Fine-scale spatial dynamics within functional brain networks manifest as high spatial-frequency variations that conventional independent component analysis (ICA) methods fail to capture. These subtle changes may carry critical information about transient connectivity and disordered brain function. We developed NeuroMark-DyFICA (dynamic frequency-informed ICA), a novel framework to enhance detection of spatiotemporal variability in fMRI data. It integrates three stages: dynamic NeuroMark ICA across sliding windows to estimate time-varying, spatially constrained networks; high-pass spatial filtering to emphasize fine-scale spatial features; and group-level ICA to extract refined dynamic components with subject-specific mixing weights. Unlike prior NeuroMark applications or conventional dynamic ICA, NeuroMark-DyFICA establishes a reproducible latent space of high-frequency dynamics, uniquely capturing transient, fine-scale reconfigurations of network topography. Validation using a controlled 2D simulation demonstrated reliable detection of subtle spatial shifts mimicking pathology, which conventional ICA failed to recover. Applying to resting-state fMRI from schizophrenia patients and healthy controls, multiple networks were estimated. We highlight six representative systems (thalamus, auditory, visual/fusiform, middle frontal, default mode, cerebellum). Results revealed two complementary abnormalities in schizophrenia: an imbalance between inactive and hyper-engaged states and altered convergence among dynamic states. NeuroMark-DyFICA reveals fine-grained spatiotemporal disruptions in brain networks, offering mechanistic insights and potential biomarkers for psychiatric disorders. Brain network dynamics are most often studied as changes in the expression or interaction of large-scale, canonical patterns over time. While this perspective has been highly productive, it implicitly emphasizes low spatial-frequency variations and may overlook more focal, transient reconfigurations that occur within otherwise stable networks. Neurophysiological and clinical evidence increasingly suggests that cognition- and disease-related alterations can manifest as subtle, localized shifts in network boundaries, internal structure, or spatial coherence—features that reside in the high spatial-frequency domain and are difficult to capture with conventional dynamic approaches. In this study, we introduce NeuroMark-DyFICA (dynamic frequency-informed independent component analysis), a framework designed to capture dynamic, fine-scale spatial variations in functional brain networks while preserving biologically meaningful network structure. The method integrates template-guided independent component analysis with spatial-frequency filtering and group-level decomposition, enabling isolation of transient, high-frequency spatial changes within canonical networks in a fully automated and reproducible manner. Using controlled simulations, we demonstrate that NeuroMark-DyFICA is sensitive to small spatial shifts that conventional static or low-frequency–focused methods often overlook. When applied to resting-state fMRI data from individuals with schizophrenia and healthy controls, the framework reveals abnormal dynamic spatial patterns across auditory, visual, frontal, default-mode, and cerebellar networks, including altered prevalence and coordination of dynamic spatial states. By explicitly targeting fine-scale spatial dynamics, NeuroMark-DyFICA provides a new lens for studying how brain networks reconfigure over time and how these processes are disrupted in neuropsychiatric disorders. This approach offers a principled pathway toward more sensitive biomarkers and advances precision neuroscience by bridging large-scale network organization with localized, clinically relevant variability.
