Temporal Heterogeneity Research Articles

The dentate gyrus of the hippocampus develops through complex cellular migrations and differentiations, which have been primarily characterized using genetic lineage tracing approaches. Through systematic application of in utero electroporation across developmental stages, we found that labeling was most effective at embryonic day 12.5 (E12.5), as earlier stages resulted in embryonic lethality while later stages showed markedly reduced efficiency. To directly compare these cells with genetically-defined progenitor populations, we established a novel dual-visualization system, combining electroporation with transgenic reporter mice (Gfap-GFP). This approach revealed marked differences in developmental trajectories: Gfap-GFP+ cells maintain undifferentiated neural stem/progenitor characteristics with persistent Sox2 expression, while E12.5-labeled cells predominantly differentiate into Prox1-positive granule cells by E18.5. These early-labeled cells display characteristic migration patterns, with 60.9% differentiating into Prox1-positive granule cells compared to only 22.8% of Gfap-GFP+ cells (P < 0.001), exclusively following an outside-in trajectory to establish the initial framework of the granule cell layer, without reaching the tertiary dentate matrix. In contrast, Gfap-GFP+ cells populate the tertiary dentate matrix and serve as a sustained progenitor reservoir. Molecular marker analysis reveals sequential expression of Sox2, Tbr2, and Prox1, demonstrating progressive differentiation during migration. Our findings identify an early-differentiating subset of dentate progenitors with accelerated neurogenic progression, revealing previously unrecognized temporal and functional heterogeneity in dentate development. This study demonstrates how stage-specific in utero electroporation can complement genetic approaches by uncovering progenitor subsets with rapid differentiation kinetics, providing new insights into the cellular diversity that shapes hippocampal structure and function.

Background: The Warburg effect, historically regarded as a hallmark of cancer metabolism, is often interpreted as a universal metabolic feature of tumor cells. However, accumulating experimental evidence challenges this paradigm, revealing a more nuanced and context-dependent metabolic landscape. Methods: In this study, we present a hybrid multiscale model of tumor metabolism that integrates cellular and environmental dynamics to explore the emergence of metabolic phenotypes under varying conditions of stress. Our model combines a reduced yet mechanistically informed description of intracellular metabolism with an agent-based framework that captures spatial and temporal heterogeneity across tumor tissue. Each cell is represented as an autonomous agent whose behavior is shaped by local concentrations of key diffusive species—oxygen, glucose, lactate, and protons—and governed by internal metabolic states, gene expression levels, and environmental feedback. Building on our previous work, we extend existing metabolic models to include the reversible transport of lactate and the regulatory role of acidity in glycolytic flux. Results: Simulations under different environmental perturbations—such as oxygen oscillations, acidic shocks, and glucose deprivation—demonstrate that the Warburg effect is neither universal nor static. Instead, metabolic phenotypes emerge dynamically from the interplay between a cell’s history and its local microenvironment, without requiring genetic alterations. Conclusions: Our findings suggest that tumor metabolic behavior is better understood as a continuum of adaptive states shaped by thermodynamic and enzymatic constraints. This systems-level perspective offers new insights into metabolic plasticity and may inform therapeutic strategies targeting the tumor microenvironment rather than intrinsic cellular properties alone.

The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an ensemble machine learning approach that simultaneously considers different time periods of the week. This study reveals the impacts of four dimensions of BE variables on UV at different time periods at the scale of the community life circle. The four well-performing base models are integrated to reveal the mechanism of differential effects of BE variables on UV under different time periods in the old city of Nanjing through Shapley addition explanation. The findings reveal that (1) the seven most important built environment variables existed in different time periods of the week: floor area ratio, service POI density, remote sensing ecological index, POI mixability, average building height, fractional vegetation cover, and maximum building area; (2) The nonlinear and threshold effects of the built environment factors differed across time periods of the week; (3) There is a dominant interaction between built environment variables at different time periods of the week. This study can provide guidance for the refined management of complex urban systems.

Temporal heterogeneity in traffic crash delays: causal inference from multi-scale time factors and sample-wise structural decomposition.

