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Machine learning–assisted optimization of a terahertz photonic metamaterial absorber for blood cancer detection

Blood cancer originates in the bone marrow and disrupts the body’s normal hematopoietic processes. The rapid progression of this disease highlights the need for accurate and sensitive detection to improve treatment outcomes. Here, we present a novel, compact, multi-resonant terahertz photonic metamaterial absorber for blood cancer detection. The compact structure, with dimensions of 0.41 λ 0 × 0.41 λ 0 × 0.029 λ 0 , integrates multiple circular resonators with a rectangular patch, supporting strongly confined resonances that achieve near-unity absorption of 97.6%, 99.9%, and 99.6% at 3.48 THz, 4.95 THz, and 6.01 THz, respectively. The sensing performance was evaluated by introducing an analyte layer representing normal and cancerous blood cells, resulting in remarkable sensitivities of 1.28 THz/RIU, 3.00 THz/RIU, and 2.14 THz/RIU at the three resonance frequencies. The corresponding quality factor (Q-factor) values are 15.03, 20.21, and 24.5, and the figure of merit (FOM) values are 5.54, 12.24, and 8.73 for the three resonance peaks, supporting its reliability in sensing. Moreover, the electric field, magnetic field, and surface current distributions were analysed, and an equivalent circuit model was also developed and validated against the simulated results. Several machine learning models were also employed for design prediction, with Gradient Boosting demonstrating excellent performance and enabling up to a 60% reduction in optimization time. The combination of a multi-band, high-absorption design and ML-assisted approach provides a robust, ultrathin, and high-sensitivity platform, offering a promising route toward next-generation terahertz biophotonic sensors for accurate and sensitive blood cancer detection.

Publisher Correction: Meaning in life improves response to others’ self-promotion

Retraction: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning

First detection of Diplodia bulgarica, a new pathogen causing black canker of apple trees in Poland

Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models

Background As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We examined referral reasons (using large language models [LLM]), wait times, and analyse factors affecting referrals from primary to tertiary care. Methods In 2023, 1,063,646 patient visits from seven primary care clinics in Singapore were analysed. Patient demographics, clinic, physician characteristics, referral volumes and wait times were extracted. LLM Claude 3.5 Sonnet was utilized to identify and classify top referral reasons within the most frequently referred specialties based on referral notes. Chi-square tests identified differences in referral rates among categorical variables, while a generalised linear model (GLM) with an identity link (normal distribution) determined factors influencing referrals by physicians. Findings Around 1 in 5 visits resulted in a referral (n = 210,839, 19.8%), achieving 76.0% attendance rate. Referrals peaked among patients aged 60–70 years. Male (Odds ratio [OR] 0.88, 95% Confidence interval [CI] 0.87–0.89) and Malay (OR 0.71, 95% CI 0.70–0.72, compared with Chinese) patients were less likely to be referred. Significant variations were observed among clinics (p < 0.001). Ophthalmology (11.1%), orthopaedic surgery (10.3%), and emergency (10.0%) were the most referred specialties, with blurred vision (n = 7,461), abnormal diabetic retinopathy screening (n = 5,266) and pregnancy and antenatal care (n = 3,959) being the top referral reasons. 51.5% were routine referrals. Wait time averaged 52.7 days with 48.9% meeting targets, with long wait times for Gastroenterology & Hepatology, and Endocrinology. On average, each additional year of physician experience was associated with a reduction of 4.45 referrals per physician (95% CI: 1.40–7.58, p = 0.005). Interpretation Our study highlighted disparities in referrals rates, patterns, and wait times. Continuing education and support for primary care is paramount. Resource allocation should be tailored to meet the population needs, with further research needed to ensure timely and appropriate referrals.

