Plant-microbe interactions shape plant metabolism and rhizosphere processes, yet how root metabolic states are coupled to exudation remains unclear. Here, we show that inoculation with Serratia marcescens (NJ2D) and Funneliformis mosseae (BJ04), particularly in combination, enhances biomass (p < 0.005) and alleviates oxidative stress in Liquidambar formosana. Metabolomic analyses revealed a concerted remodeling of root primary metabolism, characterized by shifts in amino acids, organic acids, and sugars, alongside consistent enrichment of pentose and glucuronate interconversions. Concomitantly, root exudation was restructured, with increased release of carbon-rich metabolites. Notably, trehalose declined in both roots and exudates, indicating reduced osmoprotective demand and reallocation of metabolic resources. Together, these findings demonstrate that microbial inoculation reshapes root metabolism and exudation patterns in L. formosana, linking plant physiological responses with rhizosphere processes and potentially strengthening plant-microbe feedbacks.
Conventional heterogeneous catalysts are constrained by static atomic structures, which are ill-suited for the diverse requirements of multistep reactions. This article introduces Field-Programmable Dynamic Catalysis (PDC), a paradigm enabling real-time, reversible programming of atomic structures via external fields and intelligent feedback. Using copper-based liquid metal catalysts, we demonstrate adaptive restructuring that optimizes methane oxidation performance. With magnetically tunable iron-based catalysts, we achieve reversible switching between atomically dispersed and clustered states, thus validating on-demand control over reaction pathways. This paradigm holds great promise for inert small molecule activation, such as nitrogen conversion, yet key challenges remain in boosting liquid metal surface area and designing scalable reactors. To address this multidimensional complexity, artificial intelligence is indispensable for accelerating the discovery and development of advanced PDC catalysts. Ultimately, PDC transforms catalysts from passive materials into active information-processing systems, enabling intelligent and precise chemical control at the atomic scale.
Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor-ligand recognition, protein-protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca2+, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture.
Background/Objectives: This prospective, randomised controlled trial aimed to evaluate whether using a real-time feedback device during basic life support (BLS) training for laypersons improves chest compression quality immediately after training and at the four-month follow-up. Methods: Participants were randomly assigned to a control group (standard BLS training) or an intervention group (BLS training with a real-time feedback device). All participants completed a standardised 2-h BLS course, followed by a 4-min practical assessment immediately after training and at the four-month follow-up. The primary outcomes were chest compression rate and depth, while the secondary outcomes were correct hand position, full chest recoil and flow fraction. These compression parameters were compared within and between groups at both time points. Results: Data from 101 participants were analysed. Both groups showed significantly decreased mean and adequate compression rates over time, but only the intervention group demonstrated significantly better performance at follow-up. The mean compression depth was approximately 5 cm in both groups; however, the proportion of adequate compression depth was low and did not differ significantly within or between groups. Correct hand position was consistently higher in the intervention group across both assessments. Full chest recoil improved in both groups, whereas flow fraction increased only in the control group. Conclusions: Incorporating real-time feedback devices into layperson BLS training leads to superior performance in selected chest compression parameters, particularly compression rate and hand position. Therefore, real-time feedback devices can be a valuable adjunct to standard BLS training to enhance skill retention over time.
