Biology

Prostate cancer progression is accompanied by dynamic remodeling of malignant cell states, whose metastatic potential and regulatory programs remain poorly defined. Here, we integrated single-cell, spatial, bulk transcriptomic, epigenomic, and experimental data to characterize malignant cell state transitions and identify candidate regulators associated with metastatic progression. A distinct intermediate state exhibits increased copy-number alterations, stemness, proliferation, and MYC activity, together with the highest transcriptional similarity to metastatic tumor cells, and is therefore designated as a metastasis-initiating-like (MIC-like) state. Transcriptional and regulon dynamics along the malignant cell state trajectory highlight FEV as a prominent candidate associated with the MIC-like state, with concordant expression and regulon activity patterns. Functionally, FEV is associated with a hybrid epithelial-mesenchymal phenotype, and its perturbation disrupts this phenotype, accompanied by reduced epithelial and mesenchymal features as well as impaired prostate cancer cell migration and invasion. Beyond this tumor-intrinsic feature, FEV is also associated with reduced cytotoxicity and interferon responses in the tumor microenvironment through its regulation of MIF-family genes. In addition to its potential roles in tumor cell plasticity and immune remodeling, FEV shows a reciprocal association with androgen signaling and distinct expression patterns across treatment response groups, highlighting its potential relevance in therapeutic contexts.

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.

Phenylketonuria (PKU) is an autosomal recessive disorder caused by PAH gene defects and can cause irreversible neurological damage as early as the fetal period; however, non-invasive prenatal monitoring methods are lacking. In this study, Pah+/- female mice were mated with Pah+/-; male mice and wild-type male mice, respectively. Urine samples were collected longitudinally from pregnant mice at 10 time points during gestation from Day 1 to Day 19. Data-independent acquisition (DIA) quantitative proteomics combined with GO/KEGG analysis, WGCNA, and a dual-strategy LASSO-random forest approach was used to screen candidate biomarkers. The results showed that stable differentially expressed proteins between groups were detectable as early as Day 1, and the differences persisted throughout gestation and exhibited temporal dynamics. Permutation tests indicated that the probability of random generation of the differential protein combination was low (0.04-0.06), suggesting that it was not randomly generated. The intersection of the two strategies yielded five proteins, GINM1, PRG4, LY6A, NTM, and YIPF3, and after including GLO1, a total of six candidate biomarkers were obtained. The candidate proteins had interaction or co-expression associations with 14 proteins involved in PKU mechanisms, with GLO1 serving as a network hub. This study found that the maternal urine proteome can non-invasively reflect the systemic response induced by fetal-origin PKU, providing proof of concept and candidate biomarker resources for non-invasive early-pregnancy screening of PKU.

Background: Cardiovascular disease is an important competing cause of death in men with metastatic prostate cancer, and ambient air pollution may increase cardiovascular vulnerability. We evaluated associations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with all-cause, cardiovascular, and prostate cancer-specific mortality, including joint associations with androgen deprivation therapy (ADT). Methods: We analyzed 33,384 men from 11 US state registries diagnosed with metastatic prostate cancer during 2000-2015 and followed through 2018. Residential PM2.5 and NO2 concentrations at diagnosis were assigned using 1-km exposure models. Multivariable Cox models estimated all-cause mortality, and inverse-probability-weighted Cox models assessed cause-specific mortality. Results: Each interquartile-range increase in PM2.5 was associated with higher cardiovascular mortality (HR 1.04, 95% CI 1.02-1.07; p-trend=0.008). Cardiovascular mortality was also higher in the fourth and fifth PM2.5 quintiles versus the first (HR 1.18 and 1.21, respectively). Higher NO2 was associated with prostate cancer-specific mortality in the highest quintile (HR 1.07, 95% CI 1.01-1.13). In joint analyses, ADT-treated men with high versus low PM2.5 exposure had higher cardiovascular mortality (HR 1.19, 95% CI 1.05-1.34), without clear evidence of effect modification (p-interaction=0.051). Conclusions: In men with metastatic prostate cancer, higher PM2.5 exposure was associated with cardiovascular mortality, particularly among ADT-treated patients, supporting further study of environmental exposures and systemic therapy in cardio-oncology.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.