Sleep-disordered breathing (SDB) is common in childhood and is associated with attentional and behavioral impairments despite largely preserved sleep macrostructure and minimal abnormalities in conventional electroencephalographic measures. This discrepancy has contributed to the perception that sleep is relatively preserved in pediatric SDB and has limited understanding of the physiological mechanisms underlying morbidity. To determine whether pediatric SDB is associated with disruption of the regional organization and homeostatic dynamics of slow-wave activity (SWA), a key physiological marker of sleep- dependent neural recovery and development. Cross-sectional study of 62 children aged 4 to 12 years who underwent overnight polysomnography with high-density electroencephalography in a laboratory setting. Participants were recruited from clinical referrals and the community, spanning the full spectrum of SDB severity. SDB severity indexed by hypopnea index (HI), apnea-hypopnea index (AHI), and obstructive apnea index (OAI). Regional electroencephalogram-derived SWA (0.5-4 Hz) topography and exponential decay parameters derived from frontal and posterior cortical regions. The frontal-to-posterior decay- rate ratio was evaluated as a summary measure of regional sleep homeostasis. In children with lower hypopnea index, SWA demonstrated the expected developmental pattern, with posterior predominance in younger children and a progressive shift toward a more balanced anterior-posterior distribution with age. Increasing HI was associated with attenuation or reversal of this spatial organization. Global SWA showed no meaningful association with SDB severity. In contrast, regional frontal and posterior decay parameters were strongly associated with HI (adjusted R² = 0.53; p < 1 × 10⁻⁶) but not OAI (adjusted R² = 0.05; p = .95). The frontal- to-posterior decay-rate ratio showed the strongest association with HI β = 4.15; 95% CI, 3.17- 5.13; p < 1 × 10⁻¹⁰; adjusted R² = 0.55. Pediatric SDB was associated with regional disruption of slow-wave sleep homeostasis rather than global loss of deep sleep. These alterations affected both the spatial organization and temporal dynamics of SWA during a period of active cortical maturation and were not captured by conventional sleep metrics. Regional SWA dynamics may provide a developmentally sensitive marker of physiological disease burden in children with SDB. Question: Does pediatric sleep-disordered breathing disrupt the regional organization and homeostatic dynamics of slow-wave activity during development in ways that are not captured by conventional sleep metrics?Findings: In this cross-sectional study of 62 children across the spectrum of sleep-disordered breathing, hypopnea burden was associated with altered regional organization and overnight dissipation of NREM slow-wave activity (SWA) despite preserved global SWA. A frontal-to- posterior SWA decay-rate ratio was strongly associated with hypopnea severity, whereas global SWA was not.Meaning: Pediatric sleep-disordered breathing may disrupt sleep physiology in a regional, developmentally meaningful manner not captured by conventional polysomnography, suggesting a potential physiological marker of disease burden beyond event counts.
Understanding how the brain gives rise to social cognition has been a key goal of neuroimaging research. Both changes in regional activation as well as functional connectivity have been implicated as potential mechanisms underlying social cognition, but the two have rarely been examined concurrently. Moreover, because the neural processes underlying social cognition are dynamic, developing approaches to capture dynamic changes in regional activity and functional connectivity are critical. Here, we describe a novel analysis approach that captures both regional activity and dynamic functional connectivity simultaneously during a naturalistic, socially focused movie-watching task. We found that both regional activation and functional connectivity were uniquely related to awkwardness, a judgment associated with social faux pas detection and theory of mind. Regional activation within sensorimotor networks was positively associated with awkwardness, whereas activation in the default network was negatively associated. Models including functional connectivity accounted for unique variance beyond models with activity alone. Specifically, dynamic functional connectivity between networks, primarily the frontoparietal control network, was positively associated with awkwardness. Together, these findings suggest that both dynamic regional brain activity and functional connectivity each uniquely contribute to complex and dynamic social judgments. We assessed the relationship between regional activity, functional connectivity, and awkwardness judgments during a video-watching task using fMRI in a cohort of healthy young adults. We used a novel analysis framework to simultaneously disentangle the unique contributions of dynamic regional activity and dynamic functional connectivity in their predictions of awkwardness. We found regional activity associated with awkwardness primarily in sensorimotor, frontoparietal control, and default networks, and functional connectivity associated with awkwardness within and between the visual, dorsal attention, frontoparietal control, and default networks. Finally, hierarchical models demonstrated that modeling functional connectivity significantly explained additional variance beyond modeling the activity of two regions alone, indicating the unique explanatory power offered by dynamic functional connectivity in relation to behavior.