South Africa has the largest HIV epidemic in the world; in KwaZulu-Natal Province, over 40.8% of adults aged 15 years and older are living with HIV. Despite this, South Africa is home to only 3% of the world's health care workers. Nurses constitute the largest group of providers in South Africa and experience high levels of burnout, which can contribute to negative patient outcomes for people living with HIV, including reduced treatment adherence. Nurse-centered interventions that offset these effects are urgently needed. This study aims to test the feasibility and acceptability of an adapted resiliency intervention (Stress Management and Resiliency Training-Relaxation Response Resiliency Program) for professional nurses who provide care for people living with HIV in South Africa. In phase 1 (Human Research Ethics Committee [Medical] of the University of the Witwatersrand [220813, Johannesburg, South Africa] and the Massachusetts General Brigham Institutional Review Board [2022P002765, Boston, Massachusetts, United States]), we conducted 3 focus group discussions to solicit feedback on the lived experiences of stress, sources of stress, impact on job functioning, coping strategies, the proposed intervention, and recruitment strategies for nurses. These data informed adaptations to the intervention. In phase 2 (Human Research Ethics Committee [240106, Johannesburg, South Africa]; Massachusetts General Brigham Institutional Review Board [2024P001407, Boston, Massachusetts, United States]), we conducted a small proof-of-concept study (N=8) with preintervention and postintervention assessments, 6 intervention sessions with a nurse interventionist, and a qualitative exit interview. Following appropriate adaptations, we conducted a pilot randomized controlled trial (N=60) in which participants were randomized to the intervention or the control condition. The control condition received a one-time, 90-minute didactic stress management session. The intervention condition consisted of two 4-hour group skills-based sessions on the relaxation response, components of stress, recuperative sleep, mindful awareness, resilience, and social support. Sessions included practice-based exercises and videos to complement the intervention materials. Baseline, posttreatment (intervention only), and follow-up assessments, as well as qualitative exit interviews (n=15, intervention only), were conducted. Primary outcomes are feasibility (number screened, eligible, and enrolled; the number of treatment sessions and assessments completed in the intervention arm; assessment duration; and reasons for declining enrollment and prematurely leaving the trial) and acceptability (Client Satisfaction Questionnaire-8 and qualitative data). The project is funded by the National Institute of Mental Health (R34MH126753; September 2022). As of October 2025, we have completed both the proof-of-concept study (n=8; February 2025) and the pilot randomized controlled trial (n=60; August 2025). Data analysis is in progress and is expected to be completed in August 2026. Structural changes are needed to ensure the well-being of health care providers; however, given that structural changes take time, money, and political capital to execute, we must develop interventions to support providers' mental health while advocating for systematic change.
Wireless sensor networks supported by mobile edge computing are increasingly required to process heterogeneous sensing data under stringent latency, reliability, and energy constraints. However, most existing task-offloading studies are still formulated for generic user equipment and primarily focus on uplink transmission, which is insufficient for practical sensing systems where sensor nodes continuously upload measurements while simultaneously receiving control commands, model updates, and feedback from the edge. To address this gap, this paper reformulates joint computation offloading and power control as a sensor-aware optimization problem in an edge-assisted wireless sensor network. We propose a three-layer architecture consisting of sensor nodes, access points with lightweight edge servers, and a cloud coordination layer. Each sensing task is characterized by data size, computation density, latency deadline, and sensing priority, while the optimization objective jointly minimizes long-term task delay, communication and computation energy, and packet-loss penalty under transmission power, edge resource, and residual-energy constraints. To solve the resulting mixed discrete-continuous problem, we develop a multi-agent reinforcement learning framework in which each sensor node acts as an autonomous agent and learns offloading and transmission policies with clipped proximal policy optimization, while the cloud layer performs coordinated edge-resource allocation through the alternating direction method of multipliers. In addition to delay and energy, network lifetime and sensing delivery performance are incorporated into the evaluation. Simulation results in a sensor-network monitoring scenario demonstrate that the proposed framework consistently reduces latency, lowers energy consumption, and prolongs network lifetime compared with representative baselines, highlighting its effectiveness and practical potential for intelligent sensing applications that require integrated sensing, communication, and edge computing.
In the context of population aging and the growing burden of chronic conditions, promoting exercise participation has become an important strategy for supporting healthy aging. However, older adults with chronic conditions often face multiple constraints related to symptom burden, risk perception, and everyday life. A theory-informed understanding of the determinants of exercise participation in this population is therefore needed. This study adopted a theory-informed qualitative descriptive design and conducted face-to-face semi-structured interviews with 30 community-dwelling older adults with chronic conditions. Purposive sampling was used to ensure variation in age, sex, chronic condition type, and exercise participation. Data were analyzed using the framework method guided by the Theoretical Domains Framework (TDF), and the resulting themes were subsequently mapped onto the Capability, Opportunity, Motivation-Behavior (COM-B) model. Participants were aged 60-86 years, and most were women, had low educational attainment, came from rural backgrounds, and lived with multimorbidity. Participants described exercise participation as a day-to-day process of negotiating symptoms, risk, functional boundaries, and everyday responsibilities rather than as a simple matter of willingness. Although most participants recognized the value of exercise, many lacked disease-specific knowledge about suitable exercise types, safe intensity, progression, and warning signs. Symptom burden and functional limitations constrained exercise, but many participants used symptom-based self-regulation strategies, such as resting, slowing down, or modifying activity when discomfort occurred. Family members, peers, health professionals, and community resources could either facilitate exercise or restrict it, depending on their accessibility, continuity, specificity, and practical relevance. Continued participation was closely linked to perceived benefits, controllable risk, self-efficacy, positive emotional experience, and immediate bodily feedback. Exercise promotion for older adults with chronic conditions should move beyond general advice and provide disease-adapted exercise education, symptom-based self-regulation strategies, family and peer support, professional guidance, age-friendly community resources, and feedback mechanisms that support long-term maintenance.