Background: Long-term mortality data among Brazilian police officers and firefighters are scarce. This study investigated the leading causes of death in Brazilian male military police officers and firefighters over three decades, comparing their mortality and mean age at death with those of the male population. Methods: We retrospectively analyzed death records of male military police officers and firefighters from Espirito Santo (ES), Brazil, from 1988 to 2019. Mortality was analyzed across three decades and the standardized mortality ratios (SMRs) for the three leading causes were calculated using the male populations of ES and Brazil as references. Results: Overall, 2,198 deaths were identified, with a mean age at death of 59 {+/-} 18.5 years, compared with 60.5 {+/-} 23.9 years among men in ES and 63.4 {+/-} 23.7 years among Brazilian men. Mean age at death was 36 {+/-} 9 years among active-duty and 68 {+/-} 13 years among inactive personnel. Across the study population, the leading causes of death were circulatory system diseases (32%), external causes (26%) and neoplasms (15%). Circulatory disease mortality was higher in the second and third decades compared with both reference populations, whereas external-cause mortality shifted from higher, in the first two decades, to lower than expected in the last decade. Neoplasm mortality was higher relative to Brazil only in the last decade. Conclusion: Mortality patterns differed by cause and over time. The younger mean age at death among active-duty personnel, together with cause-specific excess mortality, highlights the importance of long-term occupational health surveillance and preventive strategies.

Motivation: Real-time qPCR data are commonly analyzed to obtain a Cq values, which are used to compare the relative abundance of a targeted template. However, improper background subtraction distorts Cq and changing the assigned threshold can alter experimental conclusions. In addition, a Cq values provide no information on the quality of the qPCR for a given sample. Alternatively, global fitting of qPCR data provides agnostic quantification without the use of Cq or thresholds, and the fitting terms provide a useful metric of reaction performance. Current implementations of global fitting are limited to custom in-house programs. Results: qPyCR is comprehensive analysis tool written in Python that performs background signal subtractions, computes a threshold, and reports Cq values in a traditional manner. The program also performs global fitting and reports template abundances along with reaction quality values. The quality values can be used to assign performance standards, which allow researchers to distinguish aberrant reactions from true changes in template abundance. Availability and Implementation: Code and example data are freely available on GitHub (https://github.com/sdmoore-labs/qPyCR) and on Zenodo (https://doi.org/10.5281/zenodo.21115342).

Despite recent evidence of the ability of systemic and organ-specific proteomic aging clocks to predict a large range of age-related diseases, their utility in cancer research remains largely underexplored. In this study, we evaluated the predictive ability of systemic and organ-specific proteomic aging clocks for (i) all-cause mortality, (ii) cancer-related mortality, and (iii) cancer incidence in a sample of Finnish twins (N = 401, mean age at baseline 62.3 years) with Olink proteomic data. Participants were followed for a mean of 10.6 years (SD +- 1.6), with estimates from four systemic and 18 organ-specific proteomic aging clocks calculated at baseline. Cox proportional hazards regression models adjusted for sex and age at baseline identified 15 proteomic aging clocks as significant predictors of all-cause mortality (Ncases = 28) after multiple testing correction (FDR < 0.05), with 11 remaining significant after lifestyle adjustment (Ncases = 27). For several clocks, effect sizes were large, with hazard ratios (HR) approximately doubling per one standard deviation (SD) increase in clock estimates. In predicting cancer-related deaths among participants without a prior cancer diagnosis at baseline (Ncases = 9), only the Immune clock showed a significant positive association after multiple testing correction in basic (HR = 2.94, 95% CI: 1.88-4.59) and lifestyle-adjusted models (HR = 3.31, 95% CI: 1.78-6.17). No associations with cancer incidence (Ncases = 34 basic, Ncases = 31 adjusted) survived multiple testing correction, although several nominal associations were observed. In conclusion, our findings suggest that proteomic aging clocks are strong predictors of all-cause and cancer-specific mortality, independent of lifestyle factors. Proteomic aging clocks may further serve as valuable tools for stratifying cancers by mortality risk.

