Search Results (566)

Dried blood spots (DBSs) are a practical tool for diagnosing infectious diseases, especially in remote or resource-limited settings. This study assessed the efficacy of DBS-based serological assays for syphilis screening. EDTA blood samples from 171 syphilis-seropositive and 122 seronegative individuals were used to prepare DBSs by spotting whole blood onto filter paper. After drying, 12 mm disks were punched, incubated overnight in buffered solution, and centrifuged. Syphilis serological screening was conducted using the Liaison^® Treponema Screen assay, Macro-Vue™ Reagin Plasma Rapid (RPR) card test, and Dual Path Platform (DPP) Syphilis Screen and Confirm test. The Liaison^® assay demonstrated 100% sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with an optimized cut-off. The nontreponemal RPR test showed very low sensitivity (2.9%) on DBS but perfect specificity (100%). The DPP test for treponemal antibodies achieved high sensitivity (92.1%) and specificity (98.2%) with microreader adjustment. Visual reading of the DPP test had variable accuracy, with sensitivity reaching 100% but lower specificity (42.1%). Nontreponemal antibody detection by DPP showed moderate sensitivity and specificity. Although nontreponemal testing requires refinement, DBS testing combined with point-of-care tests like DPP holds promise for expanding syphilis screening accessibility and decentralization globally, particularly in resource-constrained environments. Full article

Background: Gait analysis plays a key role in detecting and monitoring neurological, musculoskeletal, and orthopedic impairments. While marker-based motion capture (MoCap) systems are the gold standard, their cost and complexity limit routine use. Recent advances in computer vision have enabled markerless smartphone-based approaches. OpenCap, an open-source platform for 3D motion analysis, offers a potentially accessible alternative. This review summarizes current evidence on its accuracy, limitations, and clinical applicability in gait assessment. Methods: A search was performed in major scientific databases to identify studies published from OpenCap’s release in 2023 to June 2025. Articles were included if they applied OpenCap to human gait and reported quantitative biomechanical outcomes. Both validation and applied studies were considered, and findings were synthesized qualitatively. Results: Nine studies were included. Validation research showed OpenCap achieved generally acceptable accuracy kinematics (RMSE 4–6°) in healthy gait, while increased errors were reported for pathological gait patterns. Applied studies confirmed feasibility in different clinical conditions, though trial-to-trial variability remained higher than MoCap, and test–retest reliability was moderate, with minimal detectable changes often exceeding 5°, limiting sensitivity to subtle clinical differences. Conclusions: OpenCap is a promising, low-cost tool for gait screening, remote monitoring, and tele-rehabilitation. Its strengths lie in accessibility and feasibility outside laboratory settings, but limitations in multiplanar accuracy, pathological gait assessment, and kinetic estimation currently preclude its replacement of MoCap in advanced clinical applications. Further research should refine algorithms and standardize protocols to improve robustness and clinical utility. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

Public-health campaigns have to capture and hold visual attention, but little is known about the influence of message framing and visual appeal on attention to bowel-cancer screening ad campaigns. In a within-subjects test, 42 UK adults aged 40 to 65 viewed 54 static adverts that varied by (i) slogan frame—anticipated regret (AR) vs. positive (P); (ii) image type—hand-drawn, older stock, AI-generated; and (iii) identity congruence—viewer ethnicity matched vs. unmatched to the depicted models. Remote eye-tracking measured time to first fixation (TTFF), dwell, fixations, and revisits on a priori pre-defined regions of interest (ROIs); analyses employed linear mixed-effects models (LMMs), generalized estimating equations (GEEs), and median quantile regressions with cluster at the participant level. Across models, the AR slogans produced faster orienting (smaller TTFF) and more intense maintained attention (longer dwell, more fixations and revisits) than the P slogans. Image type set baseline attention (hand-drawn > old stock > AI) but did not significantly decrease the AR benefit, which was equivalent for all visual styles. Identity congruence enhanced early capture (lower TTFF), with small effects for dwell-based measures, suggesting that tailoring benefits only the “first glance.” Anticipated-regret framing is a reliable, design-level alternative to improving both initial capture and sustained processing of screening messages. In practice, the results indicate that advertisers should pair regret-based slogans with warm, human-centred imagery; place slogans in high-salience, low-competition spaces, and, when incorporating AI-generated imagery, reduce composition complexity and exclude uncanny details. These findings ground regret framing as a visual-attention mechanism for public-health campaigns in empirical fact and provide practical recommendations for testing and production. Full article

