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Background Human endogenous retroviruses (HERVs) harbor accessory proteins that influence cellular processes and have been linked to a wide variety of diseases, including cancer. This study investigates locus-specific HERV expression and its association with gene dysregulation in hepatocellular carcinoma (HCC), a highly prevalent and deadly form of liver cancer worldwide. Methods We analyzed RNASeq data from 424 HCC samples from The Cancer Genome Atlas (TCGA), which comprised 371 tumor and 50 matched normal tissues from a total of 371 hepatocellular carcinoma participants. We employed Telescope to identify and quantify HERV expression across the total RNA sequencing data. Results The majority of differentially expressed HERVs exhibited reduced expression in tumor tissue (166 downregulated vs. 50 upregulated), suggesting a potential functional role of HERV expression patterns in shaping the pathophysiological landscape of HCC. Specifically, the suppression of HERV-H family members, which are known to regulate cellular differentiation, may contribute to tumor dedifferentiation, increased plasticity, and enhanced metastatic potential. This loss of differentiation control and increased adaptability may play a critical role in driving the progression of liver cancer. Discussion Our study highlights a significant association of HERV expression with HCC, highlighting the differential regulation of specific HERV families in tumor tissue. For example, HERVH and ERVLE families showed consistent downregulation in tumor samples, while HERVE and HERV9 were more commonly upregulated. These shifts may reflect underlying changes in transcriptional regulation or chromatin structure between normal and malignant tissues. Rather than indicating a singular functional role, the observed expression patterns likely reflect a multifaceted relationship between HERVs and tumor biology. Further studies will be needed to determine whether these expression differences contribute to, or result from, tumor progression and to explore their potential as biomarkers or therapeutic targets. 

Dialog systems (e.g., chatbots) have been widely studied, yet related research that leverages artificial intelligence (AI) and natural language processing (NLP) is constantly evolving. These systems have typically been developed to interact with humans in the form of speech, visual, or text conversation. As humans continue to adopt dialog systems for various objectives, there is a need to involve humans in every facet of the dialog development life cycle for synergistic augmentation of both the humans and the dialog system actors in real-world settings. We provide a holistic literature survey on the recent advancements inhuman-centered dialog systems(HCDS). Specifically, we provide background context surrounding the recent advancements in machine learning-based dialog systems and human-centered AI. We then bridge the gap between the two AI sub-fields and organize the research works on HCDS under three major categories (i.e., Human-Chatbot Collaboration, Human-Chatbot Alignment, Human-Centered Chatbot Design & Governance). In addition, we discuss the applicability and accessibility of the HCDS implementations through benchmark datasets, application scenarios, and downstream NLP tasks. 

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. 

In this essay, we address the intersection of trust and modularity in organization design. We argue that, while advanced digital technologies favor more modular organizational arrangements, contemporary trust scholarship has largely failed to adopt the network-based approach that is necessary to understand relationships in such settings. Addressing this void, the article introduces a framework that differentiates between and elaborates on within- and between-module trust dynamics. Our argument offers insights into the challenges and opportunities presented by modular designs, particularly regarding the concerns they raise surrounding trust pluralism and organizational coherence. The discussion extends to practical implications for organizational designers, suggesting strategies for navigating trust in modular organizations. We also point to recursive effects of trust on the emergence of modular structures. By advancing theoretical discussions on modularity and trust, our work serves as a foundation for future theoretical and empirical research aimed at refining the strategies organizations can employ to leverage modularity while fostering a trustworthy environment. 

We devise a novel formulation and propose the concept of modal participation factors to nonlinear dynamical systems. The original definition of modal participation factors (or simply participation factors) provides a simple yet effective metric. It finds use in theory and practice, quantifying the interplay between states and modes of oscillation in a linear time-invariant (LTI) system. In this paper, with the Koopman operator framework, we present the results of participation factors for nonlinear dynamical systems with an asymptotically stable equilibrium point or limit cycle. We show that participation factors are defined for the entire domain of attraction, beyond the vicinity of an attractor, where the original definition of participation factors for LTI systems is a special case. Finally, we develop a numerical method to estimate participation factors using time series data from the underlying nonlinear dynamical system. The numerical method can be implemented by leveraging a well-established numerical scheme in the Koopman operator framework called dynamic mode decomposition. 

Summary Object-oriented data analysis is a fascinating and evolving field in modern statistical science, with the potential to make significant contributions to biomedical applications. This statistical framework facilitates the development of new methods to analyze complex data objects that capture more information than traditional clinical biomarkers. This paper applies the object-oriented framework to analyze physical activity levels, measured by accelerometers, as response objects in a regression model. Unlike traditional summary metrics, we utilize a recently proposed representation of physical activity data as a distributional object, providing a more nuanced and complete profile of individual energy expenditure across all ranges of monitoring intensity. A novel hybrid Fréchet regression model is proposed and applied to US population accelerometer data from National Health and Nutrition Examination Survey (NHANES) 2011 to 2014. The semi-parametric nature of the model allows for the inclusion of nonlinear effects for critical variables, such as age, which are biologically known to have subtle impacts on physical activity. Simultaneously, the inclusion of linear effects preserves interpretability for other variables, particularly categorical covariates such as ethnicity and sex. The results obtained are valuable from a public health perspective and could lead to new strategies for optimizing physical activity interventions in specific American subpopulations.