Browse Articles

Harnessing screw dislocations in shell-lattice metamaterials for efficient, stable electrocatalysts

Abstract Developing highly active and robust catalysts remains a critical challenge for the industrial realization and implementation of nitrate reduction. Here, we proposed a screw dislocation-mediated three-dimensional (3D) printing strategy for scalable, integrated manufacturing of metamaterial catalysts. Specifically, screw dislocation was introduced into the 3D printing process to mediate the simultaneous synthesis of 3D architecture and chiral surface nanostructures, effectively eliminating conventional heterointerfaces. Additionally, severe strain effects induced by dislocation multiplication in curved spaces enhance intrinsic catalytic activity by promoting NO3 − adsorption and lowering the energy barrier of NO3 −-to-NH3 conversion. Consequently, the FeCoNi dual-scale shell-lattice metamaterials with high dislocation density achieve a Faraday efficiency of 95.4%, an NH3 yield rate of 20.58 mg h−1 cm−2, and long-term stability exceeding 500 hours. A flow-through electrolyzer coupled with an acid absorption unit successfully produced NH4Cl fertilizer products. Our work opens a new perspective for advancing 3D printing technology in catalysis applications.

Modeling interconnected minerals markets with multicommodity supply curves: examining the copper-cobalt-nickel system

Abstract Demand for many of the metals used in the energy transition is expected to grow rapidly. Many of these are by-products, often considered critical because their production responds weakly to prices and is instead tied to the economics of the host mineral. We present a model of prices and production for jointly produced commodities that accounts for interconnectivity between host and by-product markets at the mine level. We demonstrate this method using the copper–cobalt–nickel system, in which approximately 99% of cobalt is a by-product of copper or nickel mining. Our results show that the model more accurately captures the economic benefits of diversified mine outputs than previous approaches. Furthermore, changes in demand drivers for any two commodities produce non-linear effects on production and price. We challenge the prior best-practice assumption that cobalt cannot impact the copper or nickel markets. Recognizing the importance of both copper and cobalt for future electrification, we emphasize that incentivizing the copper industry to reduce cobalt supply risks could inadvertently undermine copper supply.

Highly efficient non-relativistic Edelstein effect in nodal p-wave magnets

Abstract The origin and efficiency of charge-to-spin conversion, known as the Edelstein effect (EE), has been typically linked to spin-orbit coupling mechanisms, which require materials with heavy elements within a non-centrosymmetric environment. Here we demonstrate that the high efficiency of spin-charge conversion can be achieved even without spin-orbit coupling in the recently identified coplanar p-wave magnets. The non-relativistic Edelstein effect (NREE) in these magnets exhibits a distinct phenomenology compared to the relativistic EE, characterized by a strongly anisotropic response and an out-of-plane polarized spin density resulting from the spin symmetries. We illustrate the NREE through minimal tight-binding models, allowing a direct comparison to different systems. Through first-principles calculations, we further identify the nodal p-wave candidate material CeNiAsO as a high-efficiency NREE material, revealing a  ~ 25 times larger response than the maximally achieved relativistic EE and other reported NREE in non-collinear magnetic systems with broken time-reversal symmetry. This highlights the potential for efficient spin-charge conversion in p-wave magnetic systems.

Galectin-3-integrin α5β1 phase separation disrupted by advanced glycation end-products impairs diabetic wound healing in rodents

Constructing concepts without feedback: An empirical investigation of how relational information affects multidimensional concept completion behavior in an unsupervised task

The ability of humans to intentionally learn, without feedback, unidimensional stimulus relations in categorization tasks has been empirically established over the past two decades. However, whether observers can learn more complex multidimensional stimulus relations across these unsupervised tasks has not yet been determined. We demonstrate across an unsupervised concept completion experiment that the failure to observe multidimensional learning in previous experiments may be attributable to factors such as increased stimulus or task complexity. We posit that concept completion is related to category learning in that it reveals the underlying tendencies that are associated with some categories being easier to learn than others. In our experiments, we found observers readily learned to complete a two-dimensional exclusive-or concept, evidenced by an increase in object selection as the task progressed with a decrease in choice response times. We also found that observers readily learned to complete, as evidenced by similar patterns in object selection and response time behavior, a more complex three-dimensional stimulus relation that has empirically been associated with large amounts of categorization errors in related supervised classification tasks. Accordingly, we tested two existing formal models to determine their ability to account for our observations: namely, the Simplicity Model and the Generalized Representational Information Theory (GRIT) basic measure. We show how relational information processing as expounded in GRIT accounts for the observed completion behavior. Overall, our findings show how people gravitate, in a gradual and composite fashion, towards minimizing the perceived complexity of categories as much as possible.