Enhanced lycopene fortification in eggs using poultry feed containing a carotenoid-oil preparation derived from metabolically engineered baker’s yeast

Evaluation of oxygenation indices incorporating SpO₂ and PEEP for assessing ARDS severity: Evidence from the MIMIC-IV and eICU collaborative research database v2.0 databases

Background The in-hospital mortality of acute respiratory distress syndrome can reach 35–45%, with patients requiring a more convenient and accurate way to assess the disease condition, which can change even more rapidly in patients undergoing mechanical ventilation in intensive care units. Methods Eligible patients in MIMIC-IV v3.0and eICU Collaborative Research Database v2.0were screened by the Berlin definition to examine the comparison of the diagnostic abilities of SpO 2 *10/FiO 2 *PEEP (S/F*P), PaO 2 *10/FiO 2 *PEEP (P/F*P), and SpO 2 / FiO 2 (S/F), with nine types of machine learning performed on S/F*P for 10 cross-validations, validating the diagnostic ability of the models for ARDS patients. Results ROC_AUC = 0.700(95 CI:0.624 ~ 0.777) for S/F, ROC_AUC = 0.720(95 CI:0.668 ~ 0.772) for P/F*P, and ROC_AUC = 0.761(95 CI:0.693 ~ 0.830) for S/F*P showed that S/F had a better fit in diagnosing ARDS with slightly inferior efficacy to P/F*P, had superior diagnostic efficacy after incorporating peep into S/F, and S/F*P showed good diagnostic efficacy in 9 machine learning and 10 cross-validations. In terms of predicting the prognosis of patients, the ability of S/F*P is not as good as S/F, but the grading of S/F*P has a more positive significance for the evaluation of the prognosis of patients. Conclusion S/F*P provides a more convenient judgment for mechanically ventilated patients, avoiding the phenomenon of clinical diagnosis of PEEP and oxygenation index separation as much as possible, minimizing invasive operation of patients and improving the selection of ARDS treatment modalities. Therefore, S/F*P provides a reference for the early treatment of ARDS in the clinic to improve the resource allocation in the ICU and reduce the mortality of patients, Given all patients had ARDS diagnosis, this study evaluated relative diagnostic performance among indices rather than disease vs. non-disease discrimination.

LIMK2 inactivation suppresses mechanical stimulation-induced dermal fibroblast differentiation and resistance to apoptosis

Model of leaf biomass partitioning coefficient in different main stem leaf ranks of rapeseed (Brassica napus L.)

Leaf growth is a dynamic process that critically determines canopy architecture and assimilate allocation in rapeseed. Quantifying the distribution of leaf biomass along the main stem across developmental stages is essential for advancing functional-structural plant models of rapeseed. To address the lack of a leaf biomass partitioning model in existing rapeseed growth models, this study developed a rank-specific leaf biomass partitioning coefficient model for the main stem in rapeseed. Field experiments were conducted over three growing seasons (2012–2015) using three cultivars: Ningyou 18 (V1, conventional), Ningyou 16 (V2, conventional), and Ningza 19 (V3, hybrid). The experiments were conducted under factorial combinations of cultivar, nitrogen fertilizer, and transplanting density. The leaf biomass partitioning coefficient was calculated as the ratio of leaf biomass at a given leaf rank to the total main-stem leaf biomass, with leaf ranks normalized to the (0–1] interval. Model parameters were estimated to elucidate how cultivar and environmental factors influence partitioning patterns across leaf positions. Validation using independent experimental data showed strong agreement between the simulated and observed values, with a correlation coefficient ( r ) more than 0.9 ( p  < 0.001). The mean absolute difference ( d a ) ranged from −0.080 to 0.011 g g -1 , and the ratio of d a to the average observation ( d ap ) varied between 3.077% (anthesis stage) and 13.083% (normalized leaf rank). The root mean square error ( RMSE ) values were all below 0.193 g g -1 across all stages, with the most stage-specific RMSE values under 0.032 g g -1 . The results demonstrate that the model performs reliably in simulating the main-stem leaf biomass partitioning coefficient across hierarchical leaf ranks in rapeseed. By integrating leaf-level biomass allocation with whole-plant growth processes, this work provides a key component for developing a functional-structural rapeseed model and supports further research on source-sink regulation and canopy optimization.

