Browse Articles

Quantum Zeno effect in the spatial evolution of a single atom

Abstract The quantum Zeno effect (QZE) reveals that frequent measurements can suppress quantum evolution; however, the impact of measurements on the real-space motion of a single atom remains insufficiently explored experimentally. In this work, we employ an optical trap as a measurement pulse and, by monitoring atomic loss, directly observe the QZE in the real-space motion of a single atom. We find that the action of measurement on the atom consists of a projective measurement followed by subsequent periodic unitary evolution, thereby providing an intuitive physical picture of measurement backaction across different timescales. We further investigate the effects of measurement frequency, strength, and spatial position, demonstrating that measurements pulse not only suppress the spatial spreading of the quantum state but also enable deterministic preparation of distinct motional states. Moreover, by dynamically controlling the trap position, we realize measurement-induced directional transport of a single atom, with a velocity exceeding the maximum allowed by the adiabatic condition. Overall, our results provide a direct experimental demonstration of the QZE in real space and establish a versatile framework for measurement-based control of atomic motion, opening new possibilities for motional-state engineering in cold-atom systems.

Development and characterization of ivermectin nanoformulations for topical acaricidal activity against Rhipicephalus sanguineus ticks

Abstract Ticks are among the most significant ectoparasites of livestock and humans, posing serious health and economic risks. The growing resistance to conventional acaricides highlights the need for safer and more effective alternatives. This study aimed to evaluate the acaricidal efficacy of different ivermectin (IVM) nanoformulations against different developmental stages of Rhipicephalus sanguineus tick. Different ivermectin nanoformulations were fabricated and evaluated for their physicochemical properties. An in vitro study was performed using larval, nymphal, and adult immersion tests, followed by an in vivo trial against unfed adults using the most effective formulation. All nanoformulations showed particle sizes from 450 to 650 nm and a polydispersity index range from 0.34 to 0.65. IVM spanlastics exhibited the smallest particle size, highest encapsulation efficiency (92%), and a sustained release profile. Based on LC 50 values, IVM spanlastics showed superior acaricidal activity against larvae (LC 50 : 0.05%), nymphs (LC 50 : 0.09%), and unfed adults (LC 50 :0.16%); followed by IVM-SeNPs, where the LC 50 values were 0.10, 0.23, and 0.38% for larvae, nymphs, and unfed adults, respectively. The in vivo study of IVM spanlastics demonstrated 100% mortality of adult ticks within four days after application. IVM spanlastics could be topically applied as an alternative to conventional injectable IVM to control R. sanguineus ticks. Further toxicological studies are necessary to ensure the safety of these formulations for environmental and veterinary use.

K+-free mica-assisted epitaxy of Bi-based chalcogenide and oxychalcogenide single-crystals

Genome-wide analysis reveals structured ecological and functional divergence within Geobacillus stearothermophilus

Abstract Geobacillus stearothermophilus is a thermophilic bacterium widely used in food sterilization and industrial processes. Although it has long been treated as a single, well-defined species, its internal genomic diversity has not been systematically evaluated. Here, we analyzed 36 strains using comparative genomics to clarify the structure of diversity within this species. Phylogenetic analyses consistently revealed two major genomic groups. Genome similarity measurements showed that most strains met current species-level criteria, yet clear internal differentiation was present. The two groups differed in ecological origin and genome composition. Strains associated with food-related environments tended to have smaller genomes and fewer metabolic genes, whereas strains from natural thermal habitats possessed larger genomes and broader metabolic capabilities, including genes for carbohydrate and fatty acid utilization. A small number of strains displayed intermediate positions, suggesting gradual diversification rather than sharp separation. Despite pronounced internal structuring, the strains remain within accepted species boundaries. These findings demonstrate that substantial ecological and functional divergence can accumulate within a single bacterial species. Our results provide a genomic framework for understanding intraspecific diversity in thermophilic bacteria and illustrate the importance of interpreting genome similarity thresholds in the context of population structure.

A highly stretchable tri-channel fiber for composite motion decoupling

Abstract Fiber electronics have shown considerable potential in various applications, including electronic skin, human-machine interfaces, and intelligent sensing systems. However, stretchable fiber-based strain sensors confront fundamental challenges in concurrently achieving robust mechanical endurance, wide linear response range, and effective composite motions decoupling under complex deformation conditions. Here we present a highly stretchable tri-channel fiber featuring concentric and double-helical microchannels integrated with gallium-based liquid metal, constructing a dual-strain fiber sensor capable of decoupling composite motions involving both elongation and torsional deformations. The helical architecture promotes a three-dimensional orientation of polymer chains, thereby effectively enhancing both the stretchability and cyclic durability of the sensor. Owing to the specific configuration within the fiber, the sensor exhibits a highly linear response to tensile strain, along with bidirectional torsional strain sensing across a wide operational range. Furthermore, by synergistically integrating geometric deformation with hybrid resistive-capacitive sensing mechanisms, the sensor demonstrates the ability to simultaneously monitor and decouple stretching and twisting composite motion behaviors. This strategy enables the precise characterization of object motion and deformation states, offering valuable prospects for real-time health monitoring and motion tracking applications.

Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks

Abstract Brain age prediction has gained significant attention due to its strong correlation with neurological and cognitive disorders. The discrepancy between an individual’s chronological age and their predicted brain age–known as the Brain Age Gap–has been linked to conditions such as schizophrenia, Alzheimer’s disease, cognitive decline, and lifestyle factors like stress and poor health. A positive Brain Age Gap is often associated with accelerated aging and neurodegeneration, highlighting the need for precise and reliable estimation methods. In this study, we propose a novel deep learning model that incorporates time-distributed, convolutional and bidirectional LSTM layers for brain age estimation. Using MRI data from the OpenBHB dataset, processed through Voxel-Based Morphometry (VBM), our model undergoes rigorous preprocessing, including outlier detection, data augmentation, and MRI slice selection, to enhance learning efficiency. The model is optimized with the Adam optimizer with a scheduled learning-rate decay and evaluated using Mean Absolute Error (MAE) and $$R^{2}$$ Score. Experimental results demonstrate that our model achieves an MAE of 3.1573 years, outperforming previous methods and improving brain age prediction accuracy. These findings underscore the importance of advances in deep learning and data preprocessing in enhancing brain age estimation.

Glycosphingolipids regulate phosphatidylserine transport machinery that operates at ER-PM contact sites

Morphological identification of fungi associated with preharvest spoilage of sweet orange (Citrus sinensis L.) fruit in Bilo Nopha, Southwestern Ethiopia

Surpassing homochiral assemblies in chiroptical activity through enantiomeric imbalance

ADHD medications and preadolescent brain structure: patterns of cortical attenuation from the ABCD study

Large-scale integrated optoelectronic chaos for machine learning acceleration

Abstract Chaos has emerged as a useful resource for machine learning, yet traditional nonlinear circuits face speed bottlenecks. Optical chaos sources offer an attractive alternative with ultra-wideband operation and massive parallelism, but existing schemes must trade single-channel throughput against multi-channel scalability. Here, we demonstrate an integrated microcomb-optoelectronic chaos engine (iMOCE). By driving an optoelectronic nonlinear cavity with a chaotic microcomb, the iMOCE generates massively parallel chaos with a 6-dB bandwidth of 25 GHz per channel, representing a two-order-of-magnitude improvement over previous microcomb-based approaches. The system delivers a total random-bit generation rate of 32.768 Tbps and accelerates four representative tasks. Compared with MCU/GPU baselines, it reduces per-inference time by about two orders of magnitude. These results establish iMOCE as a scalable, massively parallel chaos primitive for machine learning acceleration.

Acute stress impairs visual narrative comprehension in younger but not older adults

Abstract Visual narrative comprehension is essential for navigating modern society, where information, rules, and news are frequently communicated through images, diagrams, and visual stories. Encoding a coherent narrative from disparate elements is critical for all age groups. Although recent studies report a significant rise in stress and anxiety levels, driven by factors such as the COVID-19 pandemic and recent geopolitical conflicts, the impact of stress on visual narrative comprehension remains largely underexplored. This study explored how acute stress affects narrative comprehension in younger ( N  = 203, 18–57 years; M  = 23 years; Experiment 1) and older adults ( N  = 212, 60–85 years; M  = 67 years; Experiment 2). Participants were assessed under both acute stress and neutral conditions. A tool for inducing acute stress online employed mathematical and logical tasks under time pressure, along with elements that simulate social stress. Participants were presented with pictorial stories consisting of three panels, with the second panel intentionally left blank. Their task was to comprehend the storyline despite the missing information. On the following page, an image representing a possible bridging event was shown, either depicting the correct or an incorrect inference. Participants were asked to judge whether or not the presented image accurately reflected the missing event in the story. Results revealed that acute stress negatively impacted narrative comprehension in younger adults, while the older adults’ comprehension remained unaffected by acute stress. Similarly, younger adults demonstrated reduced confidence in their responses under stress, whereas older adults’ confidence levels remained unaffected. These findings highlight the relationship between visual narrative comprehension, stress, and aging, suggesting that, with age and experience, comprehenders may develop more differentiated event schemas, which makes their comprehension processes more resilient to stress. Understanding how cognitive and perceptional processes function under stress is crucial for daily life across all age groups. Our research demonstrates that younger adults exhibit poorer visual narrative comprehension under acute stress, whereas older adults’ performance remains stable. This finding suggests that older adults may employ more differentiated event schemas, which help maintain their narrative comprehension in the face of stress. Consequently, narrative comprehension appears to be more resilient compared to other fundamental cognitive skills. These insights could inform interventions and strategies to support cognitive health across different age groups.