Pleural mesothelioma (PM) is characterised by marked heterogeneity, both clinically in terms of survival and response to treatment, and in terms of histology and molecular status. Development of novel therapies, stratified treatment pathways and a better understanding of resistance mechanisms are urgently needed. This requires an in-depth understanding of the multiple sources of heterogeneity affecting tumour cells and the tumour microenvironment. This review, which synthesises the key studies available in the literature, provides a detailed description of the current state of the art regarding heterogeneity in PM. After an overview of the general molecular landscape and a summary of heterogeneity between patients (intertumour heterogeneity), we review sources of variation within an individual patient's tumour (intratumour heterogeneity). This section covers both the local heterogeneity classically reported in other tumours and the anatomical heterogeneity, which is more profound in PM given the large pleural surface area over which the disease develops and progresses. We also synthesise the available data on changes that develop over time (temporal heterogeneity). The various cellular and molecular sources of heterogeneity are also highlighted throughout each section, including histological variations, genomic and non-genomic molecular changes and variations in tumour, stromal and immune compartments. The solid understanding of intertumour heterogeneity recently achieved has highlighted diverse molecular and cellular landscapes. However, this knowledge has yet to be effectively translated into clinical practice (eg, diagnostic classification, treatment allocation, trial design). Further research is needed for a better comprehension of intratumour heterogeneity, including characterisation of local tumour-immune-stromal interactions, including those based on anatomical position on the pleural surface. Efforts should also include dissection of intratumour heterogeneity in patients treated by immunotherapy, development of preclinical models that adequately capture heterogeneity and the investigation of clonality and tumour evolution over time.

Multiple sclerosis (MS) is a chronic neurodegenerative disease with significant temporal and regional heterogeneity. While earlier studies described the burden before 2019, recent shifts influenced by socioeconomic development, healthcare access, and environmental exposures remain unclear. Using the Global Burden of Disease database, we analyzed recent MS trends, assessed interactions of gender, age, and sociodemographic index (SDI), and projected future dynamics. Based on the Global Burden of Disease database from 1990 to 2021, this study collected epidemiological data of 2795 patients with MS from 204 countries and regions, covering core indicators such as incidence, prevalence, mortality, and disability-adjusted life years (DALYs). Data underwent standardized processing and WHO age-standardization. Long-term trends were analyzed with Joinpoint regression; regional variation by SDI and Moran I; gender and age distributions with chi-square; and inequalities by concentration indices and Lorenz curves. A Bayesian hierarchical model with Markov chain Monte Carlo was applied to forecast trends to 2050. From 1990 to 2021, global MS cases rose markedly (incidence +49.9%, prevalence +87.9%), but age-standardized incidence and prevalence remained stable (−3.5% and −0.4%), indicating population growth as the main driver. High-SDI regions showed rising incidence (Western Europe +27.8%, Latin America +31.6%), while low-SDI regions had sharp increases in case numbers but limited standardized rate changes. Mortality and DALYs decreased globally (−12.8% and −11.0%) but rose in resource-limited areas (mortality +110.9% in Central Latin America, DALYs +315% in West Africa). Women consistently bore a higher burden, with gender gaps most evident in low-income regions (315% higher mortality in West African women). MS prevalence strongly correlated with SDI (r = 0.6975, P < .001). Projections suggest gradual incidence growth with declining mortality and DALYs by 2050. Inequality analysis showed persistent deviations from equilibrium. Despite improved survival, high-SDI regions face the challenge of managing aging patients, while low-SDI regions suffer from high mortality and limited resources. The disproportionate burden in women, especially in low-income settings, underscores the need for tailored, equity-focused strategies.

Abstract Accurately predicting terrestrial ecosystem responses to climate change over long‐timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimization, focusing on carbon‐water‐vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterizing errors due to observation biases and model‐data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple data sets, and including the spin‐up and historical period in the assimilation window. We outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation data sets, harness ML advances, and enhance the predictive capabilities of LSMs.

Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences. However, existing models often face limitations in generalization, struggling with zero-shot prediction on unseen regions and cities, as well as diminished long-term accuracy. This is primarily due to the inherent challenges in handling the spatial and temporal heterogeneity of traffic data, coupled with the significant distribution shift across time and space. In this work, we aim to unlock new possibilities for building versatile, resilient and adaptive spatio-temporal foundation models for traffic prediction. We introduce OpenCity, a foundation model that captures underlying spatio-temporal patterns from diverse data, facilitating zero-shot generalization across urban environments. OpenCity integrates Transformers with graph neural networks to capture complex spatio-temporal dependencies in traffic data. By pre-training OpenCity on large-scale, heterogeneous traffic data from web platforms, we enable the model to learn rich, generalizable representations that can be seamlessly applied to a wide range of traffic forecasting scenarios. Experiments show OpenCity excels in zero-shot prediction and exhibits scaling laws, highlighting its potential as a universal one-for-all traffic prediction solution adaptable to new urban contexts with minimal overhead. Source codes are available at: https://github.com/HKUDS/OpenCity

Swine influenza virus (SIV) is a highly contagious respiratory pathogen that causes significant economic losses in the swine industry and poses a potential health risk to humans. This study investigated the genetic diversity and evolution of the H1N1 subtype SIV across different regions of China over the past four decades. Using 959 whole-genome sequences collected between 1977 and 2020 from public databases such as GenBank and the Global Initiative on Sharing Avian Influenza Data (GISAID), we systematically analyzed the epidemiology, phylogenetics, genotypes, and molecular characteristics of the H1N1 subtype SIV. The results revealed marked temporal and geographic heterogeneity in virus distribution, with six major lineages and 25 distinct genotypes identified. The Eurasian avian-like (EA) lineage predominated, reflecting its adaptive advantage in swine populations. Genotypic turnover was evident over time, with certain genotypes (e.g., genotype 2 and genotype 3) exhibiting molecular features associated with adaptation to human hosts, thereby elevating the risk of cross-species transmission and potential pandemics. Amino acid site analysis further identified mutations favoring human-like receptor binding, mammalian adaptation, and antigenic variation. These findings highlight the ongoing evolution of H1N1 subtype SIV in China and underscore the necessity for continuous surveillance and enhanced biosecurity measures in the swine industry to mitigate future pandemic threats.

Harmful Algal Blooms (HABs) cause significant ecological damage and public health issues in freshwater reservoirs globally. While in situ sampling is essential for monitoring HABs, it is often costly and time-consuming. Remote sensing offers a rapid and cost-effective alternative for detecting HABs. This study integrates Sentinel-2 multispectral imagery with in-situ observations to monitor HABs in Dongzhang Reservoir, Fujian, China. We analyzed the Normalized Difference Vegetation Index and Normalized Difference Chlorophyll Index (NDCI) to monitor chlorophyll-a concentrations in this dynamic water body. In situ water samples were collected at three locations to determine the phytoplankton density, and these counts were correlated with spectral indices. Our findings indicate that the Dongzhang Reservoir experiences high algal biomass during spring and summer, as evidenced by seasonal variations in spectral index values. The NDCI, which leverages the red edge wavelength, provided the most accurate identification of chlorophyll-a. Linear regression analysis revealed strong correlations between phytoplankton density and remote sensing values in spring, summer, and autumn of 2022, with R2 values of 0.81, 0.74, and 0.62, respectively. Two key factors driving HAB proliferation in 2022 were total phosphorus levels and water surface temperatures, which were positively correlated with phytoplankton density (R2 = 0.83 for total phosphorus and R2 = 0.81 for water temperature). In 2023, total phosphorus remained the main factor, with an R2 of 0.75. HAB events were more severe in 2022, affecting 34.41% of the reservoir area in May, compared with 22.15% in 2023. This study demonstrated that Sentinel-2 imagery captures greater spatial and temporal heterogeneity of algal blooms than traditional in situ samples do, highlighting the potential of remote sensing as a crucial tool for monitoring algal bloom dynamics in reservoirs and other water bodies. Complementing in situ samples with Sentinel-2 images, owing to their high temporal resolution, can increase monitoring efforts. The implementation of appropriate management strategies can mitigate HABs in Dongzhang Reservoir.