Climate change is reshaping how forests balance carbon uptake and water loss. This review aims to clarify how climate change alters forest carbon–water coupling. Using water-use efficiency (WUE) as a unifying lens, we synthesize mechanisms from leaves to ecosystems and evaluate evidence from studies screened in 2000–2025 spanning eddy covariance, tree-ring isotopes, remote sensing and models. Globally, tree-ring data indicate ~40% intrinsic WUE increases since 1901, yet ecosystem-scale gains are usually <20% after accounting for mesophyll conductance. Under drought, heat and high vapor-pressure deficit, photosynthesis declines more than evapotranspiration, producing partial carbon–water decoupling and lower WUE_e. Responses vary with hydraulic traits, forest type/age and site water balance, with notable tropical data gaps. We identify when WUE gains translate into true resilience: stomatal regulation and canopy structure jointly maintain GPP, prevent hydraulic failure and ensure post-event recovery. Management options include thinning, species/provenance choice, mixed stands and adaptive rotations to balance carbon storage with water yield. Key uncertainties stem from sparse long-term observations, tropical satellite biases and models that overestimate WUE or underplay extremes. We recommend integrating multi-source, multi-scale data with interpretable hybrid models, expanding tropical networks and strengthening MRV frameworks to support risk-aware, climate-smart forestry. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article

As a staple cereal worldwide, winter wheat plays a pivotal role in food security. Leaf chlorophyll serves as a direct indicator of photosynthetic performance and nitrogen nutrition, making it critical for precision management and yield gains. Consequently, rapid, nondestructive, and high-accuracy remote-sensing retrievals are urgently needed to underpin field operations and precision fertilization. In this study, canopy hyperspectral reflectance together with destructive chlorophyll assays were systematically acquired from Yangling field trials conducted during 2018–2020. Three families of spectral indices were devised: classical empirical indices; two-dimensional optimal spectral indices (2D OSI) selected by correlation-matrix screening; and novel three-dimensional optimal spectral indices (3D OSI). The main contribution lies in devising novel 3D OSIs that combine three spectral bands and demonstrating how their fusion with classic two-band indices can improve chlorophyll quantification. Correlation analysis showed that most empirical vegetation indices were significantly associated with chlorophyll (p < 0.05), with the new double difference index (NDDI) giving the strongest relationship (R = 0.637). Within the optimal-index sets, the difference three-dimensional spectral index (DTSI; 680, 807, and 1822 nm) achieved a correlation coefficient of 0.703 (p < 0.05). Among all multi-input fusion schemes, fusing empirical indices with 3D OSI and training with RF delivered the best validation performance (R² = 0.816, RMSE = 0.307 mg g⁻¹, MRE = 11.472%), and external data further corroborated its feasibility. Altogether, integrating 3D spectral indices with classical vegetation indices and deploying RF enabled accurate, nondestructive estimation of winter wheat chlorophyll, offering a new hyperspectral pathway for monitoring crop physiological status and advancing precision agricultural management and fertilization, can guide in-season fertilization to optimize nitrogen use, thereby advancing precision agriculture. Full article

