The expansion of nuclear energy necessitates the development of advanced materials capable of managing volatile radioactive byproducts. Herein, we report the rational design and synthesis of a discrete metal-organic cage (MOC1), assembled through coordination interactions between Pd(II) acceptor (M) and tetratopic ligand (L), and its molecular boat-like structure was unambiguously determined by single-crystal X-ray diffraction. This pyridyl-functionalized architecture is specifically engineered for the simultaneous sequestration of molecular iodine (I2) and methyl iodide (CH3I). The nonporous coordination cage exhibits exceptional iodine capture performance, achieving a vapor-phase uptake of 2.54 g g-1 at 75 °C and a solution-phase capacity of 338.8 mg g-1 in n-hexane, driven by synergistic π-iodine and electron-pair interactions. Remarkably, it also displays dynamic vapor-phase adsorption with an uptake of 1.26 g g-1. In addition, the cage effectively captures CH3I vapor, reaching capacities of up to 1.10 g g-1 through a nucleophilic methylation mechanism. Stability studies confirm that MOC1 retains its structural integrity and adsorption efficiency over at least five regeneration cycles. This work establishes an effective approach for designing recyclable metallo-assembly optimized for the remediation of iodine and organic iodides, significantly advancing the safety standards of nuclear power generation.
The persistent burden of malnutrition, including undernutrition, micronutrient deficiencies, and overweight and obesity, disproportionately affects women and children in low-and middle-income countries (LMICs). Rapid urbanization and globalization are reshaping food environments (FEs), yet evidence on how different FE types influence diets and nutrition outcomes among these populations remains fragmented and largely concentrated in upper-middle and high-income settings. This systematic review examines the associations among cultivated, wild, built, and mixed FEs and diet and nutrition outcomes among women, children, and adolescents (<20 years) in low- and lower-middle-income countries (LLMICs). We searched five databases for studies published between 2000 and 2025, identifying 22,431 records. Eligible studies included women, children and adolescents (<20 years); reported at least one FE exposure, and assessed diet, nutrition, and/or food insecurity outcomes. FEs were classified using the Downs et al. typology. A total of 155 studies met inclusion criteria, spanning 17 low-income and 32 lower-middle-income countries. Most studies were conducted in rural settings (n = 114, 74%), and cultivated FEs were the most frequently examined (n = 82, 53%), highlighting underrepresentation of peri-urban and urban settings undergoing rapid food system transitions. Access to wild FEs was consistently linked to higher dietary diversity and micronutrient intake. Some school FEs were associated with obesogenic dietary patterns, while in mixed FE settings, households commonly relied on multiple food sources to meet dietary needs. Across FE types, associations with anthropometric outcomes were modest and variable, likely reflecting slow biological responsiveness of growth indicators and non-dietary factors. Additionally, commonly used diet measures do not capture consumption of ultra-processed foods, limiting the detection of unhealthy dietary shifts during nutrition transitions. This review highlights the need for expanded FE research in underrepresented settings and improved diet assessment tools to capture diverse dietary patterns and inform context-specific interventions. This study was registered in PROSPERO (CRD42024497618).
Efficient NH3 capture remains a significant and unresolved challenge, despite its critical importance for resource utilization and sustainable development. To achieve this goal, adsorbents simultaneously featuring high capacity, superior selectivity, and rapid adsorption kinetics are highly desired. In this work, we developed ionic liquid-loaded metal-organic framework (MOF/IL) composites by integrating highly NH3-affinitive functional IL with anthracene-based MOFs featuring diffusion-facilitated channels. Benefiting from the ordering phase transitions of space-confined IL-NH3, the composite presents a unique "S-shaped NH3 adsorption isotherm" and high volumetric NH3 uptake of 455.81 cm3/cm3 at 298 K and 1 bar. Moreover, the additionally created pathways decorated with a high density of ionic sites localized within the original MOF enable the specific recognition of NH3 molecules while facilitating NH3 transport throughout the composite matrix, thereby ensuring both high selectivity and fast adsorption kinetics. Overall, this work offers a new paradigm and an easy method for designing functional soft porous materials for efficient gas capture.
