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Background Single-cell RNA sequencing (scRNA-seq) provides extensive opportunities to explore cellular heterogeneity but is often limited by substantial technical noise and variability. The prevalence of zero counts, arising from both biological variation and technical dropout events, poses significant challenges for downstream analyses. Existing imputation methods face inherent trade-offs: statistical approaches maintain interpretability but exhibit limited capacity for capturing complex, non-linear gene expression relationships, whereas deep learning methods demonstrate superior flexibility but are prone to overfitting and lack mechanistic interpretability, particularly in settings with limited sample sizes. Methods We present ZILLNB (Zero-Inflated Latent factors Learning-based Negative Binomial), a novel computational framework that integrates zero-inflated negative binomial (ZINB) regression with deep generative modeling. ZILLNB employs an ensemble architecture combining Information Variational Autoencoder (InfoVAE) and Generative Adversarial Network (GAN) to learn latent representations at cellular and gene levels. These latent factors serve as dynamic covariates within a ZINB regression framework, with parameters iteratively optimized through an Expectation-Maximization algorithm. This approach enables systematic decomposition of technical variability from intrinsic biological heterogeneity. Results Comparative evaluations across multiple scRNA-seq datasets demonstrate ZILLNB’s superior performance. In cell type classification tasks using mouse cortex and human PBMC datasets, ZILLNB achieved the highest Adjusted Rand index (ARI) and Adjusted Mutual Information (AMI) among tested methods, with improvements ranging from 0.05 to 0.2 over VIPER, scImpute, DCA, DeepImpute, SAVER, scMultiGAN and ALRA. For differential expression analysis validated against matched bulk RNA-seq data, ZILLNB demonstrated improvements ranging from 0.05 to 0.3 for area under the Receiver Operating Characteristic curve (AUC-ROC) and the Precision-Recall curve (AUC-PR) compared to standard and other imputation methods, with consistently lower false discovery rates. Application to idiopathic pulmonary fibrosis (IPF) datasets revealed distinct fibroblast subpopulations undergoing fibroblast-to-myofibroblast transition, validated through marker gene expression and pathway enrichment analyses. Conclusion ZILLNB provides a principled framework for addressing technical artifacts in scRNA-seq data while preserving biological variation. The integration of statistical modeling with deep learning enables robust performance across diverse analytical tasks, including cell type identification, differential expression analysis, and rare cell population discovery, demonstrating utility across common single-cell analysis tasks.

Most eukaryotic genes express alternative polyadenylation (APA) isoforms with different 3′UTR lengths, production of which is influenced by cellular conditions. Here, we show that arsenic stress elicits global shortening of 3′UTRs through preferential usage of proximal polyadenylation sites during stress and enhanced degradation of long 3′UTR isoforms during recovery. We demonstrate that RNA-binding protein TIA1 preferentially interacts with alternative 3′UTR sequences through U-rich motifs, correlating with stress granule association and mRNA decay of long 3′UTR isoforms. By contrast, genes with shortened 3′UTRs due to stress-induced APA can evade mRNA clearance and maintain transcript abundance post stress. Furthermore, we show that stress causes distinct 3′UTR size changes in proliferating and differentiated cells, highlighting its context-specific impacts on the 3′UTR landscape. Together, our data reveal a global, 3′UTR-based mRNA stability control in stressed cells and indicate that APA can function as an adaptive mechanism to preserve mRNAs in response to stress. The function and consequences of alternative polyadenylation (APA) in stressed cells are largely unclear. Here, the authors show that stress-induced mRNA degradation depends on 3′UTR length and that APA-mediated 3′UTR shortening is an adaptive stress response mechanism for selective transcript stabilization.

Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall. Here, the authors propose using quantile regression for genome-wide association studies with quantitative traits in UK Biobank, showing its advantages over linear regression in handling nonnormal distributions and identifying heterogeneous genetic effects.

