搜索结果：obscure

Coastal development is rapidly expanding worldwide, and seismic anomalies observed in shallow-water environments provide valuable information for drilling hazard assessment and understanding geological processes. However, these anomalies are typically small in scale and are often obscured by dominant signals, making their interpretation challenging. In this study, we apply an integrated workflow that combines ultra-high-resolution (UHR) 3D seismic acquisition with localized rank reduction-based 3D diffraction imaging to enhance the detection and analysis of seismic anomalies in shallow-water settings. The UHR acquisition improves spatial resolution for detecting small-scale subsurface features, while the diffraction imaging suppresses dominant reflection energy and enhances diffraction responses associated with localized subsurface heterogeneities. The proposed workflow was applied to a data that is obtained from a study site in Yeongil Bay, Korea, producing a high-resolution 3D diffraction cube. In the resulting diffraction cube, dominant linear signals such as reflections, secondary bubbles, and multiples were suppressed, allowing channel and polka-dot anomalies to be more clearly delineated and facilitating the analysis of small-scale anomalies that are often obscured by dominant signals in conventional seismic data. These results demonstrate that the integration of UHR 3D seismic acquisition and diffraction imaging provides an effective approach for the characterization of small-scale seismic anomalies in shallow coastal environments.

State-supported research funding agencies are critical to the scientific enterprise. However, it remains unclear how funding agencies cooperate with academic communities to realize common scientific goals. Here, we present a fully digital archive assembled by the National Human Genome Research Institute (NHGRI), focusing on the nascent stages of "genomics" as a scientific field and the everyday workings of the Human Genome Project and subsequent major genomics projects. We identify early events behind the conception of genome-wide association studies, clarify hitherto obscured factors around funding decisions, and how NHGRI and academics outside NHGRI ensured continuity in technical expertise across projects. The computational models we developed correctly recapitulate how academic experts and NHGRI increased adoption of genomics by jointly deciding which organisms' genomes to sequence. Taken together, these findings reveal how a funding agency contributed to scientific innovation in a nascent field of science by repeatedly cooperating with the broader scientific community.

To understand the molecularly obscure pre-diagnostic phase of lung cancer, we mapped the temporal evolution of the plasma proteome for new biological insights and improved risk prediction. Leveraging the UK Biobank prospective cohort, we analyzed 2,921 plasma proteins from 37,759 participants, including 342 incident lung cancer cases identified over a median follow-up of 11.7&#xa0;years. We employed time-stratified Cox models, locally weighted scatterplot smoothing (LOESS) trajectory modeling, and hierarchical clustering to characterize protein dynamics relative to the time of diagnosis. A multi-algorithm machine learning pipeline was used to develop a predictive signature, and two-sample Mendelian randomization was performed to infer causal relationships. We identified 340 risk-associated proteins showing significant temporal heterogeneity. Long-term risk (>5 years pre-diagnosis) was linked to proteins like CEACAM5, indicating early dysregulation of cell adhesion. Imminent risk (<5 years) was marked by a surge in inflammatory proteins like IL6. These dynamics were resolved into four distinct trajectory patterns, creating a molecular timeline of carcinogenesis. A machine learning-derived 28-protein signature, integrated with clinical factors and Polygenic Risk Score (PRS), achieved outstanding predictive performance (AUC&#xa0;=&#xa0;0.830). Mendelian randomization also suggested a causal role for some proteins of 340 risk-associated proteins in lung cancer development. Our findings establish that lung cancer evolves through a dynamic sequence of protein changes. This provides a new model for understanding pre-diagnostic disease, and our 28-protein signature is a powerful tool for precision screening to identify individuals with active disease progression.