Salivary secretions of herbivorous insects contain proteins that modulate plant defence and facilitate herbivory. Despite the broad host range of Spodoptera frugiperda, a major agricultural pest, its salivary molecular arsenal involved in interactions with different host plants remains poorly understood. In particular, the salivary proteins induced during feeding on different host plants and their potential interactions with plant immune components remain largely unexplored. To address this gap, we generated salivary gland transcriptomes from larvae reared on an artificial diet as well as larvae fed on tomato or chickpea plants. Comparative transcriptomic analysis identified salivary genes that were differentially expressed in response to plant feeding compared with the artificial diet. The encoded proteins were subsequently analysed using a secretory protein prediction pipeline to identify host-induced putatively secreted proteins with potential effector functions. Candidate salivary effectors were further investigated using large-scale in silico protein-protein interaction analyses with AlphaPulldown to predict their interactions with immune-pathway associated proteins of tomato and chickpea. This analysis identified multiple putative salivary effector candidates whose expression was induced by plants feeding, with distinct candidate repertoires associated with tomato and chickpea. Predicted interactions between these candidates and plant immune-associated proteins further identified potential host targets. Together, these findings suggest that S. frugiperda may adjust its salivary molecular repertoire in response to the host plant and provide a transcriptome-wide framework for validating effector-target interactions and elucidating the molecular mechanisms underlying S. frugiperda-plant interactions.

The circadian clock allows living systems to anticipate environmental daily variations by scheduling cellular mechanisms according to the time of the day. The reciprocal interplay between metabolism and circadian clock is essential for energy homeostasis, and the disruption of metabolic clock inputs contributes to dysfunction of clocks in peripheral tissues. However, the molecular mechanisms through which the feeding/fasting cycle entrains peripheral clocks remain incompletely understood. Here, we investigate experimentally and theoretically the role of AMP-dependent kinase (AMPK), a key fasting sensor, in metabolic regulation of the circadian clock. Using a luciferase reporter driven by the Bmal1 promoter to monitor molecular clock activity in human U2OS cells, and thanks to a signal processing method based on the Hilbert transform to retrieve the instantaneous phase and period of circadian signals, we show that pharmacological activation of AMPK by AICAR significantly elevates Bmal1 promoter activity and markedly lengthens the clock period in a dose-dependent manner. Conversely, the clock period is shortened by the pharmacological inhibition by SR18292 of Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1-alpha). Importantly, the action of AICAR is abolished by FK866, a nicotinamide phosphoribosyltransferase (NAMPT) inhibitor, demonstrating that AMPK-dependent modulation of Bmal1 promoter activity requires NAD+ availability. These experimental results are recapitulated by numerical simulations of our previously published mathematical model, where PGC-1-alpha plays a key role in mediating AMPK activity to the clock. Moreover, the comparison of experimentally measured and theoretically predicted phase response curves, following AMPK activation at different circadian phases, provides an additional validation of the model. By integrating biological experiments with mathematical modeling, our results identify a key mechanism through which metabolic factors can entrain and regulate the circadian clock via NAD+-dependent AMPK-mediated regulation of Bmal1 promoter activity.

Simulation models are widely used to support counterfactual inference in settings where direct experimental validation is difficult or infeasible. However, for phenomenological models calibrated to observational time series, goodness-of-fit does not necessarily establish that the structural assumptions underlying such inferences are uniquely supported by the data. Here, we examine this issue using a published COVID-19 transmission model for Japan that incorporates empirical inputs including human mobility, variant prevalence, and vaccination and has been used to estimate the effects of alternative vaccination scenarios. Using the publicly archived code together with a reconstructed substitute for a restricted-access input file required by the released implementation, we first reproduced the published model behaviour. We then performed two complementary computational stress tests. First, the vaccination-related input was suppressed and the remaining parameters were re-estimated against the original epidemic trajectory. Second, as a complementary test of trajectory-level flexibility, the model was refitted to synthetic target series with qualitatively different temporal structures while retaining the empirical mobility and variant inputs. The model retained substantial fitting capacity in both tests: suppression of the vaccination-related input caused only a limited deterioration in fit to the original trajectory, while qualitatively different synthetic trajectories could also be reproduced through parameter adjustment. Variation in fitted parameters across these conditions was also consistent with compensatory parameter behaviour. These results do not imply that vaccination had no effect or that the original model is invalid. Rather, they provide empirical evidence that, in this case, goodness-of-fit to the observed epidemic trajectory alone does not uniquely support the structural interpretation required for vaccination-related counterfactual inference. The study illustrates the value of reproducible computational stress testing as a complement to, rather than a substitute for, formal identifiability or model-comparison analyses.