Functional connectivity is often constructed to understand functional organizations in neural systems, where the brain regions and their pairwise interactions are viewed as nodes and edges, respectively. In practice, functional connectivity is commonly estimated via the correlation of pairs of brain regions. One limitation is that the correlation coefficient captures only the pairwise linear dependence relationship between pairs of nodes and may fail to capture complex higher-order relationships. Recently, a novel concept known as edge-centric functional connectivity (eFC) has been introduced to measure interactions between pairs of edges based on the cofluctuation of two nodal time series, offering a new perspective for understanding brain networks. Nevertheless, eFC considers the absolute levels of edge time series, that is, their mean values, in estimation. If the parameter of interest is the covariation between a pair of edges, incorporating mean values of edge time series can introduce bias or deviation, resulting in a skewed estimation. In this manuscript, we propose an alternative approach to estimate the unbiased covariation between pairs of edges, termed centered edge functional connectivity (ceFC), with theoretical foundations. We demonstrate that the proposed estimator is consistent with a sufficient sample size or number of time frames. Additionally, we develop a multiple hypothesis testing framework with a controlled false discovery rate to evaluate the strength of the unbiased covariation among edges. Furthermore, we employ thresholding to obtain a thresholded estimator that has been shown to converge to the true ceFC matrix in high-dimensional settings in which the number of nodes is much larger than the number of samples or time frames. We validate the finite sample performance of the proposed methods via numerical studies and a data application using the Midnight Scan Club dataset. Functional connectivity typically examines the correlations between pairs of brain nodes, which only capture pairwise linear dependence. Recently, the edge-centric functional connectivity (eFC) method has shifted the focus toward the relationships between pairs of edges. While promising, this current method can introduce statistical biases that skew our understanding of these higher-order interactions. In this study, we propose centered edge functional connectivity (ceFC), a framework designed to provide an unbiased view of how brain edges covary. By establishing a rigorous statistical foundation and an inference framework to identify significant interactions, our method ensures reliable results even with a limited sample size. We show that our approach can help identify more stable overlapping brain community structures, providing a new perspective on brain organization.
In robotics research, each subdomain presents a distinct set of challenges, and any framework designed for a given domain must effectively address these complexities. However, a single application within that domain may not fully capture the breadth of challenges inherent to it. To enable systematic and comprehensive evaluation, the robotics community has developed standardized problem scenarios and associated performance metrics, commonly referred to as benchmarks, which collectively represent the diverse challenges arising across applications. In this work, we evaluate our task-motion Planning (TMP) framework on five benchmarks proposed by the TMP community. We begin by briefly describing our iterative deepening AND/OR graph-based TMP planner. Subsequently, we assess its performance across these benchmarks, each designed to capture different aspects of the challenges in TMP. The evaluation demonstrates that the proposed planner successfully solves all five benchmarks, thereby indicating that our framework constitutes a robust and effective solution for TMP.
Reaching the Landsberg limit for radiative energy conversion requires controlling not only spectra but also the direction in which radiative entropy is transported. We develop a finite-stage nonreciprocal thermal-circulator framework for positive illumination (solar conversion) and negative illumination (radiative-cooling work extraction). The ideal theory is expressed in terms of open-system exergy and a minimal port-network picture: a three-port circulator with a cold termination acts as a two-port thermal isolator, and cascaded isolators enforce directed adjacency between effective photon reservoirs. In response to the central practical limitation of nonreciprocal radiative cooling, emphasized recently by Liu et al. we recast the problem as a benchmark map rather than a claim of immediate device-level cooling enhancement. The reported work output values (in unit of W m-2) are hemispherical-equivalent upper envelopes; experimentally relevant output is reduced by captured free-space etendue, coupling efficiency, finite isolation ratio, finite termination temperature, atmospheric transmission, and out-of-window loss. We therefore introduce explicit sensitivity factors for isolation, termination, and captured etendue and ask how good a nonreciprocal system must be before the finite-stage advantage remains measurable. Finite-stage calculations give temperature and wavelength targets for 3-5 stages, while the practical benchmark identifies the isolation, termination, and coupling requirements needed to make those targets consequential. This formulation links ideal Landsberg-type limits to testable design requirements for nonreciprocal thermal photonics.