Cold preservation is a critical logistical step in liver transplantation but induces ischemia-reperfusion injury (IRI), a key driver of early graft dysfunction. While bulk tissue assays capture global damage, they obscure the cell-type-specific transcriptional programs engaged during hypothermic storage. We utilized a multicellular human liver-on-chip model comprising Patient-Derived Organoids (PDOs), hepatic stellate cells (HSCs), liver sinusoidal endothelial cells (LSECs), and macrophages. Chips were exposed to 24-h static cold storage using either the clinical standard University of Wisconsin (UW) solution or a hyperbranched polyglycerol (HPG)-based formulation, followed by normothermic reperfusion. Single-cell RNA sequencing (scRNA-seq) was performed to map transcriptional trajectories across the preservation-reperfusion axis. We identified candidate solution-dependent transcriptional differences across cell types. PDOs from UW-preserved chips showed comparatively higher mean expression of inflammatory and oxidative stress-associated transcripts (IFI27, SAA1, HMOX1) and mitochondrially-encoded genes (MT-ND5) relative to HPG-preserved samples, which retained comparatively higher expression of homeostatic epithelial markers (EPCAM, KRT18). HSCs and LSECs in the UW group showed comparatively elevated expression of fibrosis-associated (COL1A1, TAGLN) and endothelial adhesion (ICAM1) transcripts. Ligand-receptor interaction modelling identified candidate inflammatory communication axes, including chemokine signaling interactions (CXCL1, CCL20) between macrophages and epithelial compartments, with higher predicted activity under UW preservation. This study provides an exploratory, high-resolution map of cell-type-specific transcriptional patterns associated with hypothermic preservation in a liver-on-chip model. Our findings suggest that preservation solution chemistry is associated with distinct transcriptional signatures spanning stress response, mitochondrial, and intercellular signaling pathways. Transcriptional patterns in HPG-preserved cells were consistent with comparatively attenuated injury responses; however, these observations are hypothesis-generating and require independent biological replication and functional validation, including metabolic flux assays and ROS production measurements before conclusions regarding mitochondrial protection or clinical preservation efficacy can be drawn.

The electrocatalytic ethanol oxidation reaction is bottlenecked by inefficient C&#x2500;C bond cleavage. This challenge is epitomized at metal-oxide heterointerfaces, where the active site identity and cleavage mechanism remain obscured. Here, we decoded this by atomically programming model PdO&#x2500;Pt3Pd heterointerfaces. Through 18O isotopic labeling, we identify the interfacial lattice oxygen (OInt) in Pd2+&#x2500;OInt&#x2500;Pdalloy motif as the direct oxygen donor for C&#x2500;C cleavage. The interfacial built-in electric field activates OInt as a nucleophilic scalpel by upshifting its p-band center, resulting in an ultralow cleavage barrier of 0.47 eV. Beyond a single site, we demonstrate that the interface functions as a reaction-network architect. It creates a dominant OInt-mediated "non-CO" C1 pathway at the PdO&#x2500;Pt3Pd heterointerface while re-engineering the traditional "CO" pathway on the adjacent Pt3Pd domain via threefold optimization: minimizing *CO source, suppressing acetate formation and ensuring rapid *CO removal. This dual-path integration yields breakthrough performance with a mass activity of 9.09 A mgmetal -1 and a C1-pathway Faradaic efficiency of 75.6%. This work reports a paradigm shift from a passive "scavenger" model to an active "initial-attack and system-orchestration" mechanism, redefining heterointerfaces as atomically programmable reaction-network architects. This paradigm offers a blueprint for mastering complex reaction networks, extending the frontier of rational catalyst design.

Cerebral palsy (CP) is a neurodevelopmental disorder primarily affecting motor functioning and often accompanied by cognitive impairments. Although intellectual profiles in children with CP are known to be heterogeneous, standard intelligence assessments often rely on a single global IQ score, which may obscure important intra-individual differences and limit clinical usefulness. This study aimed to explore intellectual functioning in children with unilateral and bilateral spastic cerebral palsy using a detailed multi-level analysis to examine how motor impairments may influence performance on WISC-IV indices and subtests. A cross-sectional study was conducted on 48 children aged 6-15 years with spastic cerebral palsy (24 unilateral, 24 bilateral). Inclusion required at least one WISC-IV index score above 80. Intellectual functioning was assessed through Full Scale IQ, four cognitive indices (verbal comprehension, perceptual reasoning, working memory, processing speed), and individual subtests. Participants were classified by cerebral palsy type and gross motor function level. Descriptive and comparative statistics identified significant group differences. Intellectual profiles showed marked heterogeneity. Over half of the children showed marked discrepancies among WISC-IV index scores, making the Full Scale IQ difficult to interpret as a representation of their overall cognitive functioning. Working Memory and Processing Speed were the most frequently impaired indices. Processing Speed deficits were consistent across CP types and motor levels, while Working Memory impairments were more pronounced in children with bilateral CP. Perceptual and verbal reasoning differences were more evident in bilateral cases. Greater motor impairment correlated with lower Perceptual Reasoning and Processing Speed scores, highlighting the influence of motor functioning on performance. Detailed, multi-level assessments of WISC-IV performance provides a clearer understanding of cognitive functioning in children with CP. Considering indices and subtests in relation to motor constraints helps distinguish true cognitive abilities from motor-related limitations, supporting accurate diagnosis and targeted interventions.