Reaching is a fundamental motor skill essential for daily living, with over 50% of daily activities involving reaching movements. Understanding the development of postural adjustments made during reaching in early childhood is crucial for motor development. This study investigated the developmental characteristics of postural adjustments made by children aged 3–6 years during reaching tasks. A total of 135 typically developing children from Chinese kindergartens participated in this study. Kinematic and electromyographic (EMG) data were collected using an infrared motion capture system and surface electromyography, respectively. A two-way ANCOVA was performed to examine the effects of age and gender on kinematic and electromyographic parameters. Post hoc analyses revealed that completion time and shoulder angle showed a linear decreasing trend (p < 0.05). The variable wrist joint had an increasing trend in the high-touch task, while the elbow joint showed a nonlinear decreasing trend (p < 0.05). EMG results regarding Extensor Carpi Ulnaris (ECU) showed a decreasing trend at all phases (p < 0.05). The developmental patterns observed suggest that children progressively utilize more distal (wrist) and fewer proximal (elbow and shoulder) joints during reaching, indicating the maturation of motor patterns. However, the mechanisms of anticipation and compensation for children aged 3–6 are not yet fully understood.

The process of iron transport across the blood–brain barrier (BBB) includes two transmembrane steps: step 1, iron in circulating blood first passes through the luminal membrane (blood side) of BBB cells; step 2, it then passes through the abluminal membrane of the cell (cerebroside) and eventually enters the brain. Compared with step 1, we know relatively little about the mechanism of step 2. However, a large number of studies conducted in the past two decades since the discovery of ferroportin 1 (Fpn1) in 2000 has greatly enhanced our understanding of this issue. Accumulating evidence suggests that Fpn1 is a key player in step 2 and that Fpn1/hephaestin and/or Fpn1/ceruloplasmin iron export pathways found in the basement membrane of intestinal enterocytes have the same role in step 2. In this Review, we focus on the current understanding of the role of Fpn1 in iron transport across the abluminal membrane of the BBB. Fpn1 role in iron transport across blood–brain barrier The blood–brain barrier is a critical regulator of iron transport, essential for neurological processes and implicated in neurodegenerative diseases. This Review focuses on the role of ferroportin 1 (Fpn1) in iron transport across the abluminal membrane of the blood–brain barrier, a less-understood area compared with the luminal membrane. Fpn1, the only known cellular iron exporter, works with hephaestin and ceruloplasmin to oxidize ferrous iron (Fe 2+ ) to ferric iron (Fe 3+ ), facilitating its transport into the brain. This process is crucial for maintaining iron homeostasis and preventing oxidative damage. The study highlights the expression of Fpn1, hephaestin, and ceruloplasmin in various brain cells, suggesting their broader role in iron efflux. Future research should explore the dominant pathways under physiological conditions and the potential involvement of amyloid-β precursor protein in iron transport, which could inform therapeutic strategies for neurological diseases. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Carbon dots (CDs) have attracted extensive attention in recent years because of their high biocompatibility and unique optical property. But they could not be well applied in the drug delivery system to enable distribution in tumor sites with their low pH sensitivity. They are barriers for drug delivery. CDs as an imaging proper were conjugated with doxorubicin (DOX) lipid-coated calcium phosphate (LCP) nanoparticle, for a pH-sensitive nanocarrier and delivery of the antitumor drugs. CDs were prepared by one-step hydrothermal treatment of citric acid and ethylenediamine. The nanoparticles were simply prepared by using microemulsion technology to form calcium phosphate (CaP) core and further coated with cationic lipids. The structure was characterized by FTIR, XRD and TEM. In vitro release study revealed that DOX-CDs@LCP was pH dependent. The cytotoxicity assay demonstrated that it exhibited enhanced efficiency compared to the control group (DOX-CDs), but weaker than free DOX. The cellular uptake revealed that these pH-sensitive nanoparticles could be taken up effectively and deliver DOX into the cytoplasm to reach antitumor effect. The fluorescence imaging indicated that DOX-CDs@LCP mostly distributed in the tumor region due to the enhanced permeability and retention effect (EPR) to reduce its systematical toxicity. Importantly, an antitumor activity study demonstrated that the DOX-CDs@LCP nanoparticles had higher antitumor activity than any other groups and lower toxicity. The results showed that LCP could significantly promote the release in tumor microenvironment due to pH-response. The DOX-CDs could enhance load capacity and reduce drug premature releasing; real-time tracking of efficacy as confocal imaging contrast agent. Thus, DOX-CDs@LCP had antitumor capacity and lower systematic toxicity in tumor therapy. DOX-CDs@LCP were proven as a promising tumor pH-sensitive and imaging-guided drug delivery system for liver cancer chemotherapy.