Aerosol liquid water content (ALWC) plays an important role in climate and public health by influencing aerosol formation, chemical composition, and toxicity. However, ALWC remains sparsely measured and poorly constrained across space and time, despite its large variability. In this study, we derived a high-resolution (1 km × 1 km, daily) ALWC dataset for the contiguous US from 2000 to 2019. The dataset was generated by training machine learning (ML) models on outputs from a chemical transport model (GEOS-Chem) to capture the thermodynamic relationships between ALWC and relevant predictors, then applying these relationships to high-resolution, biased-corrected input datasets. Compared with GEOS-Chem simulations, the ML-based dataset better captures daily variations and spatial heterogeneity in ALWC. The predicted ALWC levels are highest in the Midwest US and lowest in the Western US, largely driven by regional differences in PM2.5 concentration, chemical composition, temperature, and relative humidity. Over the study period, ALWC declined significantly across most regions, driven primarily by the reduction in sulfate. We further demonstrate that ALWC provides a physically meaningful constraint for interpreting variability in water-soluble iron, a health-relevant fraction of aerosol metals, highlighting the potential value of this dataset for future studies of aerosol toxicity and epidemiological exposure.
Propane/propylene splitting is among the most energy- and capital-intensive separations in the petrochemical industry, yet it is indispensable for upgrading mixed C3 cuts to polymer-grade propylene. Because propane and propylene exhibit nearly overlapping physicochemical properties, conventional cryogenic distillation requires very large columns, high reflux ratios, and substantial thermal duties. Adsorption-based separations using porous solids offer a compelling non-cryogenic alternative, especially when the adsorbent is propane-selective (inverse-selective). Propane is retained as the impurity and high-purity propylene can be delivered directly as the raffinate, potentially simplifying process flowsheets and reducing regeneration penalties associated with product-capture schemes. This Review summarizes recent advances, primarily from 2019 to early 2026, in propane-selective porous materials for inverse propane/propylene separation, with metal-organic frameworks (MOFs) as the central focus and porous carbons, hydrogen-bonded organic frameworks (HOFs), and covalent organic frameworks (COFs) discussed as complementary families, while earlier landmark studies such as ZIF-8 are included where necessary for historical context. We highlight how pore-surface polarity/hydrophobicity, ultramicropore confinement, cooperative weak interactions, and framework dynamics can invert the conventional olefin preference and enhance propane capture within practical operating windows. Emphasis is placed on application-relevant evaluation, including gas adsorption, fixed-bed breakthrough behavior, working capacity, regenerability, tolerance to humidity and trace impurities, cycling durability, and prospects for scalable shaping and process integration.
Persistent immune activation and inflammation contribute to non-AIDS comorbidities in people living with HIV (PLWH) despite long-term virological suppression. Given the central role of the gut in immune homeostasis, we investigated whether mucosal and peripheral molecular signatures relate to clinical status, immune recovery, and antiretroviral therapy (ART) class. Sixty-eight virologically suppressed PLWH were stratified by CDC clinical stage, ART regimen (integrase strand transfer inhibitor [INSTI]-based vs. non-INSTI), and CD4/CD8 ratio. Targeted gene expression was assessed by RT-PCR in anorectal mucosal biopsies and peripheral blood mononuclear cells, while epithelial protein expression was evaluated by Western blot in mucosal biopsies. Plasma biomarkers of epithelial injury, microbial translocation, and systemic inflammation were quantified by ELISA. Group comparisons and correlation analyses were performed using non-parametric statistics. Mucosal analyses revealed distinct transcriptional and protein-level alterations associated with clinical and immunological stratifications, whereas peripheral cellular and circulating biomarkers showed limited discriminatory capacity. Notably, specific ART-related patterns emerged at the mucosal level, accompanied by coordinated associations between epithelial junctional features and local immune parameters that were not mirrored systemically. Together, these findings indicate persistent, spatially restricted epithelial-immune remodelling in treated HIV infection, underscoring the importance of direct mucosal assessment to capture residual barrier and immune perturbations under suppressive ART.