Study on biomarkers of homocysteine-induced transformation of vascular smooth muscle cells into foam cells

The association between parental resilience and emotional/behavioural problems in children with autism spectrum disorders: The mediating role of parenting style

Parental psychological resilience plays a crucial role in addressing children's emotional and behavioral problems. However, the association between parental psychological resilience and emotional/behavioral problems of children with Autism Spectrum Disorders (ASD) has been less explored. This study surveyed 258 parents of children with ASD(aged 3–18) who were receiving training at rehabilitation institutions in Shandong Province, China, using questionnaires. Data were analyzed using structural equation modeling to examine the association between parental psychological resilience and emotional/behavioral problems in children with ASD and to identify the underlying pathways. The results indicated that parental psychological resilience is associated with increased prosocial behavior in children with ASD through increased authoritative parenting, while simultaneously being associated with fewer emotional/behavioral problems by reducing permissive and authoritarian parenting styles. This study provides empirical support for family-focused ASD interventions and adds to the growing body of evidence on their effectiveness.

FedSCOPE: Federated cross-domain sequential recommendation with decoupled contrastive learning and privacy-preserving semantic enhancement

Abstract Cross-domain sequential recommendation (CDSR) models users’ dynamic preferences by exploiting behavioral signals from multiple domains, but it faces challenges in data sparsity, domain heterogeneity, and privacy protection. Although federated learning enables privacy-preserving CDSR by keeping raw data local, existing methods often suffer from sparse representations, unstable cross-domain alignment, and severe utility degradation under uniform differential privacy. In this work, we propose FedSCOPE, a novel federated CDSR framework that addresses these challenges through three tightly coupled and explicitly aligned components. First, FedSCOPE enriches user and item representations via offline large language model (LLM)-generated semantic augmentation, mitigating sparsity while avoiding online LLM inference and the associated privacy and deployment risks. Second, it introduces an Intra- and Inter-Domain Decoupled Contrastive Learning mechanism that separates intra-domain personalization from inter-domain discrimination, enabling robust cross-domain alignment under heterogeneous data distributions. Third, FedSCOPE incorporates an adaptive personalized differential privacy strategy that dynamically allocates privacy budgets and clipping thresholds according to client-specific data characteristics, achieving a more favorable privacy–utility trade-off in federated environments. These components are jointly optimized within a secure federated learning framework. Extensive experiments on multiple real-world datasets demonstrate that FedSCOPE consistently outperforms state-of-the-art baselines, achieving higher recommendation accuracy, stronger cross-domain generalization, and improved privacy–utility balance.

Experimental and ANN-based analysis of performance, combustion, and emission characteristics of a CI engine fueled with waste plastic oil–diethyl ether–diesel blends

This study differs fundamentally from prior investigations on WPO–diesel and WPO–DEE blends by combining combustion-resolved experimentation with predictive modeling, thereby advancing WPO utilization from empirical testing toward optimization-oriented engine integration. It examines the performance, combustion, and emission characteristics of a single-cylinder variable compression ratio (VCR) diesel engine fueled with ternary blends of diesel, waste plastic oil (WPO), and diethyl ether (DEE). WPO was produced via catalytic pyrolysis of LDPE waste and blended with diesel at 15%, 20%, 25%, and 30% by volume, while DEE was maintained at a constant 10% to improve ignition quality, volatility, and atomization. Engine tests were performed at a constant speed of 1500 rpm under variable loads ranging from 2 to 12 kg to evaluate the influence of blend composition and operating conditions on brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), combustion development, and regulated emissions (CO, HC, NOx, CO₂). The D65B25DE10 blend (65% diesel, 25% WPO, 10% DEE) demonstrated the best overall performance among the tested fuels, achieving a 22.22% reduction in CO and an 11.88% reduction in HC emissions compared with diesel, although BTE decreased by 6.93% and BSFC increased by 6.03% at full load. Combustion analysis revealed extended ignition delay and higher peak cylinder pressure for higher-WPO blends, while DEE improved vaporization and supported more complete oxidation. To complement the experimental work, a feed-forward artificial neural network (ANN) model with a 6-12-6 architecture was developed using blend ratio, load, compression ratio, and speed as inputs to predict BTE, BSFC, and emissions. The ANN achieved strong correlation with experimental data (R 2  > 0.97), confirming its suitability for performance prediction and blend optimization. The combined experimental and computational approach offers a comprehensive framework for evaluating WPO-based fuels, extending beyond previous binary blend studies by revealing the synergistic effects of DEE in ternary blends and establishing a robust ANN model for predictive optimization. This methodology demonstrates the potential of WPO-based fuels to reduce fossil diesel dependence while promoting sustainable waste-to-energy utilization.