Background: Remote ischemic conditioning (RIC) has shown promise as a neuroprotective strategy, but its application in children with cerebral palsy (CP) remains unexplored. We conducted a randomized controlled trial to evaluate the feasibility, safety, and tolerability of repeated, 6–7 sessions of RIC in children with unilateral CP. Methods: Fifty-one children aged 6–16 years with unilateral CP were randomized (1:1) to receive RIC or sham conditioning on the more affected arm. Primary feasibility outcomes included recruitment metrics, intervention adherence, retention, and protocol fidelity. Safety endpoints included continuous monitoring of oxygen saturation, blood pressure, heart rate, and adverse event incidence. Tolerability was assessed via child-reported pain ratings, conditioning pressure tolerance, skin integrity evaluations, and session adherence. Results: Of 148 children screened, 51 were randomized to RIC (n = 25), sham (n = 26) groups; 48 (94.1%) completed the intervention as allocated. Recruitment yielded 2.04 participants/month. Intervention adherence was 100% in both groups. RIC was well tolerated, with mean pain scores 2.8 ± 3.1 during inflation in RIC and 0.3 ± 0.8 in Sham group. No serious adverse events occurred. Physiological parameters remained stable across 314 conditioning sessions; no clinically significant hypoxemia, blood pressure derangements, or arrhythmias were detected. Minor adverse events (transient erythema, mild discomfort) were rare (2.22%) and self-limiting. Skin integrity was preserved, and no participants required session termination. Conclusions: Repeated RIC is feasible, safe, and tolerable in children with unilateral CP. These findings support the design of future trials using RIC as a priming agent to enhance pediatric neurorehabilitation outcomes. Full article

Background: This study compared the performance of a Head-mounted Virtual Reality Perimeter (HVRP) with the Humphrey Field Analyzer (HFA), the standard in automated perimetry. The HFA is the established standard for automated perimetry but is constrained by lengthy testing, bulky equipment, and limited patient comfort. Comparative data on newer head-mounted virtual reality perimeters are limited, leaving uncertainty about their clinical reliability and potential advantages. Aim: The aim was to evaluate parameters such as visual field outcomes, portability, patient comfort, eye tracking, and usability. Methods: Participants underwent testing with both devices, assessing metrics like mean deviation (MD), pattern standard deviation (PSD), and duration. Results: The HVRP demonstrated small but statistically significant differences in MD and PSD compared to the HFA, while maintaining a consistent trend across participants. MD values were slightly more negative for HFA than HVRP (average difference −0.60 dB, p = 0.0006), while pattern standard deviation was marginally higher with HFA (average difference 0.38 dB, p = 0.00018). Although statistically significant, these differences were small in magnitude and do not undermine the clinical utility or reproducibility of the device. Notably, HVRP showed markedly shorter testing times with HVRP (7.15 vs. 18.11 min, mean difference 10.96 min, p < 0.0001). Its lightweight, portable design allowed for bedside and home testing, enhancing accessibility for pediatric, geriatric, and mobility-impaired patients. Participants reported greater comfort due to the headset design, which eliminated the need for chin rests. The device also offers potential for AI integration and remote data analysis. Conclusions: The HVRP proved to be a reliable, user-friendly alternative to traditional perimetry. Its advantages in comfort, portability, and test efficiency support its use in both clinical settings and remote screening programs for visual field assessment. Its portability and user-friendly design support broader use in clinical practice and expand possibilities for bedside assessment, home monitoring, and remote screening, particularly in populations with limited access to conventional perimetry. Full article

Soil erosion is an increasingly severe problem and a global focus. As one of the countries facing relatively serious soil erosion, China encounters significant ecological challenges. This study focuses on the carbon sink benefits of comprehensive soil and water conservation management in the red soil erosion area of southern China, conducting an in-depth analysis using the Ziyang small watershed in Shangyou County, Jiangxi Province, as a typical case. Research methods involved constructing an integrated monitoring approach combining basic data, measured data, and remote sensing data. Changes in soil and vegetation carbon storage in the Ziyang small watershed across different years were determined by establishing a baseline scenario and applying inverse distance spatial interpolation, quadrat calculation, feature extraction, and screening. The results indicate that from 2002 to 2023, after 21 years of continuous implementation of various soil and water conservation measures under comprehensive watershed management, the carbon storage of the Ziyang small watershed increased significantly, yielding a net carbon sink of 54,537.28 tC. Tending and Management of Coniferous and Broad-leaved Mixed Forest, Low-efficiency Forest Improvement, and Thinning and Tending contributed substantially to the carbon sink, accounting for 72.72% collectively. Furthermore, the carbon sink capacity of the small watershed exhibited spatial variation influenced by management measures: areas with high carbon density were primarily concentrated within zones of Tending and Management of Coniferous and Broad-leaved Mixed Forest, while areas with low carbon density were mainly found within zones of Bamboo Forest Tending and Reclamation. The increase in watershed carbon storage was attributed to contributions from both vegetation and soil carbon pools. Comprehensive management of soil erosion demonstrates a significant carbon accumulation effect. The annual growth rate of vegetation carbon storage was higher than that of soil carbon storage, yet the proportion of soil carbon storage increased yearly. This study provides a theoretical basis and data foundation for the comprehensive management of soil and water conservation in small watersheds in the southern red soil erosion region of China and can offer technical and methodological support for other soil and water conservation carbon sink projects in this area. Full article

