Zhengjun Zhang

Identifying patient subgroups is increasingly vital for advancing personalized and precision medicine, where patient heterogeneity plays a central role. Subgroups can be revealed by examining how predictors depend on each other. However, traditional global dependence measures, such as Kendall’s τ, often overlook localized and nonlinear relationships, potentially missing critical patterns that define subpopulations. Existing local dependence measures are typically constrained to linear structures or lack flexibility in capturing complex local behaviors. We propose a novel, intuitive, and highly flexible framework, the Generalized Local Kendall’s τ, for assessing nonlinear local dependence. This method allows local regions to take diverse shapes (e.g., rectangles, squares, extreme-value neighborhoods), thereby unifying global, local, and tail dependence within a flexible and interpretable structure. The framework includes U-statistic-based and copula model-based estimators, introducing four new quantile dependence measures and novel tail dependence coefficients. We develop two visualization tools—Type I and Type II local Kendall’s τ surfaces—applied to Archimedean copulas to aid interpretation. Comparisons show that global Kendall’s τ often differs markedly from its local versions, emphasizing the added value of local analysis. Simulation results indicate that when the model is correctly specified, the copula-based estimator exhibits significantly superior performance. We illustrate the framework’s practical utility using Parkinson’s disease and COVID-19 datasets, showing its ability to detect subgroup-specific dependence patterns vital for precision medicine.

We show that the leading bubble test suffers severe size distortion when fundamentals incorporate general-purpose technology adoption. Embedding a hump-shaped technology shock in the Campbell-Shiller present-value model, we prove that the fundamental price becomes locally explosive during adoption, contaminating the test's limit distribution with a non-centrality parameter proportional to the shock's peak. We propose a fundamental-versus-speculative decomposition that projects prices onto observable technology proxies and applies the test to the residual. Empirically, the decomposition eliminates evidence of speculation in the 2020-2025 AI rally while confirming a speculative peak confined to December 1999-March 2000 in the dot-com episode.

Background: Sepsis biomarker research over the past 30 years has been plagued by the use of wrong animal models and inappropriate patient selections, leading to the failure of translating findings into precision medicine. Thousands of sepsis-related gene biomarkers have been published, but this excess hinders medical advancement because (1) an overwhelming number of genes make targeted drug development and precision medicine unfeasible; (2) many biomarkers lack cross-cohort validation, rendering them clinically unhelpful. Our goal is to identify a highly informative, single-digit set of sepsis biomarkers to advance precision medicine.Methods: We conducted large-scale research on heterogeneous populations, including patients with sepsis, severe sepsis, and septic shocks, and collected plasma samples from 32 sepsis patients and 18 healthy controls at Renmin Hospital of Wuhan University, China. RNA was isolated using the HYCEZMBIO Serum/Plasma RNA Kit, and RT-qPCR was performed on the Roche Light Cycler 480 platform. An AI-based max-logistic competing classifier was applied across 11 cohorts with thousands of samples, using both self-designed and public datasets to identify the most critical sepsis biomarkers.Results: Our analysis highlights CKAP4, FCAR, and RNF4 as key genetic drivers in sepsis-related variations. In whole blood, NONO is crucial for immune response, while in plasma, PLEKHO1 and BMP6 reveal further genetic heterogeneities. Pediatric patients also exhibit significant contributions from RNASE2 and OGFOD3. These genes form the most effective miniature set of biomarkers.Conclusion: Achieving 99.42% accuracy across cohorts, this miniature set outperforms larger published gene sets. These findings provide critical insights for personalized risk assessment, targeted drug development, and tailored treatments for both adult and pediatric sepsis patients.

The relationship between a response variable and its covariates can vary significantly, especially in scenarios where covariates take on extremely high or low values. This paper introduces a max-linear tail regression model specifically designed to capture such extreme relationships. To estimate the regression coefficients within this framework, we propose a novel M-estimator based on extreme value theory. The consistency and asymptotic normality of our proposed estimator are rigorously established under mild conditions. Simulation results demonstrate that our estimation method outperforms the conditional least squares approach. We validate the practical applicability of our model through two case studies: one using financial data and the other using rainfall data.

This study aimed to evaluate the therapeutic properties of the traditional Chinese medicine Xuesanqi (XSQ, from the rhizome of Polygonum amplexicaule D. Don) in treating ulcerative colitis. We hypothesized that its many active components can alleviate symptoms of colitis by regulating the gut microbiota, its metabolites, and various signaling pathways. To test our hypotheses, we designed a DSS- induced colitis model in C57BL/6 male mice. Apparent metrics were evaluated in each group of mice and performed histological analysis of relevant tissues. The gut microbial composition was analyzed by 16S rRNA sequencing of bacteria. Simultaneously, the SCFAs content was detected by gas chromatography, inflammatory factor secretion was evaluated by ELISA or western-blot, the expression of tight junction protein and key proteins of the MAPK signaling pathway were analyzed by western-blot. Our result showed that the treatment with XSQ alleviated significant various symptoms such as weight loss, blood in stool, and shortening of colon. In addition, XSQ treatment restored the dysregulated gut microbiota in colitis mice, increased short chain fatty acids (SCFAs) and normalized the MAPK/ERK/JNK signaling pathways, promoted expression of tight junction protein Occludin, Claudin-1, and E-cadherin proteins. Furthermore, we also observed a dose-dependent pattern in these treatment responses. These findings demonstrated the active components of XSQ is a promising new treatment platform for ulcerative colitis. Graphical Abstract

The origin of COVID-19 remains unclear despite extensive research. Theoretical models can simplify complex epigenetic landscapes by reducing vast methylation sites into manageable sets, revealing fundamental pathogen interactions that leap medical advances for the first time in tracing virus origin in the literature and practices. In our study, a max-logistic intelligence classifier analyzed 865,859 Infinium MethylationEPIC sites (CpGs), identifying eight CpGs that achieved 100% accuracy in distinguishing COVID-19 patients from other respiratory disease patients and healthy controls. One CpG, cg07126281, linked to the SAMM50 gene, shares genetic ties with rare infectious diseases like Sennetsu fever and glanders, suggesting a potential connection between COVID-19 and these diseases, possibly transmitted through contaminated seafood or glanders-infected individuals. Identifying such links among 865,859 CpG sites is challenging, with a random correlation probability of less than one in ten million. However, the likelihood of finding meaningful associations with rare diseases lowers this probability to one in one hundred million, reinforcing the credibility of our findings. These results highlight the importance of investigating seafood markets and global supply chains in tracing COVID-19's origins and emphasize the need for ongoing biosafety and biosecurity measures to prevent future outbreaks.