Prognostic prediction following gastric cancer surgery plays a pivotal role in postoperative management, helping to optimize therapeutic strategies and improve patient survival. Standard clinicopathological indicators, including tumor differentiation and lymph node metastasis, continue to serve as the basis for outcome evaluation; however, they do not adequately represent the host's systemic inflammatory response and immunonutritional status, both of which significantly affect tumor progression and postoperative recovery. Systemic inflammatory markers, such as the Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR), have emerged as reliable, noninvasive prognostic indicators. However, the complex and nonlinear interactions among inflammatory, clinical, and demographic variables pose a limitation for traditional statistical methods. This study proposes a novel deep learning framework that integrates three major components: Gradient-Boosted Decision Tree, Tree-Driven Encoder (TDE), and one-dimensional Convolutional Neural Network (1D-CNN) for postoperative prognostic prediction in gastric cancer. The GBDT module captures intricate dependencies among clinical and inflammatory variables, the TDE transforms tree-based structures into unified binary embeddings, and the 1D-CNN component learns high-level feature representations from these embeddings to predict postoperative prognosis. The model's performance was evaluated using cross-validation and compared with various traditional machine learning algorithms and advanced deep learning architectures for tabular data. Experimental findings demonstrate that the proposed hybrid framework consistently outperforms both traditional and general deep learning models in predicting postoperative prognosis. By combining tree-based feature structuring with deep representation learning, the model effectively captures nonlinear and hierarchical relationships among systemic inflammatory markers and clinicopathological features. This approach achieves high predictive accuracy, robustness, and generalization capability, particularly in identifying high-risk patients characterized by elevated inflammatory activity. Moreover, the model exhibited stable performance across multiple random seeds and data partitions, confirming its reproducibility and reliability under different experimental conditions. This study presents a data-driven and interpretable deep learning framework for postoperative prognostic prediction in gastric cancer. By integrating the strengths of gradient-boosted tree modeling and deep neural representation learning, the proposed model provides a more comprehensive understanding of the interplay among inflammation, nutrition, and tumor biology, supporting personalized treatment planning and evidence-based clinical decision-making. Future research will focus on external validation using independent cohorts, real-time clinical application, and enhancing model explainability to facilitate clinical adoption.
The cardiovascular-kidney-metabolic (CKM) syndrome represents a continuum linking metabolic dysfunction, chronic kidney disease, and cardiovascular disease, in which chronic inflammation plays a central role. However, conventional inflammatory biomarkers may not fully capture local inflammatory processes involved in CKM stage transitions. Pentraxin 3 (PTX3), a long pentraxin produced locally at sites of inflammation, may provide complementary information, yet its association with CKM staging has not been systematically evaluated. In this cross-sectional study, circulating PTX3 levels were measured in 240 adults, including healthy controls (stage 0, S0; n = 60) and individuals with stage 2 (S2; n = 60), stage 3 (S3; n = 60), and stage 4 (S4; n = 60) CKM, classified according to the CKM staging framework. Associations between PTX3 and inflammatory, metabolic, cardiac, and renal biomarkers were assessed using Spearman correlation analysis. Multivariable logistic regression models were constructed within the CKM population (S2-S4) to distinguish early-stage CKM (S2) from mid-advanced-stage CKM (S3 + S4). Model discrimination and calibration were evaluated using receiver operating characteristic (ROC) analysis and the Hosmer-Lemeshow goodness-of-fit test. PTX3 levels were significantly elevated in early-stage CKM (S2) compared with healthy controls (p < 0.001) and showed further differentiation between S2 and S3 (p < 0.01). PTX3 showed moderate correlations with biomarkers reflecting inflammatory activation, metabolic dysregulation, myocardial injury, and renal dysfunction, including high-sensitivity C-reactive protein (hs-CRP; rs = 0.361, p < 0.001), glycated hemoglobin (HbA1c; rs = 0.434, p < 0.001), triglycerides (TG; rs = 0.296, p < 0.001), and high-sensitivity cardiac troponin T (hs-cTnT; rs = 0.411, p < 0.001), and was inversely correlated with estimated glomerular filtration rate (eGFR; rs = -0.419, p < 0.001). In multivariable logistic regression models adjusting for demographic factors, metabolic indices, and cardiorenal biomarkers, PTX3 remained independently associated with classification into mid-advanced-stage CKM (S3 + S4 vs S2; odds ratio [OR] per unit increase = 1.05, 95% confidence interval [CI]: 1.03-1.08; p < 0.001), whereas hs-CRP and procalcitonin (PCT) showed no independent associations. Compared with the clinical base model, addition of PTX3 improved model discrimination, increasing the AUC from 0.833 to 0.892 (ΔAUC = 0.059; DeLong p = 0.008), without evidence of impaired calibration. Circulating PTX3 is cross-sectionally associated with CKM stage classification and demonstrates incremental discriminative value beyond demographic, metabolic, and cardiorenal variables, as well as conventional inflammatory markers, in distinguishing early-stage from mid-advanced-stage CKM. These findings suggest that PTX3 may reflect inflammatory processes not fully captured by systemic markers, supporting its potential role in CKM risk stratification.