Bilayer transition metal dichalcogenides (TMDs) are promising materials for next-generation field-effect transistors (FETs) due to their atomically thin structure and favorable transport properties. In this study, we employ density functional theory (DFT) to compute the electronic band structures and phonon dispersions of bilayer WS₂, WSe₂, and MoS₂, and the electron-phonon scattering rates using the EPW (electron-phonon Wannier) method. Carrier transport is then investigated within a semiclassical full-band Monte Carlo framework, explicitly including intrinsic electron-phonon scattering, dielectric screening, scattering with hybrid plasmon–phonon interface excitations (IPPs), and scattering with ionized impurities. Freestanding bilayers exhibit the highest mobilities, with hole mobilities reaching 2300 cm²/V·s in WS₂ and 1300 cm²/V·s in WSe₂. Using hBN as the top gate dielectric preserves or slightly enhances mobility, whereas HfO₂ significantly reduces transport due to stronger IPP and remote phonon scattering. Device-level simulations of double-gate FETs indicate that series resistance strongly limits performance, with optimized WSe₂ pFETs achieving ON currents of 820 A/m, and a 10% enhancement when hBN replaces HfO₂. These results show the direct impact of first-principles electronic structure and scattering physics on device-level transport, underscoring the importance of material properties and the dielectric environment in bilayer TMDs. Full article

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article

This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile-based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI—Moisture Index and NDSII_1—Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4 − B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma-radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 76.41% and 85.55% spatial overlap accuracy, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture-controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit the temporal availability of usable scenes. NDGRI offers a rapid, cost-effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi-arid and arid regions. Full article

Background/Objectives: Cancer is a leading cause of death for Aboriginal and Torres Strait Islander people, with remoteness increasing the risk for poorer outcomes. Primary health care (PHC) clinics have an important role in cancer screening, diagnosis, and post-discharge cancer care, particularly in remote communities, so accurate, timely communication between hospitals, specialists and PHC clinics is vital. This paper analyses the perspectives of Northern Territory health care professionals on communication between PHC and hospital services related to providing care for Aboriginal people with cancer and recommends strategies for improving communication between services. Methods: A qualitative study was undertaken in which semi-structured interviews were conducted with fifty staff from 15 health services (8 regional, remote, and very remote PHC clinics; 3 hospitals; one cancer centre and 3 cancer support services) between 2016 and 2019. Transcripts were thematically analysed, with findings categorized into barriers and enablers to communication. Results: Deficiencies in communication impeded patient care and support. A major barrier was fragmented, inefficient information systems; IT systems across health services were unable to interface, resulting in delayed/missing patient information that impacted discharge and follow up. Other barriers included PHC staff with limited knowledge of cancer, high turnover of PHC staff and tertiary hospital staff with limited understanding of remote health care challenges. Individuals used workarounds to overcome system failures and made substantial efforts around individual patients to improve communication. Specific roles and the use of telehealth between services and centralised cancer care services supported better between-service communication. Conclusions: Communication between hospital services and remote PHC clinics is essential to care for Aboriginal cancer patients; our research identified communication as inadequate in terms of consistency and timeliness. Commitment to more timely communication, health care IT systems that facilitate sharing information, designated staff in PHC clinics to support patients with cancer, dedicated Aboriginal cancer roles and additional resourcing to coordinate telehealth appointments could improve communication and sharing of patient information between services. Full article