Individualized treatment rules (ITRs) map an individual patient's characteristics to their recommended treatment value. Typically, the optimal ITR is defined as the rule which maximizes a mean counterfactual outcome; the resulting ITR maximizes the effect of treatment along all causal pathways to the outcome, including indirect pathways through mediating variables. Although maximizing the total effect is often sufficient, explicitly incorporating causal mediation in an ITR analysis has several potential benefits such as enhanced interpretability, and additional flexibility in targeting specific causal pathways. For this purpose, we introduce novel Bayesian semiparametric and nonparametric estimators for conditional mediation effects in the presence of multiple mediators and show how they can be used to estimate optimal ITRs. We demonstrate the proposed methodology via an application to optimal kidney allocation with hepatitis C positive donors.

Supplementary Table 3: Statistical Analysis of Multicolor Flow Cytometry Characterization of Tumors.

Instabilities at the coupled deformable interfaces of a nematic-isotropic free bilayer can engender rich morphological patterns governed by an intricate interplay of nematic elasticity, surface and interfacial energetics, gravitational forces, and intermolecular forces. This work presents a theoretical investigation of the instability in a bilayer composed of a liquid crystal film resting on a water layer having a free deformable nematic-air surface and a confined nematic-isotropic interface. Utilizing a long-wave hydrodynamic model, the formulation couples the Ericksen-Leslie equations with the Navier-Stokes equations to quantify growth rates of the instability across NLC thickness and director orientations. The formulation examines the competing roles of gravitational, van der Waals, and elastic forces in governing the deformations at the bilayer interfaces. For micro-thick films, density contrast between the layers initiates classical Rayleigh-Taylor instabilities (RTIs), while in nano-thin regimes, disjoining pressure drives mode dynamics. In both scenarios, we identify a pair of primary instability modes-the confined interfacial mode (CIM) and the free surface mode (FSM)-whose relative dominance is dictated by film thickness, anchoring configuration, and the spreading coefficients at the surface or interface. Systematic variation of director orientations and three canonical surface anchoring arrangements reveals critical transitions and instability mode responses, especially under asymmetrical spreading conditions. The results uncover mode switching controlled by NLC orientation and interfacial energetics, offering fundamental insights into the spatiotemporal structuring of layered soft materials. The framework provides an elementary direction for designing reconfigurable morphologies in NLC-based bilayer systems relevant to wetting, templating, and self-patterning applications.

Non-communicable diseases pose a serious threat to the world population. In this direction, point-of-care testing (POCT) devices can expedite the delivery of rapid and timely results, offering a clinically significant advantage in patient management even at low resource settings. To address these issues, this study has been performed to make a comparison between a portable blood testing analyzer device named “Mobilab” as a POCT device with a clinical laboratory fully automated analyzer (Siemens Dimension EXL 200) to evaluate the device performance. Mobilab is a portable, Internet of Things (IoT) enabled, battery operated, and smartphone-app-operated device that provides digital patient data in real-time at an affordable cost. Present study evaluated the efficacy of Mobilab by analyzing five biochemical parameters, viz., Cholesterol (CHOL), Triglyceride (TGL), Low-Density Lipoprotein-Cholesterol (LDL-C), Creatinine (CRE), and Uric acid (UA), as crucial parameters that are indicative of normal functionality in healthy individuals. Validation of Mobilab was performed by comparing results from 50-52 human serum samples assessing analytical sensitivity, linearity, precision, and performance matrices. In terms of statistical analysis, all the tested parameters showed consistency and accuracy, indicating reliable and consistent performance by Mobilab. As a POCT device, Mobilab delivers digital, rapid, and direct results from patient’ blood sample, eliminating the need for manual result analysis. Utilizing Mobilab to monitor health parameters could promote a healthy lifestyle with cost-effectiveness and time efficiency, potentially preventing the development of critical health conditions in resource-constrained areas of both developed and developing countries.

Objectives: The first case of coronavirus disease 2019 (COVID-19) was reported in December 2019 and the number of reported cases has been increased day by day. This study intends to show the scenario of time to reach the first and second peak of COVID-19 cases in different countries and also explores the potential determinants of the first and second peak time using global COVID-19 data extracted from Our World in Data dataset between 22 January 2020 and 21 January 2021. Methods: The semi-parametric Cox-PH model has been performed to conclude effects of the covariates. Results: The results reveal that there is a significant difference between survival times across different categories of several covariates in both the first and second peak. We have found that continent, Human Development Index, population density, life expectancy, cardiovascular death rate, hospital beds per thousand, total number of deaths have significant effects on determining the first peak of COVID-19 cases. On the other hand, Human Development Index, population density and diabetes prevalence have been found as only the significant factors of the second peak. Conclusions: This paper sheds light on the awareness of factors of the COVID-19 waves. Dhaka Univ. J. Sci. 74(2): 321–330, 2026 (July)

Human papillomavirus (HPV) is a well-established prognostic factor in head and neck (HN) cancer, with HPV-positive patients exhibiting markedly better survival outcomes compared to their HPV-negative counterparts. While advances in (cancer) genomics have been pivotal to precision medicine, existing gene screening methods for identifying molecular markers to predict survival often fail to account for HPV status. This oversight can result in missing important genes, whose effects are confounded or overshadowed by HPV, thereby limiting the biological interpretability and clinical utility of identified markers. To address these limitations, we propose a novel conditional screening method for ultrahigh-dimensional right-censored survival data that adjusts for HPV status. This approach identifies prognostic genes with independent associations with survival while also capturing HPV-specific interactions and synergistic effects. The proposed method employs a two-stage, model-free framework that combines nonparametric statistics for initial screening with a unified false discovery rate (FDR) control procedure to refine feature selection. Simulation studies demonstrate its advantages over existing alternatives. Application of the conditional screening framework to HN cancer data from The Cancer Genome Atlas revealed a set of robust prognostic genes, uncovering new insights into the molecular pathways driving survival outcomes across HPV subgroups.