Background While social media theoretically democratizes health information, achieving actual equity in health outcomes remains a challenge, largely due to a “capacity gap”—disparities in individuals’ abilities to critically evaluate and use information—rather than merely an “access gap.” This research moves beyond the conventional narrative of the “access gap” to instead scrutinize the emerging “capacity gap” within the Chinese context. Specifically, we investigate whether critical eHealth literacy, potentially bolstered by community-level interventions, can transform routine social media usage into tangible health empowerment for vulnerable populations with lower socioeconomic status (SES). Methods We surveyed 430 adult social media users in China using a network-based snowball sampling approach, a method chosen for its effectiveness in reaching diverse online communities, though we acknowledge its potential limitations in generalizability. The study utilized a validated, multi-dimensional instrument, including an expanded Critical eHealth Literacy Scale (eHEALS), to assess SES, usage patterns, and critical evaluation skills. Our analytic strategy employed hierarchical regression and PROCESS-based mediation and moderation analysis to test the proposed associations and the “resource substitution” hypothesis. Results The data revealed a nuanced reality: the frequency of social media use showed no direct effect on health empowerment when accounting for other factors. Instead, critical eHealth literacy acted as a crucial mediator in this relationship ( ), suggesting that usage translates to empowerment primarily through active cognitive processing. Latent profile analysis further identified a “Vulnerable Engagers” group (high use, low literacy) with the lowest empowerment scores. Moreover, participants who reported engagement in community-based workshops exhibited significantly higher critical literacy skills. Crucially, moderation analysis confirmed a “compensatory effect”: robust critical eHealth literacy was associated with a weakened link between low SES and lower empowerment, enabling these individuals to achieve empowerment levels comparable to their higher-SES counterparts. Conclusion Our findings suggest that the digital divide has evolved from a barrier of infrastructure to one of cognition and capability. Social media functions as an equalizer solely when users are equipped with the critical literacy to navigate complex digital ecosystems. These findings highlight the value of community-based interventions as a practical policy tool for reducing health disparities, shifting the focus from solely content regulation to also include demand-side skill development.

Background In pre-post designs, analysis of covariance (ANCOVA) is a standard technique to detect the treatment effect with a continuous variable measured at baseline and follow-up. For measurements subject to a high degree of variability, it may be advisable to repeat the pre-treatment and/or follow-up assessments. In general, repeating the follow-up measurements is more advantageous than repeating the pre-treatment measurements, while the latter can still be valuable and improve efficiency in clinical trials. Methods In this article, we report investigations of using multiple pre-treatment and post-treatment measurements in randomized clinical trials. We consider the sample size formula for ANCOVA under general correlation structures with the pre-treatment mean included as the covariate and the mean follow-up value included as the response. We propose an optimal experimental design of multiple pre-post allocations under a specified constraint, that is, given the total number of pre-post treatment visits. The optimal number of the pre-treatment measurements is derived. For non-linear models, closed-form formulas for sample size/power calculations are generally unavailable, but we conduct Monte Carlo simulation studies instead. Results Theoretical formulas and simulation studies show the benefits of repeating the pre-treatment measurements in pre-post randomized studies. The optimal pre-post allocation derived from the ANCOVA extends well to binary measurements in simulation studies, using logistic regression and generalized estimating equations (GEE). Conclusions Repeating baselines and follow-up assessments is a valuable and efficient technique in pre-post design. The proposed optimal pre-post allocation designs can minimize the sample size, i.e., achieve maximum power.

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset.

In July 2024, a major rainfall-induced landslide disaster occurred in Zixing county, Hunan Province, triggering more than 4000 landslides with a total area exceeding 21 km2. The scale of this hazard underscores a critical need for long-term stability assessment of the affected slopes. While previous studies have primarily used optical remote sensing to map landslide distributions, quantitative evaluation of post-failure movement dynamics remains limited. This study developed an integrated monitoring framework that combines time-series SBAS-InSAR displacement measurements (using Sentinel-1 data from August 2024 to September 2025) with deep learning-based optical interpretation, rainfall analysis, and geological data. Our approach enables the quantitative, region-scale stability assessment of the Zixing landslide cluster one year after the initial event. Experimental results reveal sustained surface displacement with rates ranging from −30 to 30 mm/year, and localized displacements exceeding 40 mm/year. Notably, over 48% of the mapped landslides are classified as active or critically active, indicating widespread, ongoing instability. Correlation analysis further establishes precipitation as a key driver of accelerated movement. Beyond the Zixing case, this work provides a transferable methodology for assessing long-term post-disaster landslide behavior, offering direct value for regional hazard management and early-warning systems.