Insomnia disorder (ID) is characterized by hyperarousal, yet the relationship between cortical excitability and large-scale network dynamics remains incompletely understood. While fMRI studies indicate network alterations in ID, high-temporal-resolution characterization of these dynamics across the sleep-wake cycle is lacking. Twenty-six patients with ID and 29 healthy controls underwent eyes-closed resting-state OPM-MEG recordings during evening (pre-sleep) and morning (post-awakening) sessions. We analyzed the aperiodic spectral exponent to index cortical excitability and employed microstate analysis to quantify fast network dynamics. A mediation analysis was conducted to explore the associations between electrophysiological features and sleep quality. Compared to controls, patients with ID exhibited a significantly flatter aperiodic power spectrum in the evening, suggesting elevated cortical excitability. Microstate analysis revealed distinct spatiotemporal alterations: (1) an evening-specific increase in the coverage of a putative temporal-limbic microstate, and (2) a sustained elevation of a putative sensorimotor microstate observed in both evening and morning sessions. Mediation analysis indicated that the altered evening limbic microstate dynamics statistically mediated the association between the aperiodic exponent and subjective sleep disturbance measures. These findings indicate that ID involves concurrent disruptions in aperiodic neural activity and microstate temporal organization. The study highlights distinct diurnal profiles for putative sensorimotor and limbic network alterations, suggesting that OPM-MEG can effectively capture the multifaceted electrophysiological signatures of the insomnia phenotype.
Dietary fibers can stimulate endogenous glucagon-like peptide-1 (GLP-1) secretion through microbial fermentation and gut hormone signaling, potentially enhancing satiety and supporting weight management. Given the growing interest in non-pharmacological strategies to complement or support tapering of GLP-1 receptor agonist therapy, a structured overview of the human evidence is needed. A pre-registered scoping review was conducted using PubMed, Scopus and Cochrane Central. Randomized controlled trials in adults assessing circulating GLP-1 concentrations and satiety following supplementation with a single, well-defined dietary fiber were included. Fiber types were categorized based on structural characteristics. Outcomes were summarized qualitatively across fiber categories. In total, 1049 papers were screened and 49 publications comprising 52 studies (total n=1,085 participants; median sample size per study=19) were included. Most studies were acute interventions (71%) and conducted in Western populations. Studies reporting increased GLP-1 showed a non-significant tendency to also report increased satiety (OR = 2.95, 95% CI: 0.87-9.98). Dextrins stood out as one of the few fiber categories showing robust effects on both GLP-1 (4 positive studies) and satiety (5 positive studies). Other fibers, such as β-glucans and mannans, showed more uniform effects on satiety or GLP-1, respectively, but did not consistently affect both outcomes simultaneously. Although these findings identify dextrins as a promising dietary fiber candidate for future research, the evidence remains constrained by small sample sizes, short interventions, and substantial heterogeneity. Longer-term studies in free-living conditions, including periods of GLP-1 receptor agonist tapering, are needed to capture microbiota adaptation and generate robust real-world evidence. https://osf.io/cnw4e/overview.