Fine-scale heterogeneity and local amplification of West Nile virus in urban environments in Berlin

Abstract Climate change can intensify mosquito-borne disease risks through rising temperatures and more frequent extreme weather events. To mitigate effects of climate change, cities are adopting nature-based solutions, such as urban greening and rainwater management, yet their implications for vector-borne diseases and host community composition remain poorly understood. West Nile virus (WNV), an emerging mosquito-borne human pathogen in Europe, is primarily transmitted between birds and mosquitoes. Using mosquito sampling at five sites within a one-square-kilometre area in Berlin, Germany, we examined how urban land cover, including climate-resilient infrastructure, influences local WNV amplification over two mosquito seasons in 2023 and 2024. We found seasonal WNV infection rates of up to 4.8% in mosquitoes and identified fine-scale heterogeneity in infection risk. Residential areas and cemeteries exhibited the highest minimum infection rates per month (up to 15 and 21, respectively), whereas natural conversation and sponge city sites showed significantly lower rates (up to 4 and 13, respectively). These patterns were not explained by mosquito abundance or species composition but by habitat characteristics and avian host community structure. Our findings reveal that urban land cover shapes WNV infection risk and suggest that incorporating biodiversity restoration into nature-based solutions may serve as strategy for sustainable climate-resilient urban planning.

Machine learning guided processing, microstructure and coercivity mapping in M type strontium hexaferrite

Experimental human colonisation with non-toxigenic Clostridioides difficile: a placebo-controlled randomised clinical trial

Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis

Abstract Shear strength of soil plays an essential role in geotechnical properties affecting construction stability. Conventional laboratory testing for determining soil shear strength is often expensive and time-consuming. Therefore, machine learning (ML) methods were employed to predict soil shear strength using geotechnical parameters compiled from previously published literature based on investigations conducted at the Le Trong Tan Geleximco project in western Hanoi, Vietnam. In total, there were 202 samples analyzed, including parameters such as depth, sand percent, loam percent, clay percent, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plasticity index, and liquidity index. Four predictive models have been developed and analyzed, including Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Multi-Expression Programming (MEP). The model performance was evaluated using several parameters like coefficient of determination (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), residual analysis, and Taylor diagrams. RF showed the best predictive performance, with training R 2  = 0.9527 and testing R 2  = 0.8578, along with the lowest prediction error. The SVM model performed impressively in terms of prediction, while the MEP model demonstrated satisfactory accuracy along with the added benefit of mathematical equation formulation. Conversely, the MLR model was relatively less accurate because of its inability to deal with nonlinear relations. Moreover, the SHAP analysis revealed that liquid index, moisture content, and plasticity index were the most critical factors influencing the prediction of soil shear strength. This research has established that modern machine learning models like RF and SVM prove to be efficient at modeling the nonlinear nature of soil characteristics.

A stretch-responsive fibroblast program promotes epidermal stem cell self-renewal during skin expansion

Spatiotemporal dynamics and antimicrobial resistance of bacterial pathogens recovered from various fomites at a paediatric care facility in Ghana

Human CD24+ dental papilla cells are competent seed cells for dentin-pulp regeneration via BMP2/SIRT1 axis

Dietary lactic acid and rosemary leaf supplementation enhances growth and immune responses in Nile tilapia (Oreochromis niloticus)

Abstract The present study investigated the individual and combined effects of lactic acid and rosemary meal on growth performance, biochemical parameters, immune responses, the expression of growth and antioxidant-related genes, intestinal morphology, and oxidative status in Nile tilapia ( Oreochromis niloticus ). A total of 120 apparently healthy fish, with an average weight of 3.03 ± 0.02 g, were randomly assigned to four equal groups, each consisting of three replicates. Four experimental diets were formulated: a basal control diet (CON), a basal diet supplemented with 1 g of lactic acid per kg of diet (LA), a basal diet containing 10 g of rosemary per kilogram of diet (RM), and a basal diet that included both supplements (LA + RM). Fish were fed these diets for a duration of 60 days. The results indicated that either lactic acid or rosemary alone or in combination had a greater growth-stimulating impact than the CON group ( P  ≤ 0.05) with superiority to the combination group (LA + RM group). Activities of aspartate aminotransferase and alanine aminotransferase, along with creatinine and urea levels, were significantly reduced ( P  ≤ 0.05) in the groups of lactic acid and rosemary alone or in combination relative to the CON group. Total protein and albumin concentrations were elevated in the LA + RM group ( P  ≤ 0.05). Intestinal histology revealed normal morphology across groups, with increased villus height, intestinal villi spacing, and goblet cell density in LA + RM ( P  ≤ 0.05) without pathological lesions in the liver and spleen. Antioxidant, immune, and growth-related gene expressions were upregulated in RM and LA + RM groups. In conclusion, rosemary supplementation, alone or combined with lactic acid, enhanced fish health status and upregulated target genes without pathological lesions with superiority to the combination treatment.