Wasting and short-term outcomes among children with cancer in resource-limited settings: A prospective study in Uganda

Background Wasting contributes to poor treatment outcomes in children with cancer, especially in low-resource settings. In these settings, there is inadequate routine, systematic assessment of the wasting status of children with cancer. Wasting is diagnosed based on visual evidence, with a subjective bias for recognition. This study determined the prevalence of wasting at diagnosis among children with cancer at the Uganda Cancer Institute (UCI) and the diagnostic accuracy of “visible wasting” in identifying children with wasting as measured by anthropometric indices, and identified predictors of 6-months negative outcomes. Methods We assessed the wasting status at diagnosis, diagnostic accuracy of visible wasting, and 6-month outcomes of children newly diagnosed with cancer at the UCI (both ambulatory and hospitalized) between April 2022 and March 2023. Data were analyzed using SPSS version 26. Descriptive, bivariate, multivariate, and survival analyses were performed as appropriate. Statistical significance was determined at P-value<0.05. Results One hundred forty-four children with cancer, with a median age of 10.0 years (interquartile range [IQR] 4.0–14.0 years), were included. The majority, 89 (61.8%), had solid tumor, whereas 55 (38.2%) had hemato-lymphoid malignancies. Thirty-two (22.2%) of the participants had visible wasting, and 57 (39.6%) were wasted based on anthropometric measurements, 32 (56.1%) of whom showed no visible wasting. Visible wasting had a low sensitivity of 43.9% (95% CI 30.7–57.6) – ROC 0.32 (95% CI 0.23–0.42), with a false negative rate of 56.1%. Overall, visible wasting missed up to 80.6% (25/31) of children with moderate wasting and 26.9% (7/26) with severe wasting. Twenty-one (14.6%) of the patients died, 8 (38.1%) of whom were deemed to be wasted, and 15 (71.4%) had anthropometrically-defined wasting. Neutropenia occurred in 20.8% (n = 30) of the participants and sepsis in 13.9% (n = 20). In univariate analyses, wasted patients were more likely to develop neutropenia (OR 3.63; 95% CI 1.56–8.42; p = 0.003), sepsis (OR 4.50; 95% CI 1.65–12.29; p = 0.003), and die (OR 3.08; 95% CI 1.15–8.28; p = 0.026). Conclusion Wasting at diagnosis is a common problem among children with cancer in this resource-limited setting and is associated with increased risks of neutropenia, sepsis, and mortality. Reliance on visible wasting as a marker for wasting misses other wasted children, some of who may be malnourished and at risk of poor outcome. For accurate categorization of wasting, all patients should undergo a standard anthropometric evaluation.

Mobile-collector capture of particles in a chaotic flow

Removing dispersed material, such as pollutants, from dynamic fluid environments like the ocean or the atmosphere is challenging when the flow is chaotic. Here the capture of passive tracer particles by a mobile collector (MC) is studied in a model two-dimensional chaotic flow with vortices. Four simple capture strategies for determining the MC direction are considered, all of which rely on periodic measurement of the local particle distribution. The ultimate success of a strategy depends on its associated motion and detection parameters as well as the underlying fluid flow. When the flow is fully chaotic or the relative velocity of the MC is large, the four strategies exhibit nearly equal effectiveness. However, when the flow is less chaotic and the relative MC velocity is small, the collector can become trapped in or outside of a vortex. Changing the particle detection parameters can prevent trapping, which improves capture. In the absence of trapping and for both high and low relative velocities of the MC, a scaling analysis explains the dependence of the capture rate on the relevant dimensionless variables based on timescales for the mobile collector and the underlying flow. For a wide range of parameters and all four capture strategies, the capture timescale depends linearly on a combination of the characteristic kinematic timescale related to the relative motion of the collector and the gradient timescale related to the underlying flow field, confirming that the capture process is properly characterized.

