Browse Articles

Counter-propagating entangled photon pairs from monolayer GaSe

Estimation of natural radioactivity in underground water sources for personal use in Zhytomyr city and its suburbs

Increasing central and northern European summer heatwave intensity due to forced changes in internal variability

Abstract In recent years, European summer heatwaves have strongly intensified due to rising anthropogenic emissions. While European summer heatwaves will continue to intensify due to the warming of summer temperatures, the effects of the changes in internal variability under global warming remain unknown. Employing five single-model initial-condition large ensembles, we find that the forced changes in internal variability are projected to intensify central and northern European summer heatwaves. Central and northern Europe will experience frequent moisture limitations, enhancing land-atmosphere feedback and increasing heatwave intensity and variability. In contrast, the forced changes in internal variability will contribute to weakening southern European summer heatwaves. Southern Europe is projected to face a more stable moisture-depleted environment that reduces extreme temperature variability and heatwave intensity. Our findings imply that while adaptation to increasing mean temperatures in southern Europe should suffice to reduce the vulnerability to increasing EuSHW intensity, in central and northern Europe adaptation to increased temperature variability will also be needed.

Generative augmentations for improved cardiac ultrasound segmentation using diffusion models

Abstract One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentation models that generalize well to external datasets. This work utilizes diffusion models to create generative augmentations that can significantly improve diversity of the dataset and thus the generalisability of segmentation models without the need for more annotated data. The generative augmentations are applied in addition to regular augmentations. A visual test survey showed that experts cannot clearly distinguish between real and fully generated images. Using the proposed generative augmentations, segmentation robustness was increased when training on an internal dataset and testing on an external dataset with an improvement of over 20 millimeters in Hausdorff distance. Additionally, the limits of agreement for automatic ejection fraction (EF) estimation improved by up to 20% of absolute EF value on out of distribution cases. These improvements come exclusively from the increased variation of the training data using the generative augmentations, without modifying the underlying machine learning model.

Ultrasound-driven programmable artificial muscles

Abstract Muscular systems 1 , the fundamental components of mobility in animals, have sparked innovations across technological and medical fields 2,3 . Yet artificial muscles suffer from dynamic programmability, scalability and responsiveness owing to complex actuation mechanisms and demanding material requirements. Here we introduce a design paradigm for artificial muscles, utilizing more than 10,000 microbubbles with targeted ultrasound activation. These microbubbles are engineered with precise dimensions that correspond to distinct resonance frequencies. When stimulated by a sweeping-frequency ultrasound, microbubble arrays in the artificial muscle undergo selective oscillations and generate distributed point thrusts, enabling the muscle to achieve programmable deformation with remarkable attributes: a high compactness of approximately 3,000 microbubbles per mm 2 , a low weight of 0.047 mg mm −2 , a substantial force intensity of approximately 7.6 μN mm −2 and fast response (sub-100 ms during gripping). Moreover, they offer good scalability (from micrometre to centimetre scale), exceptional compliance and many degrees of freedom. We support our approach with a theoretical model and demonstrate applications spanning flexible organism manipulation, conformable robotic skins for adding mobility to static objects and conformally attaching to ex vivo porcine organs, and biomimetic stingraybots for propulsion within ex vivo biological environments. The customizable artificial muscles could offer both immediate and long-term impact on soft robotics, wearable technologies, haptics and biomedical instrumentation.

Algebraic methods and computational strategies for pseudoinverse-based MR image reconstruction (Pinv-Recon)