Elevated blood sugar is a major modifiable risk factor for cardiovascular disease and premature mortality. In India, national estimates obscure substantial small-scale disparities, particularly among women. This study presents a national district-level geospatial analysis of elevated blood sugar among Indian women using data from the National Family Health Survey 2019-21 (NFHS-5), METHODS: We analyzed blood sugar data from 724,115 women aged&#x2009;&#x2265;&#x2009;15 years across 707 districts of India (NFHS-5, 2019-21) using random blood glucose&#x2009;&#x2265;&#x2009;140&#xa0;mg/dL or current diabetes medication as definition of elevated blood glucose. Socio-demographic, nutritional, maternal health and behavioural variables were considered. Ordinary least squares (OLS) regression assessed global associations, while geographically weighted regression (GWR) and multi-scale GWR (MGWR) captured spatial heterogeneity. District-level prevalence ranged from approximately 6% in parts of north-eastern India to over 30% in Punjab, Kerala and Himachal Pradesh. Overweight/obesity (&#x3b2;&#x2009;=&#x2009;0.56), female literacy (&#x3b2;&#x2009;=&#x2009;0.12), early marriage (&#x3b2;&#x2009;=&#x2009;0.03) was consistently associated with higher prevalence, with notable geographic variation. MGWR substantially improved model performance (adjusted R&#xb2; = 0.83 compared with 0.65 for OLS), revealing that obesity had the strongest effect in southern districts, while early marriage was more influential in central and northern regions. Unexpected positive associations with iron-folic acid supplementation and female literacy likely reflect complex interactions related to the nutrition transition and differential detection of metabolic risk. Hyperglycemia among Indian women is highly clustered and driven by spatially heterogeneous factors. National averages conceal more than threefold differences across districts. Targeted, district-specific interventions addressing obesity, early marriage and structural inequities are essential to achieve the World Health Organization (WHO) targets for reducing the burden of non-communicable diseases (NCDs). Integrating geospatial analytics into surveillance can enhance precision public health-that is, the use of data-driven, location-specific strategies to improve population health outcomes in low- and middle-income countries.

Objective&#xD;Transcranial Magnetic Stimulation combined with electroencephalography (TMS-EEG) enables non-invasive assessment of cortical excitability and connectivity through TMS-evoked potentials (TEPs), but it is highly susceptible to artifacts that can obscure genuine neural activity. Among these, capacitive artifacts at the electrode-scalp interface are particularly challenging as they can dominate the post-stimulus signal with sharp voltage deflections and long-lasting drifts. This study evaluated the effectiveness of windowed detrending methods for correcting capacitive artifacts, especially when online hardware mitigation is insufficient.&#xD;&#xD;Approach&#xD;We systematically compared non-windowed detrending models against windowed approaches that separately model the rise (charging) and decay (discharging) phases of the artifact. We applied these methods to multiple datasets acquired across two centers using different hardware configurations. Performance was benchmarked against a cleaning strategy based on Independent Component Analysis (ICA), spanning mild to extreme capacitive artifact conditions.&#xD;&#xD;Main results&#xD;ICA effectively corrected mild artifacts but was inadequate in moderate-to-severe cases, often suppressing physiological components or leaving substantial residual contamination. In contrast, model-based detrending, particularly windowed methods, robustly corrected extreme artifacts. Temporal segmentation improved parameter estimation and removal of nonlinear trends. Among the tested methods, windowed polynomial detrending showed a slight advantage, likely due to its greater flexibility in capturing complex artifact shapes.&#xD;&#xD;Significance&#xD;Although optimal TMS-compatible hardware and rigorous online prevention remain essential, windowed detrending provides a robust offline correction strategy when severe capacitive artifacts persist. This approach improves data quality and supports more reliable interpretation of TEPs in studies where ideal acquisition conditions cannot be fully achieved.&#xD.