Multiview data analysis has become increasingly important for integrating heterogeneous information from multiple data sources. Cooperative learning provides a flexible framework for multiview integration by bridging early and late fusion strategies. However, its use of squared loss makes it sensitive to outliers. In this study, we propose robust cooperative learning based on the soft-IPOD framework. The proposed method introduces an observation-specific outlier absorption vector into the cooperative learning objective, thereby reducing the influence of outlying observations while preserving information sharing across views. The resulting optimization problem can be formulated as a weighted Lasso-type problem using an augmented design matrix and can be naturally extended to more than two data views. Numerical simulations and a real-data application to labor onset prediction demonstrate the predictive performance of the proposed method in the presence of outliers.

Background: Plant defensins are small cysteine-rich antimicrobial peptides. In Arabidopsis thaliana, the inducible Class I gene AtPDF1.2a is widely used as a transcriptional marker of jasmonate/ethylene immunity, whereas the constitutively expressed Class II members have received less functional attention. AtPDF2.2 was compared with AtPDF1.2a during infection of Arabidopsis by the vascular fungal pathogen Verticillium longisporum, the necrotrophic fungus Sclerotinia sclerotiorum and the bacterium Pseudomonas syringae pv. tomato DC3000. Results: Profiling of the AtPDF family showed that AtPDF2.2, together with the Class II genes AtPDF2.3 and AtPDF2.5, was significantly reduced during V. longisporum infection, whereas AtPDF1.2a was induced. A promoter-reporter line showed a corresponding decrease in AtPDF2.2 promoter activity during V. longisporum infection, strongest in infected roots. KD-pdf2.2 plants contained more V. longisporum DNA, whereas OE-PDF2.2 plants contained less. The same reciprocal disease pattern was observed during S. sclerotiorum and Pst DC3000 infection. Histochemical Prom::AtPDF2.2::GUS reporter signal decreased during S. sclerotiorum infection but increased following Pst DC3000 infection. Basal AtPDF2.2 expression was lower in JA and ET-signaling mutants and higher in the SA-deficient sid2 background, while the analyzed promoter lacked the GCC-box that mediates inducible AtPDF1.2a regulation. AtPDF1.2a overexpression reduced symptoms across the three pathosystems, but loss of AtPDF1.2a increased disease only during V. longisporum infection under the conditions tested and did not alter Pst DC3000 DNA abundance. AtPDF2.2 overexpression was not associated with reduced vegetative growth under the conditions tested, whereas AtPDF1.2a overexpression reduced rosette size and leaf number. Conclusions: AtPDF2.2 is a constitutively expressed Class II defensin whose expression level is associated with disease outcome across two fungal pathogens and a bacterium. Its promoter shows tissue-dependent regulation during V. longisporum infection, while histochemical reporter signal decreases during S. sclerotiorum infection and increases during Pst DC3000 infection. This regulatory behavior differs from that of the inducible reference gene AtPDF1.2a. The reciprocal disease phenotypes of the AtPDF2.2 transgenic backgrounds support a protective association that may involve direct antimicrobial activity, modulation of host responses or both. The absence of reduced vegetative growth in OE-PDF2.2 under the conditions tested supports further evaluation of PDF2-type genes in Brassica.