Health behaviours (e.g. exercise/diet/alcohol/smoking) are a major public health concern and traditional complementary and integrative medicine providers such as traditional acupuncturists could make an important contribution. We explored the support for health behaviour change provided to and experienced by traditional acupuncture patients to understand the ways in which traditional acupuncturists may contribute to their patients making changes to their health behaviours. Longitudinal qualitative research methods were used to capture experiences as they change over time with nine patient‒practitioner dyads (8 female patients and 1 male, aged 23-55, and 3 female traditional acupuncturists, aged 35-63). Consultations were audio-recorded and in-depth qualitative interviews were conducted with patients (twice) and traditional acupuncturists (once). Experiences were explored with primary focus on the patients; data were coded both deductively and inductively and themes were produced using reflexive thematic analysis. Patients at different stages of readiness to change need different types of support-those overwhelmed by symptoms/stress needed help to gain control, patients more ready to change needed help sustaining motivation. Acceptance of lifestyle/behaviour advice was related to establishing a cycle of trust, in practitioner, explanations and treatment. Decisions to enact behaviour change were based partly on changes in feelings/mood as well as reasoned understanding (e.g. of behavioural contribution to symptoms). Patients' response to behaviour change support in traditional acupuncture may vary according to their symptom control and stress. For some, support for symptoms/stress may need addressing before any behaviour change intervention. Therapeutic trust, coherent explanations and positive feeling from treatments were important in supporting change. These findings link to concepts from the trans-theoretical model and stages of change, social cognitive theory, and the common sense model of self-regulation and dual process theories of behaviour.
Intellectual and developmental disability (IDD) is characterized by impairment of cognitive function that results in daily living limitations. Adults with IDD face barriers to meeting basic needs and must navigate multiple public systems to access services. The housing affordability crisis has contributed to a disproportionate overrepresentation of adults with IDD who experience homelessness. Little evidence has articulated the unique systems- and policy-level issues facing homeless adults with IDD. The purpose of this study was to 1) map the systems with which adults with IDD who experience homelessness must interface in order to access needed services and 2) describe the systems- and policy-level issues impacting service delivery and care coordination of this population. This qualitative study interviewed professionals providing disability, homeless, and/or social care services to adults experiencing homelessness between March and June 2021. Data were analyzed using qualitative content analysis. Participants (n = 18) mostly identified as female (n = 11), the mean age was 44 (13.9), and included clinicians (n = 5), case managers (n = 7), allied professionals (n = 5), and outreach specialists (n = 1). Participants identified five unique systems with which individuals at the intersection of IDD and homelessness frequently interact to access care and services. The theme structurally fragmented care for complex needs captures participants' perceptions of systems-level issues, and the theme policy-driven gaps in care encompasses perceptions of policy-level issues. Homeless adults with IDD exist in a nearly perpetual process of cycling through public systems, and health equity cannot be attained without attention to the structural ableism that is embedded throughout. Findings highlight the importance of moving away from a crisis-driven model of care to a cohesive care ecosystem, and future research is needed to develop and test models of care that eliminate system churn so that all individuals are supported.
Biarylitides are a group of bacterial ribosomally synthesized and post-translationally modified peptides (RiPPs) that contain a biaryl bridge formed by dedicated cytochrome P450 enzymes that can introduce different cross-links. The biarylitides are produced via a five-amino-acid precursor peptide, encoded by a minimal 18 bp gene that evades automatic detection. Previous genome mining approaches for biarylitides do not capture their full biosynthetic space. We therefore repurposed a machine learning algorithm to comprehensively chart the biosynthetic space of the biarylitides, including variation of precursor motifs, P450, and additional modifying enzymes, which yielded 277 biarylitide biosynthetic gene clusters (BGCs). We experimentally investigated biaryl formation with previously uninvestigated core peptide motifs, including YWH, YVH, and YWY, and elucidated the nature of these cross-links. This study significantly expands the biarylitide precursor and BGC diversity and provides directions for the systematic exploration of other RiPP families.