The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearance are complex due to overlap between tumor and non-tumor cells, and modality identification is difficult because tumor features vanish in the later layers of the DL. Effective feature extraction from given MRI is a possible solution to overcome this challenge. Therefore, we develop BrainFusionNet that combines Convolutional Neural Networks (CNNs), Vision Transformers (ViT), and Gated Recurrent Units (GRUs) to extract spatial, contextual, and sequential features from MRI images for improved brain tumor classification. Furthermore, explainable AI such as SHAP, LIME, and Grad-CAM are integrated to visualise and highlight image regions that contribute to BrainFusionNet's decision-making process. The proposed BrainFusionNet model is evaluated on two publicly available MRI datasets. K-fold validation suggests 98% accuracy on both datasets. The model was compared with the six state-of-the-art (SOTA) CNNs and transfer learning. Among the SOTA CNNs, DenseNet121 and VGG16 achieved the highest accuracy of 96%. The novelty of BrainFusionNet is that the hybrid model effectively extracts local and global features from MRI images, even in small-scale tumor regions and small tumor sizes. The model has a balanced sequential CNN architecture to capture low-level and deeper-layer features; a customized ViT that captures local features, stabilizes gradient flow, and reduces the risk of vanishing gradients during MRI image training. The CNN and ViT outputs are fed into a GRU for final classification. Furthermore, we analyze pixel intensities to determine whether MRI image quality affects image classification. Our findings are very novel in image interpretation, as we found that the distribution of pixel intensities in MRI images affects DL performance.

Revealing the nanoscale structural evolution of electrocatalysts under realistic acidic oxygen evolution reaction (OER) conditions remains a major challenge. Here, we report the tracking of&#xa0;the evolution of the same individual iridium (Ir) nanocatalysts during prolonged acidic OER by developing identical-location transmission electron microscopy (IL&#x2011;TEM). The Ir nanocatalysts dispersed on a TEM grid serving as a working electrode are examined before and after OER operation. We find that the deposition of Au nanoparticles and the formation of SnO2 nanoclusters occur when a conventional holey carbon-film-supported gold (Au) grid is used at 1.7&#xa0;V versus the reversible hydrogen electrode (VRHE), which obscure the identification of genuine Ir reconstruction. By coating the Au grid with Pt to form a stabilized working electrode, these artifacts are effectively suppressed for up to 2.5&#xa0;h, enabling direct visualization of the nanoscale evolution of Ir nanocatalysts. Control measurements further confirm that the electrochemical response of Ir nanocatalysts on Pt-coated grids is dominated by the Ir catalysts rather than the Pt support. This study demonstrates that IL-TEM provides a practical approach for probing catalyst evolution in harsh, prolonged acidic environments by tracking the same nanocatalysts over extended reaction times, complementing in situ and operando TEM techniques.