Broken adaptive ridge (BAR) penalty approximates L_0-regularization through iterative reweighting of L2 penalties. This penalty enjoys both the oracle property and the grouping effect for highly correlated covariates, making it particularly attractive for penalized regression with complex dependence among predictors. In this paper, we develop a BAR-penalized linear rank regression method for the semiparametric accelerated failure time model with right-censored data. Computational tractability is achieved by applying induced smoothing to the nonsmooth Gehan-type rank estimating function, yielding a more stable framework for estimation and inference. For scalable penalization, we develop a cyclic coordinate descent algorithm that minimizes the penalized objective function, and estimates the regression coefficients in a coordinate-wise manner. We further extend the proposed method to more complex survival endpoints, such as multivariate partly interval-censored (PIC) data. Under mild conditions, the proposed estimator satisfies both the oracle property and the grouping effect, and the variance estimator of the informative coefficients can be derived in analytic form. Numerical studies using synthetic data compare our approach to several well-known penalties, and demonstrate its superior selection accuracy and estimation efficiency across various scenarios. Furthermore, applications to right-censored outcomes from primary biliary cirrhosis, and correlated PIC outcomes from colorectal cancer further illustrate the practical utility of the proposed method. The R package aftPenCDA for implementing the method is available on R CRAN.

This manuscript presents an innovative statistical model to quantify periodontal disease in the context of complex medical data. A mixed-effects model incorporating skewed random effects and heavy-tailed residuals is introduced, ensuring robust handling of non-normal data distributions. The fixed effect is modeled as a combination of a slope parameter and a single index function, constrained to be monotonic increasing for meaningful interpretation. This approach captures different dimensions of periodontal disease progression by integrating Clinical Attachment Level (CAL) and Pocket Depth (PD) biomarkers within a unified analytical framework. A variable selection method based on the grouped horseshoe prior is employed, addressing the relatively high number of risk factors. Furthermore, survey weight information typically provided with large survey data is incorporated to ensure accurate inference. This comprehensive methodology significantly advances the statistical quantification of periodontal disease, offering a nuanced and precise assessment of risk factors and disease progression. The proposed methodology is implemented in the \textsf{R} package \href{https://cran.r-project.org/package=MSIMST}{\textsc{MSIMST}}.

This research examines partial dewetting and labyrinthine morphologies in thin polystyrene (PS) films using a ternary solvent mixture of water, acetone, and methyl ethyl ketone (MEK) (15:3:7 by volume). The films are initially glassy but destabilize due to the solvent into mixed structures containing labyrinth-like patterns and holes, not droplets. Morphological changes result from solvent–nonsolvent interactions. MEK is a good solvent that swells the film, lowers the glass transition temperature, and increases chain mobility, while acetone, as a cosolvent, provides uniform solvent uptake, and water as a nonsolvent reduces film–substrate affinity and promotes spreading-driven dewetting. The opposing influence of these factors leads to viscosity gradients that drive heterogeneous nucleation of holes, their growth to large size, and partial coalescence. The process ends before equilibrium is reached as rapid evaporation of MEK balances driving forces with viscosity, and traps intermediate structures. The physics behind the existence or a mechanical pathway to intermediate structures is provided by studies of the spreading coefficient and hole growth. This work demonstrates engineering solvent mixtures for controlled non-equilibrium surface patterning in polymer thin films.

This paper presents a unified rank-based inferential procedure for fitting the accelerated failure time model to partially interval-censored data. A Gehan-type monotone estimating function is constructed based on the idea of the familiar weighted log-rank test, and an extension to a general class of rank-based estimating functions is suggested. The proposed estimators can be obtained via linear programming and are shown to be consistent and asymptotically normal via standard empirical process theory. Unlike common maximum likelihood-based estimators for partially interval-censored regression models, our approach can directly provide a regression coefficient estimator without involving a complex nonparametric estimation of the underlying residual distribution function. An efficient variance estimation procedure for the regression coefficient estimator is considered. Moreover, we extend the proposed rank-based procedure to the linear regression analysis of multivariate clustered partially interval-censored data. The finite-sample operating characteristics of our approach are examined via simulation studies. Data example from a colorectal cancer study illustrates the practical usefulness of the method.

Observations of groundwater pollutants, such as arsenic or Perfluorooctane sulfonate (PFOS), are riddled with left censoring. These measurements have an impact on the health and lifestyle of the populace. Left censoring of these spatially correlated observations is usually addressed by applying Gaussian processes (GPs), which have theoretical advantages. However, this comes with a challenging computational complexity of $\mathcal{O}({n^{3}})$, impractical for large datasets. Additionally, a sizable proportion of the left-censored data creates further bottlenecks since the likelihood computation now involves an intractable high-dimensional integral of the multivariate Gaussian density. In this article, we tackle these two problems simultaneously by approximating the GP with a Gaussian Markov random field (GMRF) approach that exploits an explicit link between a GP with Matérn correlation function and a GMRF using stochastic partial differential equations (SPDEs). We introduce a GMRF-based measurement error into the model, which alleviates the likelihood computation for the censored data, drastically improving the computational speed while maintaining admirable accuracy. Our approach demonstrates robustness and substantial computational scalability compared to state-of-the-art methods for censored spatial responses across various simulation settings. Finally, the fit of this fully Bayesian model to the concentration of PFOS in groundwater available at 24,959 sites across California, where 46.62% responses are censored, produces prediction surface and uncertainty quantification in real-time, thereby substantiating the applicability and scalability of the proposed method. Code for implementation is made available via GitHub.