The purpose of this study was to explore why and how medical students use non-traditional learning resources relative to the formal curriculum, to inform curriculum development efforts, and support self‑directed learning. A qualitative study grounded in a pragmatic research approach was conducted using semi structured interviews with medical students at the University of Ottawa. Participant recruitment occurred via email/social media, and a pre survey was used to ensure sampling of both low- and high-level resource users. Transcripts were analyzed using reflexive thematic analysis in NVivo. Analysis was both deductive, guided by self-regulated learning (motivation, goal setting, feedback, self-monitoring), and inductive to capture unanticipated themes. Twenty-nine students participated (18 pre-clerkship; 11 clerkship). Four themes were developed: two addressing motivations  for using non‑traditional resources-the traditional curriculum is repetitive and inflexible and non‑traditional resources are high‑yield and flexible; one addressing goal setting-studying for today's exam or tomorrow's patient; and one addressing how students engage with resources, captured through three archetypes-the Traditionalist, the Supplementer, and the Reformer. This study demonstrates how medical students navigate learning by turning to non-traditional resources, shaped by their motivations, goal orientations, and distinct engagement patterns. These insights highlight opportunities to streamline and modernize curricula, integrate vetted high yield resources, and strengthen students' self-regulated learning skills. Leveraging the three learner archetypes can further guide curriculum planning by recognizing diverse learning approaches, engaging Supplementers as indicators of curricular gaps, supporting Traditionalists with structured pathways, and viewing Reformers' non-attendance as an expression of SRL rather than disengagement.
Markerless pose estimation provides a practical approach for extracting movement-related variables from sports video without requiring laboratory-based motion-capture systems. However, pose-derived biomechanical analysis must be interpreted carefully when datasets do not contain ground-truth action labels or independently validated biomechanical outcomes. This study developed a computational pipeline for pose-derived biomechanical feature profiling of basketball player movements using annotated gameplay pose data from the TrackID3 × 3 dataset. Frame-level keypoint annotations were processed to derive biomechanical variables, including elbow angles, segment distances, asymmetry measures, and trunk orientation. These variables were organized into short temporal sequences using a sliding-window approach, enabling descriptive analysis of player posture, limb coordination, subset-level variability, and sequence-level feature patterns. Rule-based proxy biomechanical groupings were constructed to facilitate exploratory evaluation of pattern separability within the pose-derived feature space. The pipeline generated a structured pose-derived dataset of 37,134 frame-player observations and showed measurable variation in joint alignment, limb symmetry, and trunk orientation across players and recording conditions. Classification results should be interpreted as proxy-label separability rather than validated prediction of basketball actions or performance outcomes. Future work should incorporate action-level annotations and independent biomechanical validation.
Implantable neural microelectrodes are the core components enabling high spatiotemporal resolution neural signal recording and stimulation in brain-computer interfaces (BCIs). However, current technologies still face challenges in achieving high-throughput recording, precise implantation, and long-term stability. In this work, we present a high-throughput three-dimensional (3D) helical stretchable neural probe, fabricated via planar electrode micro-fabrication technology followed by thermally driven helical shaping. The main innovations are reflected in the following: First, through the helical deformation, it is possible to simultaneously achieve cross-tissue recording on cortical surface, deep brain, and inside blood vessels. Secondly, the helical structure can expand the wiring space of the electrodes into three dimensions, achieving high spatial resolution and good mechanical compatibility with the tissue. Interface mechanics simulations indicate that the helical structure effectively mitigates strain induced by brain micromotion. Electrochemical modification significantly reduces interface impedance and enhances charge storage capacity (CSC), while cyclic stretching tests confirm stable electrochemical performance under repeated high-strain conditions. Trans-tissue in vivo experiments further validate the probe's versatility: flexible planar MEAs successfully recorded high-quality subcutaneous electromyography (EMG) signals in mice; the helical probe captured single-unit activity in the deep brain of mice with long-term recording stability; and 1024-channel high-throughput signal acquisition was achieved in the pig cerebral cortex. This technology enables high-throughput, stretchable, and cross-scale long-term stable neural recording, providing a versatile tool for next-generation BCIs and clinical neuromonitoring.
