Global Field Research Articles

Comparative analysis of two episodes of strongly geoeffective coronal mass ejection events in November and December 2023

In autumn 2023 a series of close-in-time eruptive events were observed remotely and measured in situ. For that period, we studied a set of analogous events on the Sun, where several coronal mass ejections (CMEs) were launched partly from the same (active) regions close to a coronal hole. The two episodes of events are separated by a full solar rotation covering the period October 31 -- November 3 and November 27-- 28, 2023. The two episodes of eruptive events are related to strong geomagnetic storms occurring on November 4--5 and December 1--2, 2023. We point out the complexity for each set of events; our aim is to understand how the global magnetic field configuration, solar wind conditions, and interaction between the structures relate to these geomagnetic effects. We used the graduated cylindrical shell (GCS) 3D reconstruction method to derive the direction of motion and speed of each CME. The GCS results served as input for the drag-based model with enhanced latitudinal information (3D DBM), facilitating the assessment of its connection to in situ measurements. This approach significantly aids in the integrated interpretation of in situ signatures and solar surface structures. The first episode caused visible stable auroral red (SAR) arcs, with a Dst index that dropped in three steps down to -163,nT on November 5, 2023. Close in time, two CME-related shocks arrived, separated by a sector boundary crossing (SBC), and followed by a short-duration flux rope-like structure. For the second episode, auroral lights were observed related to a two-step drop in the Dst index down to -108,nT on December 1, 2023. A shock from a CME within the magnetic structure of another CME ahead was identified, again combined with a SBC. Additionally, a clear flux rope structure from the shock producing CME was detected. In both events, we observed distinct short-term variations in the magnetic field (``ripples'') together with fluctuations in density and temperature that followed the SBC. The study presents a comparative analysis of two episodes of multiple eruptive events in November and December 2023. In addition to the interacting CME structures, we highlight modulation effects in the geomagnetic impact due to magnetic structures that are related to the SBC. These most likely contributed to the stronger geomagnetic impact and production of SAR arcs for the November 4--5, 2023, event. At the Sun, we found the orientation of the heliospheric current sheet to be highly tilted, which might have caused additional effects, due to the CMEs interacting with it.

A comparative study of EEG microstate dynamics during happy and sad music videos

EEG microstates offer a unique window into the dynamics of emotional experiences. This study delved into the emotional responses of happiness and sadness triggered by music videos, employing microstate analysis and eLoreta source-level investigation in the alpha band. The results of the microstate analysis showed that regardless of gender, participants during happy music video significantly upregulated class D microstate and downregulated class C microstate, leading to a significantly enhanced global explained variance (GEV), coverage, occurrence, duration, and global field power (GFP) for class D. Conversely, sad music video had the opposite effect. The eLoreta study revealed that during the happy state, there was enhanced CSD in the central parietal regions across both genders and diminished functional connectivity in the precuneus for female participants compared to the sad state. Class D and class C microstates are linked to attention and mind-wandering, respectively. The findings suggest that (1) increased class D and CSD activity could explain heightened attentiveness observed during happy music, and (2) increased class C activity and functional connectivity could explain enhanced mind wandering observed during sad music. Additionally, female participants exhibited significantly higher mean occurrence than males, and the sad state showed significantly higher mean occurrence than the happy state.

Local-global principle and integral Tate conjecture for certain varieties

We give a geometric criterion to check the validity of the integral Tate conjecture for one-cycles on a smooth projective variety that is separably rationally connected in codimension one, and to check that the Brauer-Manin obstruction is the only obstruction to the local-global principle for zero-cycles on a separably rationally connected variety defined over a global function field. We prove that the Brauer-Manin obstruction is the only obstruction to the local-global principle for zero-cycles on all geometrically rational surfaces defined over a global function field, and to the Hasse principle for rational points on del Pezzo surfaces of degree four defined over a global function field of odd characteristic. Along the way, we also prove some results about the space of one-cycles on a smooth projective variety that is separably rationally connected in codimension one, which leads to the equality of the coniveau filtration and the strong coniveau filtration on degree 3 3 homology of such varieties.

Towards Sustainable Development in South Asian Countries: The Role of Economic Complexity and Globalization on Environmental Pollution

In the context of developing economies, this study aims to investigate the insights found in the literature on economic complexity, globalization, and environmental pollution institutions. The available theoretical and empirical literature on the subject is reviewed and summarized in this work. It begins with a summary of Pakistan's environmental contamination, globalization, and economic complexity. By using the theoretical and empirical insights from the published literature, it identifies the literature in the second place. This article also clarified theoretical approaches that explain how and why they function. Third, this paper suggests three useful areas for additional study. By critically examining and combining current environmental theory and research institutions, this work advances the fields of economic complexity, institutions, globalization, and environmental degradation. The rapid urban expansion, there is frequently an increase in the quantity of energy utilized, the amount of trash created, and the amount of pollution produced. In order to lessen the amount of environmental damage that is caused by urban areas, it is essential to implement waste management systems that are both effective and environmentally friendly. This is accomplished via the use of urban planning strategies.

VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection

Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network’s capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet.

Resolution dependence of the turbulent atmospheric boundary layer in global storm‐resolving climate simulations

AbstractThe current generation of state‐of‐the‐art global climate models are being run at increasingly higher horizontal resolutions, with the goal of resolving organised deep convection explicitly. How a kilometre‐scale resolution impacts the representation of the atmospheric boundary layer is, however, not well known. Using statistical analysis on global fields as well as high‐frequency data at selected locations, produced with the Integrated Forecasting System (IFS) model for the Next Generation Earth‐system Models (nextGEMS) project, we investigate the horizontal resolution dependence of some boundary‐layer processes. We find that a change in resolution from 9 to 2.8 km causes no substantial changes to boundary‐layer properties and processes at most of the locations studied, although some global changes are detected that indicate circulation changes. Small changes to the boundary‐layer depth and structure are found in the Tropics. The short simulation length and lack of data for optimal boundary‐layer analysis limits the conclusions, especially in relation to the connection between the boundary layer and the atmosphere general circulation.

Reconstruction of the solid–liquid two-phase flow field in the pipeline based on limited pipeline wall information

Pipeline hydraulic transportation is the primary method for transporting deep-sea mineral resources and fossil fuels. Pipeline blockage often causes excessive pressure in the pipeline, leading to pipeline breakage or even cargo leakage, which severely impacts transportation safety and can easily trigger secondary disasters. Therefore, clarifying the global flow field within pipelines, such as particle distribution, is crucial for monitoring and controlling pipeline systems. This study uses a limited number of easily measurable pipeline wall sensor pressure values as inputs of deep learning models for flow field reconstruction, with the global flow field of solid–liquid two-phase flow in the three-dimensional pipeline as the output. Three model frameworks from existing studies are summarized, and their reconstruction effects are compared. Based on this, a new framework is proposed. It expands the low-dimensional sensor pressure values to the same size as the global flow field using a pseudo-decoder and then processes them through an autoencoder. The results indicate that the new framework achieves further accuracy improvements compared to the previous three frameworks, with R2 and mean squared error reaching 0.933 and 5.13 ×10−4, respectively. Additionally, the effects of the skip connection configuration of the model, dataset size, and model learning rate, as well as the number and arrangement of pressure sensors on reconstruction accuracy, are investigated. Finally, the transferability of the model is demonstrated by reconstructing the pressure and fluid velocity fields of the pipeline two-phase flow.

Mechanism analysis of high-order harmonics acting on transonic shock buffeting

Periodic oscillations of shock waves in transonic flow fields create very unstable aerodynamic loads that have a big effect on how safe an aircraft is and how well it handles. To elucidate the mechanism of transonic shock buffeting, a jet disturbance method is introduced and used to analyze the evolution of the flow field characteristics. First, the basic characteristics of buffeting are obtained through wind tunnel experiments. Numerical simulations are then conducted using the unsteady Reynolds-averaged Navier–Stokes equation based on the Reynolds stress model. The response of the global flow field to a jet disturbance enables the effects of the jet intervention time, jet withdrawal, and jet intensity to be studied. When the flow field is disturbed, the oscillations of the shock wave on the upper surface slow, and the buffeting load is reduced. An increase in jet strength suppresses the buffeting by preventing the factors that cause this phenomenon from exerting any influence. When the jet intensity exceeds a critical value, a complex multi-vortex structure and high-frequency pressure fluctuations appear at the trailing edge. In the presence of the jet disturbance, special high-order harmonics appear in the frequency spectrum, and high-frequency pressure waves occur in the global flow field. The fluctuating pressure generates high-order harmonics, and the trailing edge is the core position of transonic shock buffeting. The resulting high-frequency pressure waves are transmitted upstream, strengthening the shock wave–boundary layer interaction and causing shock oscillations.