Disability disclosure in healthcare settings for individuals with developmental disabilities: A qualitative study of patient and caregiver perspectives

Background People with disabilities experience significant healthcare disparities, including missed opportunities for preventive, inaccessible services, and inadequate communication with providers. These challenges often lead to unmet healthcare needs and poor health outcomes. Disability disclosure is one strategy that may aid in closing this healthcare equity gap, though limited research sheds light on patient and caregiver feelings towards and preferences for disclosure. Objective This study assessed comfort with and preferences for disability disclosure within healthcare settings among individuals with developmental disabilities and caregivers of individuals with developmental disabilities. Methods An exploratory qualitative research design was employed, utilizing semi-structured interviews with 22 participants (10 patients and 12 caregivers) in South Florida. Data were transcribed and analyzed through thematic analysis to identify key themes related to disability disclosure in healthcare settings. Results Five main themes emerged. Two themes centered on the downside of disclosure (harm avoidance and disclosure utility), while two themes illuminated the upside of disclosure (disclosure necessity and reduced stigma). The final theme focused on disclosure preferences. Conclusions Comfort with disability disclosure among patients and caregivers was largely motivated by a desire to avoid perceived pitfalls and secure quality healthcare. Findings confirm the persistence of inadequate healthcare delivered to patients with disabilities, and the beneficial role disability disclosure can play in addressing current deficiencies. With support of healthcare system leadership and other salient stakeholder groups, further research can inform development, implementation, and evaluation of disclosure systems that facilitate equitable care delivery and improve health outcomes among patients with developmental disabilities.

Advancing smart communities with a deep learning framework for sustainable resource management

Background The rapid development of urban systems and rising requirements for sustainable development lift resource management issues in smart communities. A fundamental problem for contemporary communities involves effectively using energy and water resources and waste management systems under environmental limitations. Artificial intelligence (AI) techniques at an advanced level deliver new methods that optimize resource management systems. Objective The research builds and examines a deep-learning framework that optimizes the management of smart community resources. The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. The system focuses on two main objectives, which include better forecasting precision, optimum resource distribution, and efficient detection of operational problems. Methods Research validation employed data from the Amsterdam Open Data Platform and Singapore Government Open Data Portal joined by crowdsourced platforms FixMyStreet and OneService. The preprocessing phase involved three stages, i.e., cleaning and normalization and feature engineering steps, before model training and testing phases. Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². A comparison with traditional methods revealed the proposed approach delivered superior performance results. Results The deep learning framework demonstrated superior performance, achieving an average reduction of 18.7% in resource consumption and a 16.2% reduction in operational costs. The models outperformed baseline methods, with LSTMs achieving an MAE of 1.8 for water demand prediction and autoencoders detecting anomalies with an F1-score of 95.5%. Conclusion Due to its effective capabilities, the proposed framework solves challenges in resource management for smart communities while showing the potential of AI-driven solutions for sustainable urban development. Research results demonstrate that integrating sophisticated deep-learning methods yields more significant potential for optimizing resource utilization while improving operational effectiveness.

Reducing bias in coronary heart disease prediction using Smote-ENN and PCA

Coronary heart disease (CHD) is a major cardiovascular disorder that poses significant threats to global health and is increasingly affecting younger populations. Its treatment and prevention face challenges such as high costs, prolonged recovery periods, and limited efficacy of traditional methods. Additionally, the complexity of diagnostic indicators and the global shortage of medical professionals further complicate accurate diagnosis. This study employs machine learning techniques to analyze CHD-related pathogenic factors and proposes an efficient diagnostic and predictive framework. To address the data imbalance issue, SMOTE-ENN is utilized, and five machine learning algorithms—Decision Trees, KNN, SVM, XGBoost, and Random Forest—are applied for classification tasks. Principal Component Analysis (PCA) and Grid Search are used to optimize the models, with evaluation metrics including accuracy, precision, recall, F1-score, and AUC. According to the random forest model’s optimization experiment, the initial unbalanced data’s accuracy was 85.26%, and the F1-score was 12.58%. The accuracy increased to 92.16% and the F1-score reached 93.85% after using SMOTE-ENN for data balancing, which is an increase of 6.90% and 81.27%, respectively; the model accuracy increased to 97.91% and the F1-score increased to 97.88% after adding PCA feature dimensionality reduction processing, which is an increase of 5.75% and 4.03%, respectively, compared with the SMOTE-ENN stage. This indicates that combining data balancing and feature dimensionality reduction techniques significantly improves model accuracy and makes the random forest model the best model. This study provides an efficient diagnostic tool for CHD, alleviates the challenges posed by limited medical resources, and offers a scientific foundation for precise prevention and intervention strategies.