Single-cell RNA sequencing has transformed immunological research by enabling high-resolution transcriptional profiling of individual immune cells. Despite its transformative impact, annotating immune cells based solely on transcriptomic data remains challenging. These difficulties arise from biological factors, including gene expression heterogeneity and post-transcriptional regulation, as well as technical limitations that contribute to mismatches between mRNA and protein expression. Such discrepancies may lead to cell misclassification and obscure functional insights, particularly in heterogeneous populations such as peripheral blood mononuclear cells. This review highlights the major challenges in immune cell annotation by detailing the mechanisms underlying mRNA-protein discrepancies, examining both the biological factors and technical artifacts that drive this divergence, and emphasizing their implications for accurate cell classification. A critical overview of current single-cell profiling technologies follows, with evaluation of the respective advantages and limitations of transcriptomic, proteomic, and multimodal approaches. In particular, technologies such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing integrate transcriptomic and proteomic data, addressing the shortcomings of single-modality analyses. Further examination focuses on computational strategies for immune cell annotation, with emphasis on automated methods and bioinformatics frameworks tailored to multi-omics datasets. The unique computational challenges of integrating mRNA and protein data, together with solutions for improved annotation accuracy, are discussed. This review integrates key challenges, technologies, and computational tools, highlighting the need for standardized multimodal profiling of immune cells. Such integration enhances annotation reliability and advances disease understanding and therapy discovery.

Early identification of factors influencing long-term health-related quality of life (HRQoL) after stroke is essential for individualising poststroke support. While different patient characteristics have been shown to be associated with HRQoL, few studies have investigated how these associations evolve over time. The interaction between baseline characteristics at time of stroke and the elapsed time since stroke may reveal divergent recovery trajectories that remain obscured when conditions are assessed without consideration of temporal trends. This cross-sectional single-centre cohort study analysed data from 47 patients with minor ischaemic stroke, drawn from a local aftercare programme. HRQoL was assessed using the EQ-5D-5L index value once within a period of up to 3 years after stroke. Patient characteristics at hospital admission (ie, National Institutes of Health Stroke Scale, modified Rankin Scale, age, sex and recanalisation therapy) were tested for associations with HRQoL using Spearman's rank correlation and Wilcoxon rank-sum tests. Variables significantly associated with HRQoL were entered into multivariate linear regression (MLR), adjusted for time since stroke. Interaction terms were tested to evaluate whether the effect of baseline characteristics varied by time since stroke. Only age at time of stroke showed a significant association with EQ-5D-5L index values. The final MLR model included quadratic age terms and their interaction with time since stroke, explaining 29% of the variance in HRQoL (p<0.001). Based on this model, younger age is associated with increasing HRQoL from stroke onward, whereas older age is associated with decreasing HRQoL. This study indicates that age and time since stroke jointly influence long-term HRQoL after minor stroke. Our findings guide the hypothesis that older individuals may predominantly face progressive HRQoL deterioration after stroke, while younger patients show marked recovery potential. Tailoring aftercare strategies to age-specific recovery profiles could improve the long-term support and outcomes after stroke. DRKS00031333.

The transition from minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC) marks a decisive turning point in lung cancer progression. While the bacterial microbiome is a recognized component of the tumor microenvironment, the specific contribution of the lung mycobiome to this invasive shift remains largely obscure. Using an integrated multi-omics approach, this study maps the fungal ecosystem dynamics across the MIA-to-IAC spectrum. Notably, invasive tissues exhibited a significant elevation in fungal diversity, a finding that stands in sharp contrast to the traditional view of disease-associated microbial loss. We identified Candida albicans as the pivotal biomarker distinguishing invasive from indolent lesions. Crucially, functional integration reveals that this fungal enrichment is not merely an association but appears to actively orchestrate a pro-tumorigenic shift in the host's immune and metabolic landscape. These findings uncover a novel fungal-driven mechanism of tumor invasiveness, suggesting that the lung mycobiome serves as both a hidden driver of progression and a valuable target for early therapeutic intervention.

Rational design of interface passivators for perovskite solar cells is hindered by the entanglement of intrinsic molecular efficacy with extrinsic platform-dependent performance-a confounding factor that obscures true chemical advances. Here, we present a generalizable, interpretable machine learning framework that decouples these effects via an asymptotic saturation model, enabling unbiased discovery of molecules with genuine intrinsic gains. Trained on a curated dataset of 240 experimental entries, our model identifies hydrogen bond acceptor strength and electrostatic potential difference as key descriptors. Guided by these insights, we screened >121 million PubChem compounds using a hierarchical strategy integrating diversity clustering and uncertainty quantification. Five dual-functional candidates (e.g., TDZ-S, TZC-F) are identified, exhibiting superior predicted efficacy (surpassing experimental benchmarks) and high confidence. First-principles calculations confirm strong chemisorption (Eads<-1.7 eV), net electron donation, and optimized interfacial energetics. Crucially, our closed-loop "data-interpretation-screening-verification" pipeline establishes a transferable paradigm for rational materials design, extendable to other optoelectronic interfaces beyond perovskites.