Detecting cotton leaf diseases in open-field environments is challenging due to cluttered backgrounds, scale variation, and irregular lesion morphology. Conventional detectors rely on isotropic receptive fields and coupled box-regression losses, which limit their ability to localize elongated lesions with poorly defined boundaries. We present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages. In the backbone, an Anisotropic Morphological Contrast Aggregation module (AMCA) enhances direction-aware representation and lesion-background contrast via re-parameterizable strip convolutions and high-frequency residual extraction. A Dynamic Semantic Boundary Transfer mechanism (DSBT) then captures boundary priors from shallow layers before they are lost to downsampling and injects them into the neck. A Morphological-Spectral Synergistic Feature Pyramid Network (MFS-FPN) preserves these cues during multi-scale fusion through spatial-domain operations compatible with edge hardware. Finally, an Anisotropic Boundary-Decoupled IoU loss (ABD-IoU) independently penalizes each of the four box boundaries and sustains optimization signals in high-IoU regimes via a logarithmic modulation factor. On the self-constructed Complex Cotton Leaf Disease dataset (CCLD; 6,856 images, 6 classes), the method achieves 78.50% mAP@50 and 65.00% mAP@50:95, improving the YOLOv11n baseline by 4.80% and 2.70% with only 2.73 M parameters at 202 FPS. Cross-domain evaluations on PlantDoc and RWD confirm consistent improvements. The framework runs in real time on NVIDIA Jetson edge platforms with INT8 quantization.
Biological models aim to recapitulate the molecular and histologic characteristics, as well as the carcinogenesis of the disease of interest, to enable high-fidelity experimentation and impactful translational development. In high-grade serous carcinoma of ovarian, tubal, or peritoneal origin, there is a need to regularly re-evaluate the experimental models being employed to address research questions. This review aims to provide a resource for better understanding the applications, strengths, and limitations of the available models. This work discusses the origin and carcinogenesis of high-grade serous carcinoma and reviews normal cell-of-origin models, as understanding the cellular origin is critical to experimental efforts focused on pathogenesis, disease progression, and prevention. High-grade serous carcinoma cell lines are discussed, including essential and complementary molecular features, cell line management, and limitations. Next, the review examines advances in organoids, with a specific focus on patient-derived organoids from ascites and single-cell suspensions, organ-on-a-chip platforms, and tissue slice technologies. Finally, given the heterogeneity of epithelial ovarian cancer in general, and high-grade serous carcinoma in particular, and the evolving understanding of the importance of the tumor microenvironment, in vivo models capture organism-level complexity. In this review, we focused on high-grade serous carcinoma. In this context, we review patient-derived xenograft (PDX), syngeneic, and genetically engineered mouse models. Each has its own advantages and disadvantages, and guidance is provided on the optimal use of these in vivo approaches for the study of high-grade serous carcinoma.
Electrochemiluminescence (ECL) offers high analytical sensitivity, low optical background, and spatially confined light emission at electrode interfaces, enabling a large global market for bead-based ECL immunoassays. Existing ECL instrumentation remains bulky, costly, and largely confined to centralized laboratories with limited access to true spatially resolved imaging. Here, we present a compact, modular ECL imaging platform that enables low-light, spatially resolved, and temporally synchronized electrochemical-optical measurements. The system integrates a Raspberry Pi 4 controller, a monochrome OV9281 global-shutter camera, and an EmStat4s potentiostat, coordinated through open-source Python software providing sub-100 ms synchronization and full control of camera parameters (resolution, exposure, gain, frame rate, and focus). The complete system, of approximately 21 × 15 × 21 cm, has a component bill of materials of 2337 €, which could be reduced to a few hundred euros by using a custom potentiostat, making it substantially more affordable than commercial ECL instruments. Platform performance was validated using both homogeneous solution-phase and heterogeneous bead-based [Ru-(bpy)3]2+/tripropylamine (TPrA) assays on carbon screen-printed electrodes. Synchronized cyclic voltammetry and chronoamperometry captured ECL transient behavior, including onset, peak emission, and decay dynamics. Quantitative bead-based assays showed excellent linearity with low-picomolar range limits of detection, while spatially resolved analysis enabled multiplexed measurements on a single electrode via a region-of-interest readout. These results demonstrate the importance of controlled and optimized camera parameters for quantitative ECL imaging and establish this compact, low-cost, and modular platform as a practical alternative for spatially resolved ECL biosensing and multiplex point-of-care applications.