IntroductionThis study investigates how the digital economy drives rural revitalization in China through technological progress. With the rapid growth of China’s digital economy, understanding its role in promoting rural revitalization has become increasingly important. This study explores the mechanisms through which digital development contributes to rural revitalization, emphasizing the mediating role of agricultural technological progress.MethodsUsing panel data from 281 prefecture-level and above cities from 2011 to 2021, both static and dynamic econometric models are constructed to evaluate the effects of digital economy development. Agricultural technological progress—including general progress, frontier innovation, and pure technical efficiency—is incorporated as both a moderating and threshold variable.ResultsThe results indicate that the digital economy significantly promotes rural revitalization, exhibiting clear spatial and temporal heterogeneity. Among the three dimensions of technological progress, improvements in pure technical efficiency exert the strongest positive effect. Furthermore, threshold analysis shows that as technical efficiency improves, the positive influence of the digital economy intensifies, whereas the effect weakens when general or frontier technological advancement reaches higher levels.DiscussionThese findings highlight the crucial role of agricultural technological efficiency in amplifying the benefits of digitalization for rural development. To maximize the digital dividends, policymakers should focus on strengthening rural digital infrastructure, promoting the integration of digital tools with agricultural technologies, and designing differentiated policy interventions tailored to the stages of technological development.

Clathrin-mediated endocytosis (CME) is a vital cellular process that exhibits spatial and temporal heterogeneity in its dynamics, traditionally studied through labor-intensive time-lapse microscopy and single particle tracking. To overcome the limitations posed by phototoxicity, temporal undersampling, and computational complexity, we introduce a deep learning framework that infers CME dynamics from single fluorescence images. Using a modified U-Net architecture, our model predicts spatial maps of the standard deviation (SD) of clathrin coat growth rates-an established metric of CME activity-directly from static frames of AP2-eGFP-labeled cells. The network was trained on paired image data and SDmaps derived from experimentally tracked endocytic events. The model accurately recapitulates dynamic features such as front-rear asymmetry in migrating cells and responses to membrane tension alterations, demonstrating strong agreement with traditional time-lapse-derived metrics. This approach eliminates the need for trajectory reconstruction or prolonged imaging, enabling real-time, non-invasive assessment of endocytic dynamics across diverse biological contexts. Our results highlight the potential of deep learning to extract dynamic biophysical information from static imaging data and establish a scalable methodology for probing CME and related subcellular processes.

Objective: Axillary lymph node (ALN) status in breast cancer is pivotal for guiding treatment and determining prognosis. The study aimed to explore the feasibility and efficacy of a radiomics model using voxel-wise dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-intensity-curve (TIC) profile maps to predict ALN metastasis in breast cancer. Methods: A total of 615 breast cancer patients who underwent preoperative DCE-MRI from October 2018 to February 2024 were retrospectively enrolled and randomly allocated into training (n = 430) and testing (n = 185) sets (7:3 ratio). Based on wash-in rate, wash-out enhancement, and wash-out stability, each voxel within manually segmented 3D lesions that were categorized into 1 of 19 TIC subtypes from the DCE-MRI images. Three feature sets were derived: composition ratio (type-19), radiomics features of TIC subtypes (type-19-radiomics), and radiomics features of third-phase DCE-MRI (phase-3-radiomics). Student's t-test and the least absolute shrinkage and selection operator (LASSO) was used to select features. Four models (type-19, type-19-radiomics, type-19-combined, and phase-3-radiomics) were constructed by a support vector machine (SVM) to predict ALN status. Model performance was assessed using sensitivity, specificity, accuracy, F1 score, and area under the curve (AUC). Results: The type-19-combined model significantly outperformed the phase-3-radiomics model (AUC = 0.779 vs. 0.698, p < 0.001; 0.674 vs. 0.559) and the type-19 model (AUC = 0.779 vs. 0.541, p < 0.001; 0.674 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. The type-19-radiomics showed significantly better performance than the phase-3-radiomics model (AUC = 0.764 vs. 0.698, p = 0.002; 0.657 vs. 0.559, p = 0.037) and type-19 model (AUC = 0. 764 vs. 0.541, p < 0.001; 0.657 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. Among four models, the type-19-combined model achieved the highest AUC (0.779, 0.674) in cross-validation and testing sets. Conclusions: Radiomics analysis of voxel-wise DCE-MRI TIC profile maps, simultaneously quantifying temporal and spatial hemodynamic heterogeneity, provides an effective, noninvasive method for predicting ALN metastasis in breast cancer.