Pixel integration from fine to coarse for lightweight image super-resolution

Recently, Transformer-based methods have made significant progress on image super-resolution. They encode long-range dependencies between image patches through self-attention mechanism. However, when extracting all tokens from the entire feature map, the computational cost is expensive. In this paper, we propose a novel lightweight image super-resolution approach, pixel integration network(PIN). Specifically, our method employs fine pixel integration and coarse pixel integration from local and global receptive field. In particular, coarse pixel integration is implemented by a retractable attention, consisting of dense and sparse self-attention. In order to focus on enriching features with contextual information, spatial-gate mechanism and depth-wise convolution are introduced to multi-layer perception. Besides, a spatial frequency fusion block is adopted to obtain more comprehensive, detailed, and stable information at the end of deep feature extraction. Extensive experiments demonstrate that PIN achieves the state-of-the-art performance with small parameters on lightweight super-resolution.

Spatially-aware diffusion models with cross-attention for global field reconstruction with sparse observations

Diffusion models have gained attention for their ability to represent complex distributions and incorporate uncertainty, making them ideal for robust predictions in the presence of noisy or incomplete data. In this study, we develop and enhance score-based diffusion models in field reconstruction tasks, where the goal is to estimate complete spatial fields from partial observations. We introduce a condition encoding approach to construct a tractable mapping between observed and unobserved regions using a learnable integration of sparse observations and interpolated fields as an inductive bias. With refined sensing representations and an unraveled temporal dimension, our method can handle arbitrary moving sensors and effectively reconstruct fields. Furthermore, we conduct a comprehensive benchmark of our approach against a deterministic interpolation-based method across various static and time-dependent PDEs. Our study attempts to addresses the gap in strong baselines for evaluating performance across varying sampling hyperparameters, noise levels, and conditioning methods. Our results show that diffusion models with cross-attention and the proposed conditional encoding generally outperform other methods under noisy conditions, although the deterministic method excels with noiseless data. Additionally, both the diffusion models and the deterministic method surpass the numerical approach in accuracy and computational cost for the steady problem. We also demonstrate the ability of the model to capture possible reconstructions and improve the accuracy of fused results in covariance-based correction tasks using ensemble sampling.

Correction of global elevation data SRTM to the national elevation system in the territory of Vietnam

This paper presents a five-step procedure for correcting the global elevation data Shuttle Radar Topography Mission (SRTM) to the national elevation system involving: (1) selecting GNSS/levelling points, obtaining global elevation data SRTM from https://earthexplorer.usgs.gov/ and height anomalies of the global gravity field model EGM96 from http://icgem.gfz-potsdam.de/home, corresponding to GNSS/levelling points; (2) calculating the deviation between the global elevation data SRTM and the measured height of GNSS/levelling points; (3) converting the global elevation data SRTM to national elevation; (4) checking the global elevation data SRTM after converting to national elevation; and (5) performing correction and evaluation. The procedure is carried out on the territory of Vietnam from 813 GNSS/levelling points of grades I, II and III. The results show that the accuracy before and after correction is ±4.112 m and ±3.131 m, respectively, for the territory of Vietnam, >3 m and >2 m for mountainous regions including Northwest, Northeast, North Central, South Central, Central Highlands and Southeast, where the elevation ranges from 700 to 1500 m, and from <3 m to <2 m for plain regions including Red River Delta, Southwest, where the elevation ranges from 2 to 10 m. The correction results of the global elevation data SRTM based on GNSS/levelling points indicate that the accuracy before and after correction depends on the topographical characteristics. These correction results for eight different regions in Vietnam are also consistent with research conducted in various regions of the world.

Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation

PurposeThis work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification.MethodsA total of 76 supratentorial meningioma patients treated with radiotherapy were studied. Gross tumor volumes (GTVs) were contoured by a single experienced radiation oncologist on high-resolution contrast-enhanced T1 MRI scans (T1ce), and both T1 and T1ce images were utilized for segmentation. SPU-Net, an adaptation of U-Net incorporating spherical image projection to map 2D images onto a spherical surface, was proposed. As an equivalence of a nonlinear image transform, projections enhance locoregional details while maintaining the global field of view. By employing multiple projection centers, SPU-Net generates various GTV segmentation predictions, the variance indicating the model’s uncertainty. This uncertainty is quantified on a pixel-wise basis using entropy calculations and aggregated through Otsu’s method for a final segmentation.Results/conclusionThe SPU-Net model poses an advantage over traditional U-Net models by providing a quantitative method of displaying segmentation uncertainty. Regarding segmentation performance, SPU-Net demonstrated comparable results to a traditional U-Net in sensitivity (0.758 vs. 0.746), Dice similarity coefficient (0.760 vs. 0.742), reduced mean Hausdorff distance (mHD) (0.612 cm vs 0.744 cm), and reduced 95% Hausdorff distance (HD95) (2.682 cm vs 2.912 cm). SPU-Net not only is comparable to U-Net in segmentation performance but also offers a significant advantage by providing uncertainty quantification. The added SPU-Net uncertainty mapping revealed low uncertainty in accurate segments (e.g., within GTV or healthy tissue) and higher uncertainty in problematic areas (e.g., GTV boundaries, dural tail), providing valuable insights for potential manual corrections. This advancement is particularly valuable given the complex extra-axial nature of meningiomas and involvement with dural tissue. The capability to quantify uncertainty makes SPU-Net a more advanced and informative tool for segmentation, without sacrificing performance.