Abstract Image reconstruction in Magnetic Resonance Imaging (MRI) is fundamentally a linear inverse problem, such that the image can be recovered via explicit pseudoinversion of the encoding matrix by solving $${\textbf {data}} = {\textbf {Encode}} \times {\textbf {image}}$$ —a method referred to here as Pinv-Recon. While the benefits of this approach were acknowledged in early studies, the field has historically favored fast Fourier transforms (FFT) and iterative techniques due to perceived computational limitations of the pseudoinversion approach. This work revisits Pinv-Recon in the context of modern hardware, software, and optimized linear algebra routines. We compare various matrix inversion strategies, assess regularization effects, and demonstrate incorporation of advanced encoding physics into a unified reconstruction framework. While hardware advances have already significantly reduced computation time compared to earlier studies, our work further demonstrates that leveraging Cholesky decomposition leads to a two-order-of-magnitude improvement in computational efficiency over previous Singular Value Decomposition-based implementations. Moreover, we demonstrate the versatility of Pinv-Recon on diverse in vivo datasets encompassing a range of encoding schemes, starting with low- to medium-resolution functional and metabolic imaging and extending to high-resolution cases. Our findings establish Pinv-Recon as a versatile and robust reconstruction framework that aligns with the increasing emphasis on open-source and reproducible MRI research.

Exploring the potential of extracting seismic attributes from image logs for enhanced fracture characterization

RECQL1 as a potential therapeutic target for PARP inhibitor-resistant ovarian cancer

Distorted representations of age and gender are reflected in AI models

Application of AI and deep learning technology for IPE education under dual track cultivation model

Anxiety about getting fat may increase the number of binge-eating disorder symptoms

Abstract According to preliminary sources, general anxiety and anxiety about getting fat (AGF) are associated with the occurrence of binge eating disorder (BED). Still, there is little research in this area. The aim of the present study was to investigate anxiety, AGF, and depression in BED. The research focused on BED from two separate perspectives: the number of BED symptoms and the recurrence rate of BED episodes. Women ( n  = 103) were surveyed using a self-developed questionnaire evaluating the presence of BED symptoms, the Hospital Anxiety and Depression Scale (HADS), and the Body Mass Anxiety Scale (BMAS-20). Participants who met the criteria for BED reported elevated levels of anxiety and AGF. Significant differences were observed in the level of anxiety and AGF between groups with a lower and higher number of BED symptoms. Also, groups with mild, moderate, and severe BED were found to differ significantly in the level of depression symptoms. AGF was associated with a greater number of BED symptoms, suggesting it may contribute to symptom escalation.

Fatigue analysis of the flexor digitorum superficialis muscle using mechanomyogram during moderate-intensity intermittent dynamic tasks

To make water, exoplanets might just need some pressure

An investigation on structural, optical, and magnetic properties of Zn1−xCoxO nanorods fabricated by electrochemical deposition

Magnetotelluric evidence for a melt-rich magmatic reservoir beneath Mayotte

Rat superficial masseter operates at long lengths during biting

Abstract The operating length ranges of mammalian jaw muscles have been estimated using twitch contractions or force measurements at the bite point, prompting a consensus that jaw muscles operate at short lengths on their force-length (FL) curve. However, since activation intensity truncates muscle optimal length ( L O ), we hypothesized that L O of rat superficial masseter (SM) would decrease with activation intensity, with high-force biting involving muscle shortening from long lengths on the FL curve. We measured muscle activation, strain, and force in vivo during biting on food with varying hardness and mapped the in vivo data from each muscle ( N  = 6) onto its FL relationship, measured in situ . Submaximal L O was approx. 12% shorter than twitch L O , and SM bite forces averaged 4.1 ± 3.9 N/cm 2 (mean ± S.D.) and reached 10.6 N/cm 2 , corresponding to muscle activation and food hardness. Length operation ranged from 7% below L O (ascending FL plateau), to 27% beyond (descending limb). The finding that jaw muscles operate at long, potentially unstable lengths, particularly during hard food biting significantly expands our understanding of skeletal muscle function, with broad implications for craniofacial evolution, muscle mechanics and control, and healthy as well as pathological function of the jaw musculoskeletal system.

