p = 0.001], and hypokalemia [AOR = 4.08; 95% CI: 1.53–10.6; p = 0.004] were significantly associated with hypochloremia. Conclusion In this study hypochloremia at admission was common and associated with higher in-hospital mortality and longer hospital stays. It was more frequently observed in patients with severe heart failure, COPD, hypokalemia and hyponatremia.
Can AI models reliably forecast extreme weather events?
Hierarchical Co–Ni hydroxides integrated with carbon nanotubes via ZIF-67 templates for high-performance supercapacitors
A snow-fire bridge mechanism for the 2025 Southern California winter wildfire
Abstract In January 2025, a rare and highly destructive wildfire devastated Southern California, becoming the costliest wildfire event in recorded history. The unusual timing of this wildfire suggests the possibility of unique remote, large-scale climatic precursors that differ from those of previous wildfires. Here, through observational analysis and large-ensemble numerical simulations, we identify that western Eurasian snow cover reduction is associated with weather conditions favorable for wildfires in Southern California in December-January, and simulations indicate that this can occur via an atmospheric teleconnection from western Eurasia, across the North Pacific and into North America. Both observations and simulations show that the reduced snow cover over western Eurasia contributes to the typical wintertime western warming-eastern cooling dipole pattern in North America. The main dynamical mechanisms involve downstream propagating Rossby wave trains triggered by the reduced snow cover in western Eurasia, as well as wave-mean flow interaction over the North Pacific. Our study suggests that Eurasian snow cover anomalies can be predictively linked to both subsequent wildfire risk in California and the wintertime North American zonal dipole temperature pattern, highlighting the broader impacts of Eurasian cryosphere variability on remote climate extremes.
Lipidomic analysis of bile from patients with extrahepatic cholangiocarcinoma
Objective Cholangiocarcinoma and gallstones are significant gastrointestinal diseases with diverse etiologies and complex clinical manifestations. Understanding their underlying molecular mechanisms is crucial for advancing diagnosis and treatment strategies. In this study, we compared the lipidomic profiles of bile samples from patients with extrahepatic cholangiocarcinoma (eCCA) or choledocholithiasis with those of healthy controls to identify potential biomarkers and therapeutic targets. Methods Bile samples were prospectively collected from 33 patients undergoing endoscopic retrograde cholangiopancreatography at the Korea University Guro Hospital, including 12 patients with eCCA, 15 with choledocholithiasis, and six controls. Lipidomic profiling was performed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry. Principal component analysis, ANOVA, and volcano plots were used to identify the differential lipidomic signatures across the groups. Results A total of 230 lipid metabolites were identified, and significant differences were observed among the groups; each group had distinct lipidomic patterns. In both eCCA and choledocholithiasis, phosphatidylcholine contents were consistently more downregulated than in controls. However, diacylglycerol lipids were upregulated in eCCA while acylcarnitine lipids were upregulated in choledocholithiasis. Lysophosphatidylcholine levels were notably lower in patients with eCCA than in those with choledocholithiasis. Conclusion Our results suggested that specific lipidomic changes and their inter-relationships contribute to the pathophysiology of choledocholithiasis and eCCA. Longitudinal studies and functional assays can further validate the findings and translate them into clinical practice.
Monitoring blast wave evolution and propagation using coupled visual recording and pressure measurements
Abstract Explosives are a group of special purpose materials, which are important in various branches of industry, such as mining, civil engineering and military. Interest in this type of materials has increased recently, especially due to the current geopolitical situation. Thus, it is important to assess the properties of the explosive as well as evaluate the effects of their use. This paper presents results of research on the analysis of the pressure distribution and characterization of the blast wave produced by two explosives: Ammonal and Heksoflen (95 wt.% RDX / 5 wt.% Viton A). During the research velocity of detonation was measured by four probes placed inside of the prepared charges. The pressure distribution of the blast waves was measured with use of the three pressure probes, placed at various distances from the detonation point. The obtained data were used to determine the explosive constants related to the overpressure, based on which the overpressure prediction was made at various distances for both tested explosives. Moreover, the detonation of the explosives have been recorded with use of Phantom v9.1 high-speed camera. Performed research indicates that, the pressure of the blast wave highly depends on the type of explosive used. Blast wave caused by Heksoflen is characterized by higher maximum pressure and impulse in comparison to Ammonal. After burning of intermediate detonation products differs significantly for the two explosives. After burning of the Heksoflen intermediate products is characterized with wider zone and longer times.