Daily briefing: Lithium supplements reverse Alzheimer’s symptoms in mice

1-4-2: Evaluation of applied mechanical power to individual lungs in a simulator-based setting of one ventilator for two patients

Background The concept of ventilating multiple patients concurrently using a single ventilator has been proposed as a solution when the demand for ventilators surpasses the available supply. While the practicality of this approach has been established, a thorough evaluation of the risks involved has yet to be comprehensively addressed. Methods Two circuits, a simple one (circuit-1) and another with an adjustable resistance valve (circuit-2), were evaluated within an experimental framework utilizing two computer-controlled lung simulators (TestChest and ASL 5000). These simulators were ventilated by an ICU (intensive care unit) ventilator (Servo-u) employing various ventilation modes (volume- and pressure-controlled ventilation). The study was conducted under differing respiratory conditions, characterized by low compliance (20 ml/cmH2O) as well as normal-high compliance (100 ml/cmH2O), in order to ascertain the applied tidal volume (VT), pressures, and the resultant mechanical power (MP). Results Circuit-1: The applied VT, pressures, and MP differed significantly between the two simulators, as well as in relation to ventilation mode, compliance, and respiratory rate (RR) (p < 0.001); the differences were most pronounced in settings with differing compliance levels. Circuit-2: Differences in VT, pressures, and MP were observed between simulators concerning valve settings (p < 0.001). The VT demonstrated a negative correlation, with volumes derived from valve closure spanning from 50 to 100 ml across all settings. In the design of circuit-2, MP exceeded the 12 J/min threshold in both lung simulators at elevated RR and could only be decreased through valve closure followed by a consequential hypoventilation in one simulator. Conclusion The simultaneous ventilation of two patients using a single ventilator is technically viable, yet it presents considerable risks. Even with the integration of an adjustable resistance valve to accommodate varying lung complexities, the likelihood of unilateral hypoventilation and elevated mechanical stress remains high.

Expanding the identification of key resource combinations for mid- to long-term growth in electric vehicle market entry

This study examines the key resource combinations influencing electric vehicle (EV) adoption, differentiating between short-term market entry and mid- to long-term growth, using the Technology-Organization-Environment (TOE) framework and the Resource-Based View (RBV). Analyzing 12 companies from 2012 to 2022, we find that firms with a well-balanced combination of technological capabilities, organizational strategies, and environmental adaptability achieve sustained market diffusion. A deficiency in any one of these areas hinders long-term success, and excellence in a single resource alone is insufficient. Moreover, the resource requirements for initial market entry differ from those needed for sustained growth, highlighting the need for firms to dynamically adjust their resource strategies based on evolving technological advancements, organizational capabilities, and regulatory and market conditions. Theoretically, this study integrates innovation diffusion theory, RBV, and TOE to offer a comprehensive perspective on EV market dynamics. From a managerial standpoint, companies must develop technological advancements, organizational capabilities, and environmental adaptability to ensure long-term success. Policy-wise, governments can accelerate EV adoption by implementing targeted infrastructure investments, standardizing digital and charging networks, and supporting sustainable innovation through incentives and regulatory frameworks.

These genes can have the opposite effects depending on which parent they came from

FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices

The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git.

Factors associated with intubation and heated high-flow nasal cannula use in hospitalized respiratory syncytial virus infected children: A single-center retrospective cohort study

Background Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract illness (LRTI) in children, often requiring hospitalization and respiratory support. This study, therefore, aims to identify factors associated with intubation and heated high-flow nasal cannula (HHFNC) use in children hospitalized with RSV infection. Methods This retrospective study reviewed medical records of children aged 0 month to 15 years hospitalized with RSV infection at Chiang Mai University Hospital between January 2018 and December 2022. Baseline characteristics, clinical features, and laboratory findings were analyzed. Factors associated with intubation or HHFNC use were analyzed using univariable and multivariable logistic regression with significance set at p < 0.05. Result Among 260 children (53.8% male; median age 28 months, IQR 12–44), 76.5% required low-flow oxygen therapy, 11.5% required HHFNC, and 11.9% required intubation, respectively. Prematurity (22.7%) and respiratory comorbidities (17.6%) were common. HHFNC use was significantly associated with prematurity (adjusted odds ratio [aOR] 3.11, p = 0.016), chest retractions (aOR 5.42, p = 0.017), and multi-lobar infiltrates on chest X-ray (aOR 7.52, p < 0.001). Factors associated with intubation included age ≤ 2 years (aOR 3.70, p = 0.008), prematurity (aOR 5.68, p < 0.001), chest retractions (aOR 4.39, p = 0.033), and multi-lobar infiltrates (aOR 8.83, p < 0.001). Conclusions Prematurity, younger age, chest retractions, and multi-lobar infiltrates on chest X-ray were key predictors for HHFNC and intubation in RSV-infected children. These findings may inform risk stratification and management strategies for severe RSV-related illness in pediatric patients.