Physically active play (PA-play) offers natural, self-directed, and varied opportunities for physical activity and motor skill development in children. It is often viewed as a rich context for learning, yet how PA-play systematically supports the core concepts and elements of motor learning (ML) requires a closer examination in typically developing (TD) children and those with neurodevelopmental disorders such as Developmental Coordination Disorder (DCD). A two-part integrative conceptual synthesis was conducted to explore how core ML concepts are reflected in children's PA-play. Part 1 involved a synthesis of the play literature, analyzed through an ML lens in TD children. Part 2 involved synthesizing the literature on ML elements and characteristics of PA-play in children with DCD. In Part 1, the conceptual synthesis highlighted that PA-play in TD children enables conditions supportive of ML, including both implicit and explicit learning, high-volume practice, task variability, progressive challenge, and feedback through verbal and non-verbal cues. In Part 2, the synthesis highlighted ML difficulties in children with DCD, such as slow, effortful learning with reduced adaptability and greater performance variability. Additionally, the synthesis highlighted limited DCD evidence using PA-play as an ecological context for ML. Overall, PA-play could offer environments consistent with ML elements in TD children, yet evidence for its effectiveness in children with DCD remains limited. Future research should explore how PA-play can be leveraged to address the specific ML challenges faced by children with DCD.
Wavelength stability of distributed feedback (DFB) semiconductor lasers in dense wavelength division multiplexing (DWDM) systems hinges on sub-millikelvin temperature regulation, a task complicated by the nonlinear, multi-node dynamics of the thermoelectric cooler (TEC) and the purely reactive nature of conventional proportional-integral-derivative (PID) control. We present a physics-informed neural network (PINN) built around a residual correction architecture for hybrid feedforward-feedback TEC temperature control. Rather than penalizing physics-residual violations in the loss function, the architecture wires a simplified one-node thermal model directly into the network graph as a frozen baseline. A trainable branch then learns only the residual mismatch. Temporal lag features are appended to the input so that the network can reconstruct unmeasured internal thermal states from the cold-side temperature history, which proves essential for overcoming the partial-observability bottleneck inherent in multi-node TEC packages. Ablation experiments on a high-fidelity three-node TEC simulator show that all model variants (PINN, physics-feature-augmented NN, and pure NN) exceed R2 = 0.993 when trained on the full dataset, yet the PINN's advantage becomes pronounced under data scarcity. At a 3% training budget, it reaches R2 = 0.966 versus 0.930 for the pure NN, implying an approximately 5.4× reduction in the data needed to reach a given accuracy target. In closed-loop validation, the PINN+PID hybrid settles 60% faster than standalone PID. Tracking RMSE drops by 69%, and peak disturbance deviation falls by 74%, across step, multi-setpoint, and current-perturbation scenarios. All results reported here are obtained in simulations. Experimental validation on physical DFB-TEC hardware is left to future work.