IntroductionAccurate prediction of treatment response and prognosis in breast cancer patients is critical to advance personalized medicine and optimize therapeutic decision-making. Within the context of AI-enabled healthcare, there remains a pressing need to develop robust, interpretable models that can account for the temporal complexity and heterogeneity inherent in longitudinal patient data.MethodsThis study proposes a novel framework designed to model patient-specific treatment trajectories using a dynamics-aware, deep sequence learning architecture. Aligned with the core themes of computational prognostics and precision therapy, our method addresses the challenges posed by variable patient responses, missing clinical records, and complex pharmacological interactions. Existing approaches, including conventional supervised learning and static classification models, often fall short in capturing the underlying temporal dependencies, multimodal data fusion, and counterfactual reasoning necessary for real-world clinical deployment. These limitations hinder generalizability, especially in scenarios where treatment outcomes are delayed or weakly annotated. In contrast, our approach integrates recurrent modeling, attention mechanisms, and uncertainty quantification to better capture the evolving nature of patient health trajectories. Moreover, we incorporate domain-informed regularization techniques and causal inference modules to improve interpretability and clinical relevance.Results and DiscussionBy learning temporal dynamics in a personalized manner, the proposed model enhances predictive performance while remaining sensitive to patient-specific variations and therapeutic regimens. Through extensive validation on real-world breast cancer cohorts, we demonstrate that our framework not only outperforms existing baselines but also provides actionable insights that can inform adaptive treatment planning and risk stratification.

Immune evasion is a hallmark of cancer development and poses an important impediment to the effectiveness of immune checkpoint inhibitors (ICIs). Cancer cells take advantage of heterogeneous intrinsic and extrinsic pathways to circumvent immune detection, such as metabolic remodeling (e.g., increased glycolysis, activation of IDO1), genomic mutation (e.g., JAK/STAT, β-catenin), and epigenetic suppression of immune-regulatory genes. Concurrently, TME promotes immune suppression through Tregs, MDSCs, TAMs, and fibroblast-mediated extracellular matrix remodeling. Hypoxia and cytokine dysregulation also undermine antigen presentation and T-cell functionality. These immunoevasion strategies form the foundation of both native (innate) and adaptive resistance to ICIs, while recent evidence places emphasis on microbiota composition being able to modify therapeutic response. The PD-1/PD-L1 pathway remains the focus of ICI therapy, but PD-L1 expression is limited by spatial, temporal, and technical heterogeneity. Beyond PD-L1, integrated biomarker approaches including tumor mutational burden (TMB), microsatellite instability (MSI), IFN-γ gene signatures, and circulating tumor DNA (ctDNA) have arisen to further inform patient stratification. Emerging therapeutic technologies—e.g., dual checkpoint blockade, engineered cytokines, personalized neoantigen vaccines, and adoptive T cell therapy (CAR-T, TCR-T)—are designed to overcome resistance and maximize clinical efficacy. Integration of multi-omics and AI-based models provides additional precision in the tailoring of immunotherapy. This review integrates existing knowledge of immune escape and resistance, highlighting dynamic biomarker development and combinatorial approaches for next-generation personalized cancer immunotherapy.