A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil

Abstract Spatiotemporally resolved ambient temperature data are essential for environmental epidemiology, especially in urban areas where temperature can vary sharply over short distances, influencing population exposure. Additionally, heat distribution often reflects built environment patterns and may correlate with existing social and environmental disparities. Continuous temporal records at high spatial resolution are, however, often lacking, especially in low- and middle-income countries. We developed a generalizable tree-based machine learning approach to estimate daily mean temperatures at 500 × 500 m resolution using São Paulo, a megacity in Brazil, as a case study, to demonstrate its utility in highly urbanized settings with a heterogeneous urban fabric and unevenly distributed temperature monitoring stations. We trained a Random Forest model using open-access remote sensing data, along with derived products, and temperature measurements from 43 ground stations. To prevent overfitting and select relevant features, we employed a forward feature selection algorithm with target-oriented (spatial) cross-validation. Hyperparameter tuning was performed using grid search approach. The model was validated through ten-fold station-based cross-validation and an external hold-out dataset. The model demonstrated strong performance (RMSE RF = 0.80; R 2 RF = 0.95), with slightly reduced accuracy in rural areas (R 2 rural = 0.91; R 2 urban = 0.95). Compared to traditional multilinear approaches (RMSE MLR = 1.02; R 2 MLR = 0.92), the Random Forest model outperformed, likely due to its ability to better capture microclimates and complex relationships between data sources. This 500 × 500 m daily temperature dataset is the first of its kind in South America, with the São Paulo pipeline and data freely accessible. The approach is adaptable to other regions with appropriate retraining and validation, enabling high-resolution exposure assessments.

GNSS/SINS/DVL integrated navigation algorithm based on adaptive differential Kalman filtering

The global navigation satellite system/strapdown inertial navigation system/doppler velocity logger (GNSS/SINS/DVL) integrated navigation system leverages the complementary advantages of its three subsystems to provide essential navigation information—such as attitude, velocity, and position—for carriers operating in marine environments. However, unmanned underwater vehicle (UUV) faces challenges like observation anomalies and dynamic model inaccuracies during dynamic maritime navigation and positioning. These issues make it difficult for the standard Kalman filter (KF) to cope with the complexities of the ocean environment, thereby reducing the accuracy of navigation parameter estimates. To address this, this study introduces an adaptive differential Kalman filtering (ADKF) method for processing integrated navigation data. Experimental results indicate that, compared with the KF, the proposed algorithm significantly enhances the accuracy and stability of parameter estimation, making it well-suited for post-processing integrated navigation data in complex marine settings.

Prevalence of Alzheimer’s disease pathology in the community

Abstract The prevalence of Alzheimer’s disease neuropathological changes (ADNCs), the leading cause of cognitive impairment, remains uncertain. Recent blood-based biomarkers enable scalable assessment of ADNCs 1 . Here we measured phosphorylated tau at threonine 217 in 11,486 plasma samples from a Norwegian population-based cohort of individuals over 57 years of age as a surrogate marker for ADNCs. The estimated prevalence of ADNCs increased with age, from less than 8% in people 58–69.9 years of age to 65.2% in those over 90 years of age. Among participants aged 70 years or older, 10% had preclinical Alzheimer’s disease, 10.4% had prodromal Alzheimer’s disease and 9.8% had Alzheimer’s disease dementia. Furthermore, among those 70 years of age or older, ADNCs were present in 60% of people with dementia, in 32.6% of those with mild cognitive impairment and in 23.5% of the cognitively unimpaired group. Our findings suggest a higher prevalence of Alzheimer’s disease dementia in older individuals and a lower prevalence of preclinical Alzheimer’s disease in younger groups than previously estimated 2 .