After a crush injury in sciatic nerve fibers, dynamic changes in blood circulation and immune-cell mobilization occur during axonal regeneration. High-resolution visualization under near-physiological conditions is crucial for understanding these mechanisms. Conventional histological techniques introduce perfusion- and dehydration-induced artifacts that obscure circulation. We employed the in vivo cryotechnique (IVCT) to visualize blood flow within sciatic nerve fibers and assess temporal changes during regeneration. In uninjured mice, IVCT preserved native tissue architecture with minimal shrinkage compared to perfusion fixation, with superiority quantitatively shown by fractal analysis. In the crush model, hematoxylin-eosin, Luxol fast blue, and immunohistochemical staining of IVCT-prepared, freeze-substituted sections revealed axonal degeneration and regrowth. The close association between regenerating fibers and vascular structures, along with erythrocyte distribution, indicates a morphological link between nerves and blood vessels. Electrophysiological assessment using compound muscle action potentials and functional recovery measured by the sciatic functional index demonstrated restored nerve function at 28 days, consistent with histology. These findings suggest that IVCT is a useful method for analyzing peripheral nerve regeneration and vascular dynamics, thereby highlighting its potential as a novel approach in peripheral nerve research.

Simultaneous anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and medial collateral ligament (MCL) tears represent a severe form of multiligament knee injury (MLKI). This combination of injuries often results from high-energy trauma and leads to gross instability, impaired joint biomechanics, and long-term degenerative changes if not addressed appropriately. Untreated or improperly treated MLKIs may result in chronic pain, instability, and early onset osteoarthritis. Combined ACL, PCL, and MCL reconstruction is indicated in patients with complete tears of all 3 ligaments confirmed on physical examination, stress radiography, and magnetic resonance imaging, typically presenting with marked instability in both the anterior-posterior plane and to valgus stress. Early surgical intervention is often recommended in active individuals and in cases of knee dislocation, where spontaneous reduction may obscure the extent of concomitant ligament tears. The technique described was used to surgically reconstruct the ACL, PCL, and MCL, and repair the medial meniscus. A central-third bone-patellar tendon-bone autograft was used for the arthroscopic ACL reconstruction, and a double bundle PCL reconstruction used Achilles and tibialis anterior tendon allografts. The medial meniscal repair was also performed arthroscopically. The MCL was reconstructed open using autografts of the semitendinosus and gracilis tendons. Clinical outcomes after simultaneous reconstruction of the ACL, PCL, and MCL have demonstrated significant improvements in subjective stability, return to sports, and pain reduction. Patients with multiligament injuries treated within 3 weeks have been reported to have improved outcomes compared with those who delay surgery. Anatomic reconstruction of concomitant ACL, PCL, and MCL tears is an effective technique for restoring knee stability and function in the setting of multiligament knee trauma. Early diagnosis and treatment are critical for optimal outcomes. Single-stage reconstruction allows for efficient restoration of native biomechanics while minimizing the risk of instability, osteoarthritis, and chronic pain. The author(s) attests that consent has been obtained from any patient(s) appearing in this publication. If the individual may be identifiable, the author(s) has included a statement of release or other written form of approval from the patient(s) with this submission for publication.