Background/Objectives: Anterior direct composite restorations are evaluated through instrumental color matching, clinician appraisal, and patient perception, but these endpoints may diverge by age. This cross-sectional study compared adolescents/young adults (AYA, 15-25 years) with adults/elderly (AE, 50-75 years) for spectrophotometric color difference (ΔE*ab), patient and clinician aesthetic ratings, patient-clinician agreement, and oral-health-related quality of life (OHRQoL). Methods: Consecutive recall patients with at least one anterior direct composite restoration placed ≥6 months earlier were screened; 128 were enrolled, and 126 completed all assessments (AYA n = 64; AE n = 62). Participants completed the OHIP-14 and aesthetic visual analogue scale (VAS) before receiving any USPHS, clinician VAS, or spectrophotometric feedback. A separate clinician, masked to patient scores and spectrophotometric outputs but not to patient age, recorded clinician VAS and modified USPHS criteria. Results: AE restorations showed higher ΔE*ab than AYA restorations (4.8 ± 1.6 vs. 3.2 ± 1.1; p < 0.001), whereas AYA reported lower patient VAS (72.4 ± 12.3 vs. 81.6 ± 10.8; p < 0.001) and higher OHIP-14 psychosocial burden (7.2 ± 2.8 vs. 4.0 ± 2.3; p < 0.001). Clinician VAS was higher in AYA (85.2 ± 7.3 vs. 79.4 ± 8.9; p < 0.001). Patient VAS correlated modestly with ΔE*ab (ρ = -0.38 in AYA; ρ = -0.31 in AE) and more strongly with psychosocial OHIP-14 scores (ρ = -0.54 and -0.47, respectively). Patient-clinician agreement was poor in AYA (ICC = 0.26) and moderate in AE (ICC = 0.58), with larger negative patient-minus-clinician discrepancies in AYA. Exploratory mediation statistically decomposed the age-related patient-satisfaction difference more through patient-clinician discrepancy than through ΔE*ab; causality cannot be inferred. Conclusions: Younger patients may experience dissatisfaction and psychosocial burden despite better instrumental color match. Assessment of anterior composites should combine objective shade measurement with patient-centered expectation clarification, and longitudinal studies should test temporal mechanisms and communication interventions.
Cultivating mathematical higher-order thinking is a paramount pedagogical objective. While student feedback literacy has gained attention, its longitudinal mechanisms on complex cognitive outcomes remain underexplored. This study investigates the sequential mediating roles of emotion and motivation self-regulation and mathematics discourse feedback skills in the relationship between feedback literacy behavior and mathematical higher-order thinking. A three-wave longitudinal design with a 3-month interval was employed. A total of 1,795 Chinese high school students completed surveys assessing feedback literacy behavior at Time 1, emotion and motivation self-regulation at Time 2, and mathematics discourse feedback skills alongside mathematical higher-order thinking at Time 3. Data were analyzed using structural equation modeling, controlling for autoregressive effects. Feedback literacy behavior at Time 1 was significantly and positively associated with mathematical higher-order thinking at Time 3. Both emotion and motivation self-regulation and mathematics discourse feedback skills independently mediated this relationship. Furthermore, the serial mediation pathway was significant: feedback literacy behavior enhanced emotion and motivation self-regulation, which subsequently fostered mathematics discourse feedback skills, which were concurrently associated with mathematical higher-order thinking. Feedback literacy behavior acts as a developmental catalyst for cognitive restructuring. This trajectory is sequentially mediated by intra-individual psychological regulation and interpersonal social interaction. Educators should transcend traditional corrective feedback by actively cultivating feedback literacy and facilitating dialogic feedback environments.
A novel non-contact piezoelectric motor modulated by electromagnetic force is proposed in this work. The motor consists of a driving system and a transmission system. The transmission system includes a driving torque modulation mechanism and a keeping torque modulation mechanism. The calculation model of the magnetic forces of the motor is deduced, based on which the calculated equations of the magnetic driving torque, the magnetic keeping torque, the total torque, and the torque fluctuation applied to the rotor are presented. The transfer functions of the motor torque and its proportional-integral (PI) control are also given. Compensation control is used to remove the torque fluctuation. Via the derived equations, the effects of the system parameters on the system gain and time constant are investigated. Moreover, the step responses of the motor torque and the effects of the system parameters on them are analyzed, as are the step responses of the closed-loop control system with a PI controller. Furthermore, the torque fluctuation of the rotor is investigated, and its compensation signals are determined. Finally, the compensation control of the torque fluctuation is realized by adding feedback compensation signals.
The acidic tumor microenvironment (TME) presents a key pathological hallmark that can be exploited for targeted therapeutic and diagnostic interventions. This review systematically outlines the design and application of pH-responsive materials in biomedical imaging and therapy, emphasizing their role in achieving spatial and temporal control over drug delivery and imaging contrast. We first detail the fundamental chemical mechanisms underpinning pH responsiveness, including dynamic covalent bonds (e.g., acylhydrazone, imine, orthoester, and borate ester), metal-ligand coordination bonds, and pH-dependent noncovalent interactions. These principles are then translated into a variety of nanocarrier platforms, such as polymeric micelles, liposomes, hydrogels, metal-organic frameworks (MOFs), PROTAC conjugates, and self-assembling peptides, all of which are designed to undergo structural or functional changes in response to acidic conditions. The convergence of these smart materials with advanced imaging modalities has enabled transformative applications, including activatable probes for high-contrast functional imaging, multistimuli responsive theranostics, organelle-precision targeting, and spatiotemporally programmed release systems. These innovations collectively enhance diagnostic accuracy, therapeutic specificity, and the potential for real-time treatment monitoring. However, clinical translation remains challenged by issues related to reproducible synthesis, long-term biocompatibility, and the complex heterogeneity of in vivo environments. Future advancements are anticipated to focus on the integration of artificial intelligence for material design, the development of closed-loop feedback systems, and rigorous translational studies to bridge the gap between laboratory innovation and clinical application.