The peer-review crisis: how to fix an overloaded system

Safety assessment of Osilodrostat: The adverse event analysis based on FAERS database by means of disproportionality analysis

Background Osilodrostat is a medication recently approved for the treatment of Cushing’s syndrome. However, there is a current dearth of large-scale studies on the adverse events associated with Osilodrostat. Consequently, this study aims to comprehensively evaluate these adverse events using data from the FDA Adverse Event Reporting System (FAERS). Methods A disproportionality analysis was utilized to identify signals of adverse events linked to Osilodrostat. Furthermore, a Weibull distribution analysis was conducted to evaluate the temporal evolution of adverse events, and subgroup analyses were performed. The Wilcoxon test was applied to investigate differences in the temporal patterns of adverse events across different genders. Results A total of 1,078 cases related to Osilodrostat were identified, including 3,744 adverse events. The most frequent and severe signals of adverse events were investigations, off-label use, fatigue, nausea, and adrenal insufficiency. The median time to onset of adverse events related to Osilodrostat was 52 days after starting the medication. There was a gender difference in the median time to onset of adverse events, with a median of 15 days for males and 34 days for females. Conclusion This study provides a comprehensive evaluation of adverse events related to Osilodrostat, confirming some known side effects and revealing other potential risks. This information offers valuable insights for the clinical application of Osilodrostat.

Cytokines interferon−γ− inducible protein 10 and granulocyte−macrophage colony−stimulating factor are associated with psychiatric symptoms in opioid−dependent patients: A cross− sectional study

Background Psychiatric disorders and chronic hepatitis virus C infection are known to alter blood cytokines levels. However, little is known about the association between cytokines and psychiatric symptoms in patients with chronic hepatitis C virus infection. This study aimed at exploring this association. Moreover, since nearly half of the patients receive opioid maintenance treatment, we also investigated if long−term opioid treatment had any impact on these associations. Methods We conducted a cross−sectional study on 120 outpatients referred for antiviral hepatitis C treatment. Serum level of 27 cytokines was measured using multiplex technology, and psychiatric symptom clusters were assessed using the Symptoms Check−List−90−R. Data on confounding factors including age, gender, weight, height, current medication and smoking habits were collected. Multiple linear regression analysis was performed to examine associations, adjusting for confounding factors. Results After adjusting for the most commonly known confounding factors, IP−10 and GM−CSF were negatively associated with depression, and GM−CSF was negatively associated with phobic anxiety. Subgroup analyses revealed that these associations were present only in patients receiving opioid maintenance treatment, as demonstrated by repeated regression analysis. Conclusions In patients with chronic hepatitis C viral infection, only IP−10 and GM−CSF were negatively associated with self−reported psychiatric symptom clusters. These associations were observed exclusively in patients receiving opioid maintenance treatment. Our study contributes to others investigations pointing to a possible immune dampening caused by long−term opioid treatment.

Variability of test parameters from mice of different age groups in published data sets

The use of mice as animal models in biomedical research allows the standardization of genetic background, housing conditions as well as experimental protocols, which all affect phenotypic variability. In this study, the phenotypic variability of test parameters was analyzed in genetically identical mice of different age groups, i.e., early adults versus late adults. Therefore, published data sets of genetically identical mice of different age groups collected from the same investigator/ project were retrospectively analyzed. Morphological parameters, blood parameters and behavioral tests were analyzed which are predominantly used in biomedical research. The JaxKOMP project examined C57BL/6NJ mice with an age of 7–20 weeks and 66–81 weeks. Further substrains of C57BL/6N mice with an age of 8–16 weeks and 49–63 weeks were examined as wild-type controls from various investigators of the International Mouse Phenotyping Consortium (IMPC). Additional data sets of young and old groups of genetically identical mice were derived from the Mouse Phenome Database (MPD) and the RIKEN BioResource Research Center (RBRC). The phenotypic variability of the chosen traits and parameters was measured by calculating the coefficient of variation (CV = standard deviation/ mean) of the animals with the same sex of a given mouse strain. Subsequently, the CVs of the young and the old mouse group were compared. The comparison of the phenotypic variability of the late adults versus early adults revealed the appearance of unpredictable interactions between genotype, environment and experiment. Overall, a higher phenotypic variability of the late adults appeared almost consistently for body weight including lean mass and fat mass for females as well as for hematology and immunology parameters, particularly for females. Clinical chemistry often appeared inconspicuous. No noticeable differences were detected for the traits echocardiography and electrocardiogram, whereas late adults also often showed a higher phenotypic variability for behavioral tests.