Strigolactone (GR24) has been implicated in the plant response to phosphorus (P) stress; nonetheless, the precise molecular mechanisms involved remain obscure. The present study examined the role of GR24 in regulating the internal distribution of P and the mobilization of P from root cell walls in rice. The research involved subjecting rice plants to differential treatments of GR24 and P, followed by a comprehensive analysis employing biochemical and molecular methodologies to elucidate the mechanisms underlying P utilization. The findings indicated that diminished P levels result in a swift escalation of endogenous GR24 accumulation within the roots of rice plants. Moreover, the application of exogenous GR24 notably augmented the tolerance of rice to P deficiency, as evidenced by the enhancement of biomass and a reduction in the root/shoot ratio under P deficient conditions. The application of GR24 also alleviated the decline in P content observed in both roots and shoots. At a mechanistic level, GR24 facilitated the reutilization of P, particularly within the root pectin fraction, thereby increasing the liberation of soluble P. Additionally, GR24 promoted the expression of OsPHT1;2 (OsPT2), a gene implicated in the translocation of P from roots to shoots. Furthermore, it was observed that GR24 stimulated the production of nitric oxide (NO) in P-deficient rice roots. The application of sodium nitroprusside (SNP) further augmented the alleviating effect of GR24 on P deficiency. In summary, these findings underscore the potential of GR24 as a viable strategy to enhance the tolerance of rice to P deficiency and to improve P use efficiency in agricultural applications.

Quantitative analysis of network-like biological molecular architectures, such as cytoskeletal networks, remains a fundamental challenge in super-resolution fluorescence imaging because single-molecule localization microscopy (SMLM) typically produces sparse and discontinuous localization patterns arising from stochastic labeling, incomplete probe occupancy, and structural deformation of biological networks. These factors obscure the underlying connectivity, periodicity, and symmetry, limiting the ability of existing analysis methods to resolve higher-order organization. Here, we report the Classifier of Super-resolution Structural Networks (CLaSSiNet), a conceptually novel computational framework that overcomes these sparsity and heterogeneity constraints. By integrating connectivity, 1D periodicity, and 2D regularity classifiers through newly developed algorithms, CLaSSiNet sensitively captures the organizational signatures of imaged networks to automatically segment and map networks with an unprecedented resolution (&#x223c;256 nm, reaching the diffraction limit of light). CLaSSiNet uniquely resolves four distinct organizational states (1D periodic, 2D polygonal, disordered, and non-network), providing a robust platform for analyzing SMLM data sets regardless of labeling chemistry. Using CLaSSiNet, we achieve the first spatially resolved, quantitative mapping of organizational heterogeneity in the actin-spectrin membrane-associated periodic skeleton (MPS), a conserved cytoskeletal network located underneath the plasma membrane of animal cells. This analysis reveals previously unrecognized organizational principles for these MPS networks, with ordered 1D and 2D networks enriched at cell edges and junctions, while non-network states dominate the cell body. Furthermore, we uncover a mechanical coupling principle wherein actin stress fibers bias the symmetry and orientation of nearby spectrin lattices, indicating bidirectional coordination between contractile actin bundles and periodic MPS networks. Comparative analysis across diverse cell types highlights the cell-specific "tuning" of these supramolecular design rules. Broadly, CLaSSiNet establishes a principled computational framework for dissecting the nanoscale design rules of complex molecular networks, offering a robust methodology applicable to the wider study of hierarchical biological and bioinspired architectures.

Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including heart rate variability (HRV), supporting analysis of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability. This study aims to compare aggregate and individual-level analyses of DHT data for pregnancy-related conditions, using the comprehensive BUMP (Better Understanding the Metamorphosis of Pregnancy) dataset to highlight the importance of individual variability and data heterogeneity. We analyzed wearable and self-reported data from 256 participants enrolled in the BUMP study (January 2021 to May 2022), including HRV, sleep, and fatigue measured via Oura Rings and smartphone surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch t tests, with interindividual variability flagged based on high-variability thresholds. Substantial interindividual variability was observed across all features. Only 4.76% (12/256) of participants exhibited an HRV inflection at the aggregate week-33 inflection point, with a coefficient of variation of 14.24%. The median value of the gestational week in individual fatigue troughs was 23 (IQR 8; range 8-38) weeks, differing from aggregate estimates. Distributional comparisons showed no statistically significant differences in individual-level model fit (R&#xb2;) by pregnancy complications or age (P values ranging from .06 to .99 across all model fit comparisons). Case studies further highlighted both intraindividual and interindividual differences, emphasizing the importance of considering external factors, such as adverse events and severe symptoms. Our findings show that aggregate wearable data often fail to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women's experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care.