Macrophages, alongside mammary epithelial cells, are major contributors to the innate immune response triggered by bacterial invasion of the mammary gland. To investigate breed-specific immune mechanisms, we stimulated monocyte-derived macrophages from Holstein and Normande cattle with Escherichia coli and Streptococcus uberis, two major mastitis pathogens. We monitored bacterial survival using [methode] and profiled gene expression via RNA-seq. E. coli exhibited significantly higher survival 6 hours post-infection compared to S. uberis (p < 0.05). Moreover, E. coli induced a robust transcriptional response, with 4403 differentially expressed genes (FDR < 0.05, |log2FC| > 1), versus only 813 for S. uberis. Qualitative differences were also observed: genes such as CCL2 and CSF1 were uniquely induced by S. uberis. Additionally, macrophages from Holstein and Normande cattle showed distinct expression profiles: 185, 45, and 174 genes were differentially expressed at steady state or after E. coli or S. uberis stimulation, respectively. These genes were enriched in immune-related pathways. Overall, our results demonstrate a stronger response to E. coli, although S. uberis specifically induces a subset of genes. The observed breed-specific differences may reflect underlying immune mechanisms that could influence disease susceptibility, warranting further functional validation.

Heavy metal contamination of aquatic environments poses persistent risks to ecosystems and human health. Aquatic macrophytes such as Pistia stratiotes are increasingly used for phytoremediation due to their capacity to accumulate contaminants without displaying symptoms of abiotic stress. However, the molecular mechanisms underlying their metal tolerance remain poorly understood. Metallothioneins (MTs) are cysteine-rich proteins involved in metal binding and detoxification in plants. In this study, genome-wide analysis was performed to identify and characterise candidate MT genes in P. stratiotes and to assess their transcriptional response to copper exposure. Six novel MT genes (PsMT2f, PsMT3a-d, and PsMT4) were identified and classified into MT2, MT3, and MT4 subfamilies based on conserved cysteine motifs and domain architecture. Quantitative real-time PCR showed differential and tissue-specific expression of MT genes in roots and leaves following exposure to copper for up to 4 h. These findings provide the first comprehensive characterisation of the MT gene family in P. stratiotes and demonstrate their potential role in copper tolerance, supporting the application of this species in the phytoremediation of metal-contaminated aquatic systems.

The perception of flavor during food consumption is a complex process governed by the dynamic release of aroma compounds, which is influenced by the interaction between the food matrix and human physiology. While mechanistic models have been developed to simulate these phenomena, their practical application has often been limited by the lack of flexible software tools capable of integrating diverse food structures and real-time physiological data. This study addresses this gap by incorporating in vivo mechanistic models for both liquid foods and melting solid matrices in a modular Python-based framework: digimouth. The modeling approach treats the oral, pharyngeal, and nasal cavities as a series of interconnected reactors with mass transfer governed by ordinary differential equations (ODEs). These models explicitly integrate key physiological parameters, including salivary flow, bidirectional respiratory airflow, and discrete events such as mastication and swallowing. For solid foods, the framework incorporates additional mechanisms for matrix dissolution, temperature-dependent melting, and dynamic surface area change. The models were validated using experimental data from 30 subjects who consumed aroma solutions and flavored gelatin gummy discs while monitored by PTR-MS (Proton Transfer Reaction - Mass Spectrometry). The simulations successfully reproduced the main kinetic features and release profiles observed experimentally for both types of matrices. Despite some limitations related to the self-reporting of physiological events and the non-inclusion of initial orthonasal mass transfer in the models, the developed software package proved to be a robust and evolutive tool. It offers a foundation for exploring inter-individual variability and the impact of complex food structures on sensory perception.

In this study, we develop a mathematical model of the hypothalamic pituitary adrenal (HPA) axis and systematically investigate the model complexity required to reproduce observed hormone dynamics. The model is fitted to ACTH and cortisol measurements from five healthy volunteers reported in the literature. We show that simultaneous reproduction of both hormone profiles requires a delay differential equation model with at least five state variables among the model architectures considered. Following fitting the volunteer data, we investigate the response of the model to sustained stress using bifurcation analysis. The coupled system exhibits bifurcations over a range of stress intensities. This analysis reveals bifurcations whose critical stress thresholds depend strongly on the modulatory state of the system. These results provide a mathematical framework for investigating transitions between physiological and dysregulated HPA axis dynamics, which may be related to states of health and disease.