This study aimed to analyse the agreement in the clinical classification of keratoconus using four grading systems based exclusively on anterior corneal surface topographic parameters. A retrospective, descriptive, analytical, cross-sectional study was conducted to evaluate agreement and differences among the keratometric scale, Amsler-Krumeich (A-K), Alió-Shabayek (A-S), and Keratoconus Severity Score (KSS) classifications. Retrospective, anonymised data were obtained from anterior corneal surface tomography, including keratometry, pachymetry, corneal aberrations, and the location of the thinnest corneal point, measured with a corneal tomographer (Pentacam®, Oculus Optikgeräte GmbH, Wetzlar, Germany). Agreement between each pair of ordinal scales was assessed using weighted Cohen's kappa coefficient with quadratic weights. A total of 455 eyes with keratoconus were analysed. The mean age was 39.3 ± 12.2 years, with no significant sex differences (p > 0.05). Mean keratometry was 46.5 ± 5.9 D and mean pachymetry was 474.8 ± 70.2 μm. Only 111 eyes could be simultaneously classified across all four systems. Pairwise agreement analysis using weighted kappa revealed generally low concordance between classification systems. The highest agreement was observed between the keratometric and Amsler-Krumeich classifications (moderate agreement), whereas comparisons involving aberration-based systems showed weak to slight agreement. When classifications were based solely on each system's differential parameter, a similar pattern was observed, with moderate agreement between keratometric and pachymetry-based classifications and fair agreement in the remaining comparisons. Keratoconus classification systems based on anterior corneal surface parameters show significant discrepancies and cannot be used interchangeably. Even when reduced to their primary defining parameters, agreement remains limited, indicating that these systems capture different dimensions of keratoconus severity.
To update the 2021 International Society for Gynecologic Endoscopy (ISGE) recommendations for structured reporting of dynamic ultrasound findings in patients with suspected or known endometriosis, integrating recent technical and methodological advances and contemporary diagnostic evidence. Study design A multidisciplinary ISGE working group (sonologists, surgeons, radiologists, methodologists) performed a focused, non-systematic literature review (January 2015-September 2025), prioritizing prospective multicentre diagnostic accuracy studies, high-quality cohorts, systematic reviews and intersociety consensus statements. Draft items were derived from validated frameworks (International Deep Endometriosis Analysis, Morphological Uterus Sonographic Assessment, International Endometrial Tumor Analysis, International Ovarian Tumor Analysis) and the #Enzian classification, refined by a writing subgroup and scored iteratively using Delphi rounds. Evidence quality and recommendation strength were graded using the Grading of Recommendations Assessment, Development and Evaluation system. When performed by experienced operators, dynamic ultrasound shows high specificity and generally high sensitivity for ovarian endometriomas, many forms of deep endometriosis, and adenomyosis; sensitivity is more variable, particularly for parametrial involvement and small superficial peritoneal lesions. Structured, compartment-based reporting improved completeness and may increase concordance with intraoperative mapping compared with unstructured reports. The 2026 ISGE update expands the 2021 template with standardized superficial peritoneal descriptors, lateral compartment subdivision (parametrium, pelvic sidewall, pelvic nerve assessment) and detailed quantitative pelvic metrics. Optional advanced fields capture items supported by lower-quality or emerging evidence. Implementation recommendations include targeted training, Picture Archiving and Communication System integration, and staged adoption. The 2026 ISGE structured ultrasound report provides a practical, surgically actionable template. Prospective multicentre validation and standardized training are priorities.
Sex differences in the incidence and outcomes of non-reproductive cancers persist across many tumor types, even after adjustment for major exposure- and care-related factors. This review examines how sex-hormone signaling may contribute to these patterns through tumor-intrinsic mechanisms and regulation of the tumor microenvironment. In tumor cells, ER, AR and PR mediate classical nuclear transcriptional programs, whereas membrane-associated or cytoplasmic receptor pools, together with GPER, support rapid non-genomic signaling through PI3K/AKT, MAPK/ERK and related kinase or second-messenger pathways. Intratumoral steroid handling can create local ligand conditions that differ from circulating hormone levels and modify context-specific receptor activity. Hormonal context may also influence vascular, stromal and immune phenotypes. Clinically, sex-hormone-related tumor states may be better captured by integrated activity-based readouts, including receptor status, pathway activation, local steroid availability and immune context, rather than receptor immunostaining alone. Overall, sex-hormone signaling offers a hypothesis-generating framework for understanding sex-biased tumor biology beyond traditional hormone-driven cancers, but its clinical relevance requires further mechanistic and prospective validation.