Assessing the effectiveness of riparian buffers in protecting biodiversity: a meta-analysis
Expression of Concern: Enhanced surface plasmon resonance biosensor with graphene-black phosphorus heterostructure for ultra-high sensitivity refractive index detection with machine learning for behaviour prediction
Comprehensive evaluation of milk biomarkers as indicators of intramammary infection in dairy goats across lactation
Tumor-specific lncRNA IGF1R-AS1 trans-regulates chromatin interactions associated with oncogenic MYC signaling
Abstract LncRNAs have emerged as pivotal regulators in the development and progression of various human cancers. However, understanding the precise mechanisms by which lncRNAs influence cancer progression remains a substantial challenge, largely due to their cell type- and tissue-specific expression patterns and the lack of well-defined functional domains or motifs. In this study, we investigate the complex interplay between super-enhancers and lncRNAs through a comprehensive analysis of lncRNA expression in a cohort of metastatic castration-resistant prostate cancer patients. Our analysis identifies 1344 lncRNAs, among which an antisense lncRNA in the IGF1R locus named IGF1R-AS1 displayed the strongest super-enhancer association. Through pan-cancer transcriptome analysis, we find that IGF1R-AS1 is specifically transcribed in tumor specimens and is overexpressed in prostate and lung cancers. Notably, we reveal a non-canonical trans -acting role for IGF1R-AS1 whereby it interacts with chromatin remodeling complexes and architectural proteins to facilitate long-range chromatin looping between distal MYC enhancers and its promoter, leading to MYC overexpression and enhanced tumorigenicity. Collectively, our findings elucidate a mechanism by which a tumor-specific trans -acting lncRNA modulates oncogenic MYC expression through long-range chromatin interactions, suggesting IGF1R-AS1 may play an important role in the pathogenesis of MYC-driven malignancies.
Integrating biological and machine learning models for rainbow trout growth: Balancing accuracy and interpretability
Invasive species management demands predictive models that balance accuracy with ecological interpretability, yet traditional approaches often fail to capture complex environmental interactions. We evaluated hybrid frameworks integrating biological and machine learning models for rainbow trout ( Oncorhynchus mykiss ) growth in the Lower Colorado River using ten years of tag–recapture data and environmental covariates, comparing traditional and Bayesian von Bertalanffy (VBGM) and Gompertz models with Random Forests, XGBoost, LightGBM, Support Vector Regression, Neural Networks, and ensemble methods through probabilistic performance analysis. Incorporating environmental context and advanced modeling produced substantial gains, with top methods achieving 70–80 percent error reductions relative to baseline models, equivalent to 45–70 mm or 20–32 percent of mean fish length. A stacked ensemble of XGBoost and the VBGM achieved the best performance (RMSE = 15.96 mm, R 2 = 0.966 ) and exhibited stochastic dominance across the posterior, while gradient boosting models formed a strong second tier, led by LightGBM and XGBoost. Bayesian Model Averaging reached comparable accuracy while explicitly quantifying uncertainty. Even traditional mechanistic models improved by up to 80 percent when enhanced with covariates and Bayesian estimation, preserving biological interpretability through parameters such as asymptotic size and growth rate. Feature importance analysis identified initial length, time at large, and weight at release as dominant predictors, and the stacked ensemble outperformed baseline models in over 99 percent of posterior samples. These results establish hybrid ensemble frameworks as powerful tools for ecological forecasting that unite predictive performance with mechanistic insight, providing a generalizable template for systems where both accuracy and interpretability are required.
Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net
The relative role of direct orbital forcing versus CO2 and ice feedbacks on Quaternary climate
Abstract During the Quaternary (the last 2.58 million years), Earth’s climate has fluctuated between glacials and interglacials, paced by external forcings and mediated by internal feedbacks. However, General Circulation Models (GCMs), essential for addressing the mechanisms associated with these fluctuations, require substantial computational resources, meaning they are unsuitable for exploring orbital-scale variability on million-year timescales. Here, we use a GCM to calibrate a faster statistical model, or emulator, and apply this to the Quaternary. We show a good agreement between the emulated climate and proxy data over the last 800,000 years, especially the timing of glacial-interglacial cycles. A series of sensitivity experiments allows us to identify the dominant components driving long-term climate change. The results show that a combination of the CO 2 and ice sheet feedbacks provide the dominant contribution to the annual mean temperature signal, with the direct orbital radiative forcing playing only a minor role.
Non-invasive host transcriptome and HPV oncogene expression map the molecular landscape of HPV-driven cervical lesions
Cervical cancer remains a significant global health burden. Current screening methods are not yet capable of detecting molecular alterations preceding cytological abnormalities. In this study, we performed integrative transcriptomic profiling of 132 HPV-positive cervical Pap Smear specimens (NILM, ASCUS, LSIL, HSIL), combining HPV genotyping, and E6/E7 mRNA quantification to map molecular progression. Our analysis revealed stage-specific signatures: NILM displayed a “stealth infection” profile marked by upregulated protein synthesis and growth signaling (EGFR/ERBB2) alongside immune suppression. ASCUS presented a critical tipping point with introduction of early oncogenic drivers (CCND1, SHH), while LSIL prioritized viral productivity with suppressed antimicrobial defenses (MPO, DEFA1). HSIL was distinct from earlier stages and defined by cell cycle hyperactivation (CDK1, PLK1), replication licensing (MCMs), and epithelial dedifferentiation. Pathway crosstalk analysis demonstrated minimal overlap between HSIL and earlier stages (OC < 0.07), highlighting molecular discontinuity during malignant transformation. Additionally, E6/E7 mRNA Ct levels were significantly associated with lesion severity (X 2 = 24.407, df = 9, p = 0.003), indicating higher viral mRNA expression are associated with more severe cytological abnormalities. These findings highlight the transformative potential of transcriptomic profiling in cervical cancer prevention, offering stage-specific biomarkers to refine risk stratification. By integrating transcriptomics profiling with current clinical testing, clinicians can distinguish transient infections from high-risks lesions likely to progress. This combined approach addresses the critical limitations of morphology and DNA-based methods, enabling more precise therapeutic interventions and reducing unnecessary overtreatment, and the risk of undertreatment or dismissal of high-risk cases.