This paper introduces an electrocardiogram (ECG) noise removal front-end amplifier circuit based on a current-feedback operational amplifier (CFOA) that uses the current feedback to detect error signals and control the output. This ECG circuit focuses on denoising the ECG noise to accentuate the ECG electrical signals from the heart. Noises in ECG refer to baseline wander (BW), powerline interference (PLI) and motion artifacts. We proposed a CFOA-based ECG pre-amplifier using the AD844 commercial operational amplifier built inside with a positive second-generation current conveyor (CCII+) and a voltage follower circuit. This work introduces an ECG noise removal front-end amplifier based on a CFOA. The primary innovation lies in the balancing instrumentation amplifier architecture that utilizes the high-speed and robust properties of the AD844 commercial operational amplifier to achieve superior noise rejection. To protect against high-frequency interference, we introduce a novel cascaded low-pass filter (LPF) stage that ensures a sharper cut-off compared to traditional single-stage designs. Experimental results validate the design's effectiveness, achieving a high common-mode rejection ratio (CMRR) of 75.4 dB and a mid-band gain of 46.5 dB. These performance metrics, combined with the circuit's ability to eliminate BW and PLI, confirm its robust suitability for high-fidelity wearable ECG monitoring.
In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control.
The integration of olfactory feedback into Virtual Reality (VR) applications remains significantly underexplored compared with other sensory modalities, particularly within room-scale Cave Automatic Virtual Environments (CAVEs), where related research is even more limited. To address this gap, this paper presents Scentree, a custom olfactory system capable of delivering scents in real time based on user interactions, along with Smelling Ancient Greece, an olfactory-enhanced VR experience developed for integration within our CAVE system. Central to the proposed approach is the concept of the Diegetic Olfactory Feedback Loop, which reframes olfaction from a passive ambient effect into an active, interaction-driven feedback mechanism embedded within the narrative context of the virtual environment. To evaluate the system, we conducted a technical performance assessment and an exploratory user study (N=51) examining participant perceptions of immersion, presence, perceived realism, usability, and overall user experience. The findings support the feasibility of interaction-driven olfactory feedback as a multisensory design approach for CAVE environments and provide a foundation for future controlled investigations of olfactory feedback in immersive VR.
Tumor metastasis constitutes a frequent contributor to high mortality rates, with EMT intimately implicated in this disseminative process. Accumulating evidence in recent years indicates that neoplastic cells undergoing EMT frequently exhibit concurrent metabolic reprogramming. Multiple modalities-including glycolysis, mitochondrial oxidative phosphorylation, lipid metabolism, as well as amino acid metabolism-cooperatively supply energy, facilitate membrane remodeling, and sustain redox homeostasis. Specifically, glycolytic flux, oxidative phosphorylation, lipid turnover, and amino acid catabolism/anabolism function in a concerted manner to meet the bioenergetic demands, support biogenesis of cellular membranes, and preserve the intracellular redox equilibrium during phenotypic conversion. Notably, intermediate metabolites can in turn modulate the trajectory of EMT through signal transduction cascades or epigenetic modifications. This review systematically delineates the bidirectional regulatory circuitry interconnecting EMT and metabolic reprogramming; furthermore, it examines the implications of this crosstalk for neoplastic disease progression. Finally, therapeutic strategies targeting the nexus of metabolic reprogramming and EMT are summarized.