Biomolecular interactions involving proteins, nucleic acids, and small molecules constitute the molecular foundation of cellular regulation, signaling, and therapeutic intervention. Advances in mass spectrometry-based proteomics have enabled the systematic characterization of these interactions at unprecedented depth, sensitivity, and structural resolution. This chapter provides a comprehensive overview of state-of-the-art proteomics methodologies developed to investigate protein-protein, protein-nucleic acid, and protein-drug interactions, with particular emphasis on experimental design, sample preparation, and data quality control. Targeted and untargeted strategies are discussed, including affinity purification-mass spectrometry, proximity-dependent labeling, cross-linking mass spectrometry, blue native electrophoresis, and size-exclusion chromatography-mass spectrometry for protein-protein interactions; affinity capture, EMSA-MS, chromatin immunoprecipitation-mass spectrometry, CRISPR-based locus-specific enrichment, and CLIP-based approaches for protein-nucleic acid complexes; and chemoproteomics, thermal proteome profiling, and label-free structural proteomics for protein-drug interaction analysis. The chapter further highlights recent technological innovations, computational tools, and integrative multi-omics strategies that enhance interaction mapping across biological scales. By critically evaluating the strengths, limitations, and appropriate applications of each methodology, this work aims to provide practical guidance for researchers seeking to design robust interactomics experiments and to interpret complex molecular networks in both basic and translational research contexts.
Despite the growing prevalence of hate crimes in the United States, little is known about whether and how state-level institutional and cultural climates surrounding bias-motivated violence pattern health and health disparities. This study conceptualizes state-level hate crime rates as composite indicators of these climates-reflecting the underlying incidence, institutional recognition and reporting, and political-cultural context-and examines their association with individuals' mental and physical health. Linking FBI Hate Crime Reporting Program data with individual-level data from the Behavioral Risk Factor Surveillance System (2011-2023) (n = 3,827,446), we first document substantial variation in hate crime rates across states and over time. Using two-way fixed effects models, we find that higher state-level hate crime rates are associated with more days of poor mental and, to a lesser extent, physical health, with associations concentrated among Black respondents and little evidence of associations among White or Hispanic respondents. By contrast, state-level violent crime rates show weak, racially undifferentiated associations with health, underscoring that state hate crime climates likely operate through distinct pathways from general crime rates to shape health risks. Taken together, findings are consistent with the notion that state-level hate crime rates capture a meaningful dimension of institutional and cultural context relevant to population health, with especially concerning implications for the health of historically marginalized populations, particularly Black Americans.
This paper proposes a semantic-guided edge enhancement approach for graph self-supervised learning in network intrusion detection. It aims to address several issues that the existing intrusion detection systems face, such as relying on a large amount of labeled data, struggling to capture complex network topology, and overlooking the internal information of edges. Concretely, to improve the discriminability of the network flow graph, we introduce a new node‑edge‑node attention algorithm for graph enhancement representation. It integrates edge-aware attention and intra-edge feature self-attention collaboratively, thereby assists the model to perceive complex attack behaviors at multiple granular levels effectively. Meanwhile, we devise a semantic-aware contrastive learning framework that collaboratively enhances nodes and edges, which enables view augmentation without corrupting the original graph semantics, forcing the model to learn more robust and discriminative features. Consequently, our method overcomes the scarcity of labeled samples remarkably. In the experiments, seven SOTA methods were contrasted with the proposed one on four public datasets. The results show that the proposed method outperforms existing mainstream models in accuracy, precision, recall, and F1-score, demonstrating its efficient detection performance and strong generalization capability.