Chronic kidney disease-associated cardiomyopathy (CKD-CM) is a term that captures the spectrum of myocardial disease that begins early in chronic kidney disease (CKD) and progresses as kidney function declines. Historically described as uremic cardiomyopathy, the condition was associated with severe left ventricular hypertrophy and fibrosis in patients with kidney failure. However, functional abnormalities and myocardial injury have been shown to begin much earlier, with diffuse interstitial fibrosis preceding overt hypertrophy or reduced ejection fraction. Fibrosis drives heart failure with preserved ejection fraction (HFpEF)-like physiology and atrial fibrillation and increases arrhythmic risk. In this review, we describe the multiple interacting mechanisms, including abnormal loading, neurohormonal activation, metabolic and inflammatory stress, mineral bone disorder, and microvascular dysfunction. We summarize imaging findings across CKD stages and describe established and emerging therapeutic strategies. A better understanding of CKD-CM pathogenesis is likely to enable early intervention and prevention, with successful outcomes measured by reduced progression to severe cardiomyopathy and lower cardiovascular mortality in CKD.
Trypanosoma lewisi is a blood parasite of wild rodents increasingly recognised as an emerging zoonotic pathogen associated with atypical human trypanosomiasis. However, epidemiological and molecular data from major Indonesian metropolitan areas remain limited. This study investigated the prevalence and molecular identity of T. lewisi in urban wild rats from Surabaya, Indonesia's second-largest city. A total of 100 wild rats, comprising Rattus norvegicus (n = 54) and Rattus tanezumi (n = 46), were captured across five geographic zones between February and August 2025. Infection was assessed by wet-mount examination and Dip Quick-stained blood smears, followed by PCR amplification of the ITS1 region of ribosomal DNA, Sanger sequencing, and Bayesian phylogenetic analysis. Overall microscopy-detectable T. lewisi clade prevalence was 13.0% (13/100; 95% CI: 7.1-21.2%), with infected rats detected in four of five sampling zones. All successfully sequenced samples were placed within the well-supported T. lewisi clade (PP = 1.0) in Bayesian phylogenetic analysis. These findings provide the first molecular evidence of T. lewisi circulating in urban wild rats in Surabaya and demonstrate that synanthropic rodents may serve as important reservoirs of this zoonotic parasite in densely populated Indonesian cities. The study highlights the need to incorporate rodent-borne trypanosomes into One Health surveillance frameworks in urban environments.
Amid global labor shortages, automated harvesting robots are essential for enhancing agricultural productivity, with robust instance segmentation serving as the core vision task. However, existing methods fail to balance high-fidelity boundary delineation and real-time efficiency under severe visual degradations caused by protective fruit bagging and dense canopy occlusions. To resolve these limitations, LRD-Inst, a lightweight and robust dual-branch instance segmentation framework, is introduced for unstructured orchards and resource-constrained edge platforms. The architecture explicitly decouples feature extraction: a spatial pathway utilizes Parallel Hierarchical Enhancement Blocks (PHEB) and Frequency-Decoupled Spatial Pyramids (FDSP) to safeguard high-frequency boundary cues, while a contextual branch embeds a High-frequency Detour State Space Model (HDSSM) to capture long-range global dependencies for obscured targets. A Spatially-Refined Adaptive Fusion (SRAF) module bridges these pathways, optimized via an Area-Stratified Dice (AS-Dice) loss to reinforce small-target geometric fidelity. Extensive experiments on a mixed-apple dataset demonstrate that LRD-Inst achieves a primary Average Precision (AP) of 0.568 with only 3.43 M parameters and 9.12 GFLOPs, outperforming contemporary baselines including the YOLOv8-YOLOv26 families and RTMDet. The model operates at 45.4 FPS on an NVIDIA RTX 3060 GPU. LRD-Inst establishes an optimal equilibrium between accuracy and efficiency, providing a highly deployable solution for autonomous agricultural robotics. The source code is available at https://github.com/ly27253/LRD-Inst.