Macaranga tanarius (Euphorbiaceae), a myrmecophilic plant grown in subtropical countries, was identified as a source of Okinawan propolis, a honeybee product, which is glandular trichomes developed on the surface of the fruits. This tissue contains a high amount of characteristic geranylated flavonoids known as nymphaeols (nymphaeol A - C and isonymphaeol B), which exhibit strong antioxidant activity. In this study, we characterized the structure of glandular trichomes of M. tanarius, showing that they are sac-like structures containing drupelet-like aggregates of secretary cells, and a subcuticular cavity was also observed. From a cDNA library, we identified two prenyltransferase (PT) genes involved in biosynthesis of nymphaeols, and a PT gene grouped in a primary metabolic PT. Our biochemical experiments demonstrated that one of the former secondary metabolic PTs (MtPT1) is a B-ring-specific geranyltransferase for eriodictyol to yield nymphaeols B and isonymphaeol B. MtPT1 was unusual for a plant PT, an enzyme that yielded multiple reaction products. The other PT (MtPT3) transferred a geranyl moiety to the A-ring of eriodictyol to form nymphaeol A. Although dimethylallyl diphosphate (DMAPP) was not recognized as its prenyl donor substrate of eriodictyol, giving a B-ring geranylated eriodyctiol as the prenyl acceptor substrate, this enzyme used DMAPP to attach the prenyl moiety to the A-ring and produced nymphaeol C. These data suggest that M. tanarius has evolved its PT function to produce a diverse range of prenylated flavonoids using a limited set of genes.

Cannabis sativa L. cultivation in tropical regions or under indoor conditions requires supplemental lighting to maintain plant yields. Night-break (NB) photoperiods offer a cost-effective alternative to conventional day extension, although genotypic responses have not been clearly described. This study evaluated the effects of continuous and pulsed NB treatments on the growth, phenology, physiology, productivity, and cannabinoid accumulation of three Cannabis sativa genotypes representing two chemotypes. Plants were exposed to a commercial 18-h photoperiod (T1), a continuous 1-h NB (T2), or four 15-min NB pulses (T3) with equal total duration. Continuous NB produced responses comparable to the commercial photoperiod. In contrast, pulsed NB accelerated phenological progression by two to three weeks and reduced final plant height by 15-28%, depending on genotype, without impairing photosynthetic performance or decreasing floral dry biomass. Harvest index increased under both NB treatments, indicating enhanced biomass allocation to reproductive structures, while cannabinoid accumulation remained primarily genotype-dependent with only minor treatment-specific modulation. These findings demonstrate that NB efficacy depends not only on the duration of nocturnal light but also on its temporal distribution, highlighting temporal organization of nocturnal light as a key regulator of photoperiodic responses and a promising strategy for optimizing cannabis cultivation.

Tardigrades, key ecdysozoan organisms, have poorly characterized daily behavioral rhythms linked to their molting (ecdysial) cycle. We continuously tracked the locomotor activity of the model tardigrade Hypsibius exemplaris using infrared videography and deep learning-based automated tracking under three photic regimes: a light/dark cycle, constant darkness following entrainment, and constant darkness without prior entrainment. We identified a robust endogenous circadian-like rhythm in locomotor activity that persisted in constant darkness, independent of photic input. Although the light/dark cycle enhanced the rhythm amplitude and the overall animal activity, it was not required for rhythmicity. Individual phases varied widely, indicating a lack of population-level synchronization. Furthermore, we developed a behavioral classification system based on activity level, which enables categorizing distinct behaviors. Behavioral patterns were modulated by distinct ecdysial stages, with quiescence gating premolt and time-of-day dependent patterns in feeding and wandering. These findings provide evidence consistent with an endogenous, circadian-like timing system in tardigrades that integrates daily activity with molting cycles and establish H. exemplaris as a promising model for studying ecdysozoan chronobiology.