Histatin-1 promotes the expression of markers associated with odontoblastic differentiation in the dental pulp and apical papilla

Symptom burden, viral load, and antibody response to ancestral SARS-CoV-2 strain [D614G] in an outpatient household cohort

Background Early in the SARS-CoV-2 pandemic, description of COVID-19 illness among non-hospitalized patients was limited. Data from household cohorts can help reveal the full spectrum of disease and the potential for long-term sequelae, even in non-severe disease. Methods Daily symptom diaries were collected in a US household cohort of SARS-CoV-2 infection from April to November 2020, during the pre-COVID vaccine period. SARS-CoV-2 nasal viral loads were measured at study entry and weekly until day 21; serologic testing was performed at study entry and day 28. A subset of volunteers underwent an additional assessment 8–10 months later. Participants who met the criteria for early infection—testing antibody-negative at study entry but PCR-positive either at baseline or during follow-up—were included in this analysis (n = 143). Results Daily symptoms were ascertained in 143 outpatients with acute COVID-19, including 60 index cases who sought testing and 83 of their household contacts. Asymptomatic cases comprised 16% (13/83) of SARS-CoV-2 infections detected among household contacts. Among 119 persons with mild or moderate illness, the number of symptoms peaked 3 or 4 days after symptom onset. Fever and anosmia occurred in nearly half of participants. Symptom severity was associated with increased age, viral load, and cardiovascular disease. Increased BMI was associated with a higher antibody level at day 28, independent of symptom severity. Those with a higher day 28 antibody level were more likely to develop symptoms consistent with post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID-19, 8–10 months later. Conclusions Fever, anosmia, as well as asymptomatic infection were common features of COVID-19 non-severe illness when the D614G variant circulated in the US, before the availability of vaccines or outpatient therapies. Antibody levels following acute infection were linked to the development of symptoms of PASC 8–10 months later.

Dual band Notched 2-port UWB MIMO antenna reconfiguration using lumped capacitors

Abstract In this paper, a dual port reconfigurable band-notched UWB MIMO antenna is presented to reduce the interference with WLAN and WiMAX applications. The suggested MIMO antenna is designed on RO 4350 substrate with partial ground plane for achieving the UWB frequency range with a band-notch at 5.4 GHz to avoid interference with WLAN application. An isolation structure is merged with the partial ground plane to achieve an isolation better than 17 dB between the two elements. Two pairs of lumped capacitors are embedded in the band stop resonators for achieving frequency reconfigurability by shifting the band stop behavior from 5.4 GHz to 3.5 GHz for interference mitigation with WiMAX application. The suggested MIMO antenna is experimentally fabricated and tested to validate the obtained simulated results since a good consistency between both results is achieved not only the impedance characteristics, but also the radiation and diversity outcomes. The new contribution of presented MIMO antenna is evident when it compared with state-of-the-art antennas which confirms the ability of the fabricated model to be utilized for various wireless communication applications in the microwave frequency range.

Visualizing vastness: Graphical methods for multiverse analysis

Multiverse analysis is an increasingly popular tool for improving the robustness and transparency of empirical research. Yet, visualization techniques for multiverse analysis are underdeveloped. We identify critical weaknesses in existing multiverse visualizations—specification curves and density plots—and introduce a novel alternative: multiverse plots. Using both simulated and real-world data, we illustrate how multiverse plots can retain detailed information even in the face of thousands of model specifications. Multiverse plots overcome key limitations of existing methods by eliminating arbitrary sampling (a common issue with specification curves) and information loss on analytical decisions (an issue with density plots). Furthermore, they effectively show what conclusions a dataset can reasonably support and which researcher decisions drive variation in results. By providing software code to generate multiverse plots in Stata and R, we enable analysts to visualize multiverse results transparently and comprehensively.