Machine learning-driven risk stratification for distant metastasis in gastric cancer: A comparative study of clinical features and composite indices integrated models

Objective Distant metastasis (DM) of gastric cancer (GC) represents a significant health challenge due to its high mortality rates, necessitating advancements in early detection and management strategies. The objective of this study was to create a machine learning (ML) model that is interpretable for preoperative prediction of DM in GC. Methods We retrospectively analyzed 1,009 GC patients, of which 769 were from Zhejiang Cancer Hospital as development cohort and 240 from Zhejiang Provincial Hospital of Chinese Medicine as external test cohort. Nine clinical features, and four composite indices derived from ten laboratory indicators were selected as candidate features. The dataset was balanced using the borderline Synthetic Minority Over-sampling Technique (SMOTE) and the Edited Nearest Neighbors (ENN) under-sampling method. Univariate and multivariate analyses were used to identified key metastasis-related features. Based on the identified features, we developed predictive models incorporating five ML algorithms, with performance evaluated via receive operating characteristic (ROC) curves, recall, precision-recall (PR) curves. Ultimately, Shapley additive explanations (SHAP) analysis were applied to rank the feature importance and explain the final model. Results Univariate and multivariate analyses identified five metastasis-related features: cT stage, cN stage, differentiation grade, PLR and TMI. Logistic Regression emerged as the optimal predictive model with the highest area under the curve (AUC) of 0.942 (95% CI: 0.922–0.962), Recall of 0.895 (95% CI: 0.843–0.947), and AUPRC of 0.889 (95% CI: 0.867–0.911) among five models. Additionally, the internal and external test cohorts recorded AUC values of 0.935 (95% CI: 0.897–0.972) and 0.879 (95% CI: 0.833–0.926), respectively. The SHAP analysis revealed the features that played a significant role in the predictions made by the model. Conclusion This ML model integrates clinical features and composite indices to predict GC metastasis risk, supported by an online tool to guide preoperative decision-making.

7 basic science discoveries that changed the world

Electron fourier ptychography for phase reconstruction

Abstract Exit wavefunction reconstruction is important in transmission electron microscopy for structural studies. We describe electron Fourier ptychography and its application to phase reconstruction of both radiation-resistant and beam-sensitive materials. We demonstrate that the phase of the exit wave can be reconstructed to high resolution using a modified iterative phase retrieval algorithm from data collected in an alternative optical geometry. This method achieves a spatial resolution of 0.63 nm at a fluence of 4.5 × 10 2 e − /nm 2 , as validated on Cry11Aa protein crystals under cryogenic conditions. Notably, this method requires no instrumental modifications, is straightforward to implement, and can be seamlessly integrated with existing data collection software, providing a broadly accessible alternative approach for structural studies.

Sudden cardiac death in adults living with HIV: A systematic review

Background People living with HIV (PLWH) have rising life expectancy, and robust evidence shows they are also at increased risk of cardiovascular disease. However, sudden cardiac death (SCD) for PLWH on antiretroviral therapy (ART) has received little attention. Our systematic review examines the quantitative adult PLWH SCD risk literature with a sub-focus of PLWH on ART. Methods We conducted systematic searches of PubMed, Embase, CENTRAL, CINAHL, Scopus, and Clinicaltrials.gov for peer-reviewed population studies using search terms “sudden cardiac death” AND (”HIV” OR “human immunodeficiency virus”) until 20 June 2025. Two reviewers analysed papers meeting eligibility criteria for their SCD classification methodology including for, but not limited to, comparability, generalizability, and misclassification biases including using the Newcastle-Ottawa Scale. Results The eight eligible studies included ~98 436 PLWH and demonstrated that males PLWH experience elevated SCD risk compared to the general population. One study with 97% male participants found a hazard ratio (HR) of 1.14 (95% CI: 1.04–1.25) for PLWH compared to non-PLWH. In another, comparing viral load groups of ≥500 vs < 500 found a HR of 1.33 (95% CI: 1.04–1.71) for PLWH with CD4 ≥ 500 compared to HIV-negative HR of 1.03 (95% CI: 0.90–1.18). An autopsy study’s male sex arm found a mortality rate ratio for PLWH compared to a reference of 1.34 (95% CI: 0.62–2.87). Conclusions The limited available research provides evidence that while SCD risk for male PLWH is elevated, maintaining HIV-RNA plasma viral load suppression and ≥200 CD4+ cells/mm 3 counts (ideally higher) likely lowers the risk of SCD to a rate that is approaching comparability to the general population. The risk of SCD in women living with HIV is still unknown, due to small sample sizes, as the majority of the participants in the PLWH studies were male.