Malaria remains a major public health challenge in sub-Saharan Africa (SSA), where climatic variability continues to influence transmission dynamics despite ongoing control efforts. This study investigates the nonlinear and lagged effects of climatic factors on malaria incidence using a balanced monthly panel of 20 SSA countries covering the period 2015-2024 (N = 2400 observations). Monthly malaria incidence per 1000 population is modelled as a function of rainfall, 2-m air temperature, and vegetation greenness, while population density and elevation are included as demographic and topographic controls. Fixed-effects generalised additive models (GAMs) are employed to capture nonlinear exposure-response relationships, complemented by distributed lag nonlinear models (DLNMs) with lags of 0-3 months and a log-linear fixed-effects panel specification. Climatic and environmental variables are obtained from publicly accessible datasets, including CHIRPS, ERA5-Land, MODIS, WorldPop, and NASA SRTM, while malaria incidence data are compiled from national surveillance systems and World Health Organisation repositories. The results reveal significant nonlinear and temporally lagged effects of rainfall and temperature on malaria incidence, with evidence of threshold behaviour across climatic ranges. Vegetation greenness exhibits an increasing-then-saturating association with transmission. Population density is positively associated with malaria incidence, whereas elevation exerts a significant protective effect. The models explain approximately 70-73% of the observed variation in malaria incidence and satisfy key diagnostic requirements. These findings demonstrate the importance of accounting for nonlinear and delayed climatic influences when modelling malaria risk and provide evidence to support climate-informed early warning systems and adaptive malaria control strategies across SSA.
Viruses represent the most abundant biological entities on Earth and play a pivotal role in microbial ecosystems, yet, as prominent human pathogens, they are closely linked to human morbidity and mortality. Accurate identification of viral sequences from viral genome sequences is therefore essential, but existing genome-based classification models that largely rely on composition- or frequency-based subsequence features often suffer from limited interpretability and reduced accuracy, particularly on complex or imbalanced datasets. To address these limitations, we propose GeneNSPCla (Genomic Negative Sequential Pattern-based Classification), a novel viral classification framework based on Negative Sequential Patterns (NSPs) that extracts discriminative absence-based features from nucleotide sequences of RNA viral genomes. By transforming these NSPs into numerical feature vectors and integrating them into multiple supervised classifiers, GeneNSPCla effectively captures both presence and absence signals in viral sequences. Furthermore, we propose a negative pattern mining algorithm adapted for processing genomic data: GONPM+, which can discover longer and more biologically meaningful negative sequential patterns. The experimental results demonstrate that the average accuracy of GONPM+ in 8 classifiers has improved by 10.03% compared to the original negative pattern mining algorithm and by 24.75% compared to the positive pattern mining algorithm. These findings highlight the effectiveness of incorporating absence-based sequential information, providing a new and complementary perspective for viral genome analysis and classification. The source code and datasets are available at https://github.com/zhuwenxi317/GeneNSPCla.
Interpersonal coordination - the temporal coupling of movement between interactants - is widely considered to support social interaction, with laboratory studies demonstrating that greater coordination is associated with increased rapport, bonding, and positive affect. However, most of the evidence for these psychosocial benefits comes from tightly controlled interactions involving unfamiliar partners. Whether these findings generalise to naturalistic, real-world interaction remains largely unexplored. The present study examined interpersonal coordination and its psychosocial correlates in friends and strangers. Sixty-two dyads (30 friend pairs, 32 stranger pairs) completed a cooperative scavenger hunt task across a university campus. Movement was captured using motion tracking and analysed via cross-recurrence quantification analysis to characterise coordination dynamics across the interaction. Psychosocial measures - including rapport, connectedness, and affect - were assessed before and after the task. Friends exhibited higher overall levels of coordination than strangers, while strangers showed a significantly faster increase in coordination over time. Coordination metrics, however, showed limited associations with psychosocial outcomes. Although some measures predicted momentary affect in strangers, coordination did not predict rapport change in either group. Instead, psychosocial outcomes were primarily predicted by relationship type, with strangers showing greater increases in rapport and connectedness than friends. Together, findings suggest that the social consequences of interpersonal coordination are contingent on relational context and underscore the importance of studying interpersonal coordination within naturalistic settings.
The transition to clean energy in the United States remains insufficient despite rising environmental concerns and increasing renewable energy adoption. This study investigates whether better institutional quality can effectively drive cleaner energy outcomes by examining the impact of governance alongside key macroeconomic factors. Using quarterly data from 1990 to 2024, the study employs a wavelet quantile regression approach to capture nonlinear and time-varying dynamics across short-, medium-, and long-run horizons. The findings reveal that economic growth, foreign direct investment, and trade openness positively influence renewable energy consumption, particularly over longer time horizons. In contrast, carbon emissions exhibit a negative relationship with renewable energy adoption. Surprisingly, institutional quality shows a predominantly negative effect, suggesting that stronger institutions may reinforce existing fossil fuel-based energy structures rather than accelerate transition. These results highlight the complexity of institutional roles in energy transformation and emphasize the need for targeted regulatory reforms to support renewable energy expansion in the United States.