Small lakes (average depth 4.7–7 m) serve as important natural reservoirs, providing habitats for numerous species and offering significant potential for ecosystem services. The lakes of the Lori Plateau occupy an intermediate position between classical lakes and wetland ecosystems. However, due to anthropogenic factors and climate stressors, they face a serious risk of degradation. One of the most alarming manifestations of this process is the recurring algal blooms, often accompanied by hypoxia in deep layers. Analyzing the spatial and temporal heterogeneity of phytoplankton distribution in these lakes is crucial for assessing their ecological state and managing water quality. Climate change plays a major role in influencing phytoplankton dynamics, as it extends the growing season, leading to an earlier onset of stratification and subsequent algal blooms in spring (Winder, Sommer, 2012; Berge et al, 2020). The first detailed phytoplankton monitoring, conducted in the lakes Urasar, Konsky (Horse) Liman, and Prozrachny (Clear) Liman in 2023–2024, revealed a broad diversity of species belonging to key groups: Cyanobacteria, Bacillariophyta, Chlorophyta, and Euglenophyta and . In total, over 120 species were identified, 85% of which serve as biomarkers of eutrophication and indicators of various saprobity degrees. Among them, cyanobacteria and euglenophytes dominated the phytoplankton community. Using advanced scanning electron microscopy (SEM), 24 previously unrecorded species of the genus Trachelomonas were identified in Armenia’s algal flora for the first time. Saprobity indices ranged between 1.6 and 1.3 (with the highest values recorded in 2023), while the Shannon index was 1.8–2.3 (peaking in 2024). Algal biomass reached 4.3–10.5 g/m³, characterizing these water bodies as mesoeutrophic. For sustainable water resource management in the region it is essential to consider the biodiversity of autotrophic organisms and the dynamics of their development. Regular monitoring and conservation strategies for the small lakes of the Lori Plateau will not only help preserve aquatic ecosystems, but also contribute to climate adaptation efforts, mitigating environmental change impacts on regional water systems.

This study investigates whether carbon trading improves air quality by using high-frequency daily data from China’s seven pilot carbon markets and real-time air pollution readings from 206 surrounding monitoring stations. We construct alternative indicators of carbon market liquidity and assess their effects on the Air Quality Index (AQI) using a fixed effects model. The findings show that poor liquidity—reflected by higher illiquidity ratios—is significantly associated with higher AQI values, suggesting that illiquid markets weaken incentives for carbon emission reductions and air quality benefits. Spatial heterogeneity suggests that pilot carbon markets with more efficient allocation generate greater air quality benefits. Temporal heterogeneity indicates that the effect is most pronounced in summer, moderate in winter and spring, and weakest in autumn. Furthermore, the observed improvement in air quality is primarily attributable to reductions in PM2.5. This study offers new evidence on the short-term environmental impacts of carbon trading and provide actionable insights for improving market design and regional policy alignment.

Tissue factor (TF) has emerged as a promising target for the diagnosis and treatment of hepatocellular carcinoma (HCC). However, there is limited data available on TF-related PET imaging for longitudinal monitoring of the pathophysiological changes during HCC formation. Herein, we aimed to explore the TF-expression feature and compare a novel TF-targeted PET probe with 18F-FDG through longitudinal imaging in diethylnitrosamine (DEN)-induced rat HCC. Wistar rats were randomly allocated to three groups receiving DEN: intragastric administration (10 mg/kg, i.g.); intragastric administration (80 mg/kg, i.g.); intraperitoneal administration (80 mg/kg, i.p.) for 12 consecutive weeks. Longitudinal Al18F-NOTA-tTF and 18F-FDG (∼3.7 MBq) PET/CT were performed at weeks 7, 15, and 21. Terminal histopathological evaluation included hematoxylin-eosin staining and immunohistochemical analysis of Ki-67, AFP, and TF expression. In DEN-induced HCC rats (80 mg/kg, i.p. and i.g.), longitudinal 18F-FDG PET/CT revealed temporal metabolic progression, with no detectable lesions at week 7 but distinct hypermetabolic foci appearing by week 15 (4 lesions/group) and increasing to 12 lesions/group at week 21. Quantitative analysis demonstrated significant SUVmax increases in both models from weeks 15 to 21 (i.p.: 1.53 ± 0.54 to 1.87 ± 0.53; i.g.: 1.34 ± 0.37 to 1.77 ± 1.08), though notable heterogeneity emerged with 33.3% of i.p. lesions showing paradoxical SUVmax reduction. In contrast, the Al18F-NOTA-tTF demonstrated superior early detection, identifying lesions as early as week 7 (2 lesions/group) with progressive detection rates reaching 5 lesions by week 15 and 21 (i.p.)/12 (i.g.) lesions at week 21. From weeks 15 to 21, the Al18F-NOTA-tTF exhibited consistently increasing SUVmax (i.p.: 0.56 ± 0.23 to 0.76 ± 0.30; i.g.: 0.47 ± 0.13 to 0.58 ± 0.13), with sustained uptake in 90% (i.p.) and 83% (i.g.) of lesions. Immunohistochemistry demonstrated overexpression of Ki-67, AFP, and TF in PET-positive regions. The longitudinal 18F-FDG PET imaging exhibits significant spatial, temporal, and quantitative heterogeneity in detecting lesions during hepatocarcinogenesis, while TF-targeted PET imaging shows superior capabilities in early and advanced HCC detection. This complementary information highlights the complexity of hepatocarcinogenesis and supports the potential clinical translation of TF-targeted PET for improved HCC detection and characterization.