To address the issues of low perception accuracy and poor robustness in traditional motion recognition methods within complex walking environments for visually impaired individuals, this study utilizes multi-modal data, including ECG, PPG, and IMU, for classification. Regarding the low filtering efficiency of multi-modal data, an improved wavelet filtering algorithm based on LSTM is proposed. To further enhance classification accuracy, this paper introduces a motion recognition method for the blindfolded mobility simulation based on an Attention-based Two-Stream Deep Fusion Convolutional Neural Network (ATS-DFCNN). The proposed method constructs a two-stream heterogeneous feature extraction architecture by synchronously collecting tri-axial motion signals and physiological signals from subjects. A 1D-CNN is employed to capture the spatial geometric features of limb movements, while a hybrid CNN-GRU network is utilized to mine the temporal evolution patterns of physiological stress. Furthermore, an attention mechanism is introduced to achieve dynamic weighted fusion at the feature level, which strengthens critical motion features and suppresses environmental noise. Experiments were conducted with 10 subjects simulating the movements of visually impaired individuals, covering typical actions such as walking, standing, climbing stairs, descending stairs, and falling. The results demonstrate that the proposed adaptive filtering algorithm achieves an AUC of 0.942, significantly improving feature distinctiveness compared to traditional algorithms. The ATS-DFCNN model achieved an average recognition accuracy of 92.2% across five activity categories, representing a 4.8% performance increase over single IMU modal classification. Particularly in fall detection, the model effectively reduces false alarms through physiological feedback and accurately infers motion intentions, providing reliable technical support for the safety monitoring of intelligent walking-aid systems.
Background/Objectives: Parenting interventions are an effective way to support child development, and brief screening tools can support equitable implementation of parenting interventions by reducing program costs, increasing accessibility, and engaging populations who have traditionally been underserved. However, brief assessments are frequently overlooked and underutilized. The Family Check-Up (FCU) Online is a digital parenting intervention that integrates a brief FCU Online Assessment, feedback, and parenting skills via an app along with optional provider support. To date, no prior work has validated the FCU Online Assessment. Method: The current study combined two samples of parents participating in FCU Online studies and assessed: (1) reliability, (2) construct validity, (3) convergent validity by comparing FCU Online Assessment subscales to similar parenting and child behavior measures, and (4) predictive validity by using FCU Online Assessment at pretest to predict posttest scores as well as parenting and child behaviors at time 2 and time 3. Results: Strong reliability was found among all five subscales, including Low Conflict (7 items, α = .81), Positive Parenting Practices (11 items, α = .80), Positive School Behaviors (5 items, α = .83), Consistent Rules and Routines (11 items, α = .81), and Child Mental Health (5 items, α = .80). The FCU Online Assessment demonstrated construct and convergent validity, as well as predictive validity in that the FCU Online Assessment at pretest predicted posttest scores. Conclusions: The FCU Online Assessment is a brief, reliable, and valid measure of parenting and child wellbeing. It can be used by parents and providers alike to evaluate parenting skills and child mental health, develop targeted goals and intervention approaches, and assess family wellbeing over time.
Tumor localization during pulmonary surgery has become increasingly challenging with the earlier detection of smaller and smaller lung nodules. Concomitantly, minimally invasive surgical (MIS) techniques have been increasingly adopted within the field of thoracic surgical oncology. Surgeons face growing challenges not only with locating these small tumors, but also with immediate margin assessment, reduced tactile feedback, and nodal assessment. Intraoperative molecular imaging (IMI) has emerged as a promising adjunct to address these challenges by enabling real-time visualization of malignant tissue during pulmonary resection. In its current form, IMI integrates systemically administered, tumor-targeting near-infrared fluorophores with fluorescence-capable imaging platforms to enhance intraoperative decision-making. Early clinical experiences in thoracic surgery suggest particular utility in the localization of small or nonpalpable pulmonary nodules and for improved margin assessment during MIS. Despite encouraging preliminary data, widespread adoption of IMI remains limited by biologic variability in target expression, optical depth constraints, false-positive fluorescence in inflammatory tissue, and challenges in workflow integration. Applications for nodal evaluation, staging, and longer-term oncologic outcome improvement remain investigational. Addressing these multifaceted barriers will be essential for the translation of IMI from a promising, experimental adjunct to a more broadly implementable surgical technology. This work summarizes the current state of IMI in thoracic surgical oncology, highlighting key translational studies, established and emerging clinical applications, and critical limitations within the current landscape. The authors also outline future directions for the field, including quantitative fluorescence interpretation, standardized reporting, and outcomes-driven clinical trials evaluating margin adequacy, recurrence, staging impact, and cost-effectiveness to support widespread evidence-based implementation.