Reliable potency assays are critical for ensuring the therapeutic consistency and manufacturing quality of mesenchymal stem/stromal cells (MSCs). Conventional assays based on soluble factor quantification often fail to capture extracellular matrix (ECM) remodeling capacity and exhibit high variability. We developed a biologically relevant, image-based potency assay that directly quantifies MSCs-mediated ECM remodeling under defined biophysical conditions. A collagen-coated (CL) surface that provides a stable, proliferation-suppressive microenvironment, enabling single-cell quantification of remodeling activity using collagen hybridizing peptide staining. The ECM remodeling index (ICHP+) defined an optimal working range (3.0 × 103 ≤ cells/cm2 ≤ 4.5 × 103), where spatially separated MSCs achieved high analytical precision (CV < 10%). The CL-based assay demonstrated robust repeatability across MSCs sources and biologically relevant quantitation limits. Integration of ICHP+ with γH2AX, enabled simultaneous assessment of functional potency and cell health. Response surface methodology modeling of culture duration, seeding density, and passage number revealed a dome-shaped relationship, with maximal remodeling at early passages (Np1-Np3) and 3-5 days of culture, whereas extended culture (> 6 days) decreased potency alongside elevated γH2AX expression. The model exhibited strong predictive performance (R2 > 0.95), supporting its utility for process optimization. Collectively, this mechanism-based potency assay enables standardized platform for potency evaluation and manufacturing control for consistent, high-quality MSCs therapeutics.
Unplanned equipment and infrastructure interruptions are a persistent source of radiotherapy service loss, yet prioritization is often based on either downtime burden or risk criticality alone. This study analyzed five years of interruption logs to integrate a standardized failure taxonomy with downtime-based Pareto analysis and FMECA criticality scoring. A harmonized event log captured asset class, subsystem, cause category, operational severity, downtime, and maintainability indicators, including response and repair times. Event burden and downtime distributions were quantified, Pareto rankings were performed at asset, subsystem, and cause levels, and FMECA Risk Priority Numbers were analyzed. Agreement between Pareto and FMECA priorities was assessed using Top-k overlap and Spearman rank correlation. A total of 4200 events produced 16,006.4 h of service loss. Downtime per event was right-skewed, with a median of 0.93 h and an interquartile range of 0.43-2.50 h. Minor events comprised 42.95% of events but accounted for only 4.39% of downtime, whereas critical events represented 6.60% of events but generated 50.93% of downtime. Major events contributed 31.79% of downtime from 19.43% of events. Unit-level burden ranged from 755 to 1707 events and from 2990.9 to 6077.8 h of downtime. Downtime was dominated by LINAC failures, accounting for 60.5%, followed by HDR after loader faults at 15.0%, OIS/network interruptions at 8.6%, CT-sim faults at 6.9%, TPS-related events at 6.0%, and auxiliary infrastructure at 3.0%. The leading subsystem contributors were imaging systems at 1324.7 h, MLC components at 1178.7 h, and software/controls at 1152.5 h. The leading cause categories were hardware wear/aging at 4532.6 h, power instability at 2340.2 h, and vendor part delay at 2212.6 h. Median response times clustered between 0.60 and 0.61 h, while median repair times ranged from 0.62 to 0.86 h. Pareto and FMECA alignment was limited, with a Top-10 overlap of 3/10, a Top-5 overlap of 0/5, and a Spearman correlation of approximately 0.09. Service loss was concentrated in a minority of major and critical episodes. Combining Pareto analysis, which prioritizes lost hours, with FMECA, which identifies rare but high-criticality modes, supports balanced and actionable prioritization for maintenance planning, spare-parts and logistics strategy, power conditioning, and digital infrastructure resilience. Radiotherapy services can be disrupted when equipment fails, which may delay treatment for patients. This study reviewed five years of equipment failure records to understand which problems caused the most service interruptions and how best to prioritize them. This study found that a small number of serious issues caused most delays, and using more than one method helped identify priorities more clearly. This matters because better planning can reduce delays and support safer, more reliable treatment.