Invasive species can alter community composition and ecosystem functioning. In the subtidal rocky shores of Arraial do Cabo Bay, southeastern Brazil, the invasive coral Tubastraea spp. has established populations, raising concerns about long-term impacts on native benthic communities. This study investigates temporal shifts in β-diversity across 44 fixed plots containing Tubastraea spp., monitored over 383 days. Underwater photographic surveys and multivariate analyses identified nine distinct benthic community types, each forming mosaic structures of sessile organisms. Temporal β-diversity analyses revealed that only the group characterized by Tubastraea, crustose calcareous algae and the zoantharian Palythoa caribaeorum showed significant differences between species gains and losses over time, suggesting temporal-scale dependency. Key contributors to community dissimilarity included P. caribaeorum, crustose calcareous algae, turf, the sponge genus Darwinella, and Tubastraea. This study highlights the importance of considering both spatial and temporal heterogeneity when assessing the ecological impact of marine invasive species. Our findings underscore the need for multi-scale monitoring to fully understand the dynamics of tropical subtidal ecosystems under biological invasion. While numerous studies report a correlation between Tubastraea abundance and shifts in ecological diversity, this relationship may be weak, as critical drivers such as the complexity of community organization are rarely accounted for.

ABSTRACTIt is known that phenological changes (i.e., behavioural and sometimes morphological and physiological traits that repeat annually) influence the wildlife gut microbiota. However, it remains largely unknown to what extent geographic variation could modulate the effect that phenology has on wildlife microbiota. Here, we analysed the feather microbiota in adult Magellanic penguins (Spheniscus magellanicus) and the microbes from samples of nest soil and seawater, using Illumina MiSeq sequencing of bacterial 16S rRNA (V3–V4 variable region) at three phenological stages (courtship, egg‐laying and chick‐rearing) across five nesting colonies with environmental heterogeneity, in the Magellan Strait, Chile. We found over 67,000 ASVs, most belonging to the bacterial family Moraxellaceae. We detected seven core bacterial genera despite geographic and phenological variation; among them, Psychrobacter had the highest relative abundance. Phenology affected feather microbiota alpha diversity and the relative abundance of selected genera in a colony‐specific fashion. Still, it consistently affected feather and nest soil microbial composition, highlighting a phenological microbial succession pattern in penguin feathers and nest soil. From the geographic perspective, we detected three main results in the penguin feather microbiota: (1) alpha diversity was higher in the largest colonies, although only in the chick‐rearing stage; (2) a significant distance‐decay pattern, in the egg‐laying and chick‐rearing stages; and (3) compositional clusters that follow the geographic location of each colony. Our results highlight how temporal and environmental heterogeneity shape microbial traits in marine wildlife.