“Spectrum Professionals” and Subjective Authors: Training the Photographic “I” for Meaningful Storytelling Beyond the Fields

ABSTRACT Postema and Deuze [2020, “Artistic Journalism: Confluence in Forms, Values and Practices.” Journalism Studies 21 (10): 1305–1322] suggest that “photographers identify with both the arts and the journalism world”. They claim that the world of art has both legitimated the author’s perspective in photojournalism and institutionalized “the photojournalist as producer of culture […] and as artist”. The border between “two opposite practices – art photography and photographic realism (as used in journalism)” [Åker 2012, “Photography, Objectivity and the Modern Newspaper.” Journalism Studies 13 (3): 325–339] seems to be disappearing. Yet, critical analyzes still divide photographic practice according to the settled notions of professional fields, identities, and boundaries established through the opposing discourses of subjectivity and objectivity [Solaroli 2016, “The Rules of a Middle-Brow Art: Digital Production and Cultural Consecration in the Global Field of Professional Photojournalism.” Poetics 59:50–66]. This article turns to photography students in Russia and Sweden to learn whether they see the “realistic” and “expressionistic” in photography as setters of field boundaries or if they instead view them as a means to orient, practice and, potentially, move along the art–journalism spectrum (or even beyond it). By conducting eight focus groups in three photography schools with different specializations, the findings suggest that photography students tend to see the convergence of artistic and journalistic forms, values, and epistemologies as both attractive and unavoidable. Most of the students, despite contextual differences, see their future as “spectrum professionals” and subjective authors.

Arithmetic Quotients of the Bruhat-Tits Building for Projective General Linear Group in Positive Characteristic

Let d ≥ 1 d \ge 1 . We study a subspace of the space of automorphic forms of G L d \mathrm {GL}_d over a global field of positive characteristic (or, a function field of a curve over a finite field). We fix a place ∞ \infty of F F , and we consider the subspace A S t \mathcal {A}_{\mathrm {St}} consisting of automorphic forms such that the local component at ∞ \infty of the associated automorphic representation is the Steinberg representation (to be made precise in the text). We have two results. One theorem (Theorem 5.4.2) describes the constituents of A S t \mathcal {A}_{\mathrm {St}} as automorphic representation and gives a multiplicity one type statement. For the other theorem (Theorem 4.5.1), we construct, using the geometry of the Bruhat-Tits building, an analogue of modular symbols in A S t \mathcal {A}_{\mathrm {St}} integrally (that is, in the space of Z \mathbb {Z} -valued automorphic forms). We show that the quotient is finite when a level is fixed and give a bound on the exponent of this quotient.

Circular Intuitionistic Fuzzy EDAS Approach: A New Paradigm for Decision-Making in the Automotive Industry Sector

The automotive industry has become a robust manufacturing sector in the global manufacturing field, boosting and developing at high speed based on technology updates, environmental protection rules and regulation changes, and customers' new needs and demands. Such dynamics give rise to complex problems like optimizing cost strategy and innovation, operating under high sustainability standards, and navigating the supply chain. These difficulties have led to the need for more structured, logical and effective approaches to decision-making that can handle cross purposes and the world of slow changes and unpredictability. To mitigate these problems, in this context, this study presents the Circular Intuitionistic Fuzzy EDAS (CIF-EDAS) approach to handle decision-making challenges in the automotive industry. Integrating the circular intuitionistic fuzzy sets (CIFSs) with the EDAS approach provides a better assessment of uncertainty and hesitation, enhancing the reliability and strength of multi-criteria decision-making (MCDM) methods. The applicability of the proposed approach is illustrated by a case study in the automotive industry, in which the assessment of different scenarios based on conflicting criteria is effective. Moreover, a comparison analysis has been conducted, demonstrating the proposed approach's superiority and highlighting its efficacy in facilitating robust decision-making. The CIF-EDAS approach is clarified to exemplify its applicability in optimizing decision-making of complicated real-life industrial problems and demonstrating its capability to evolve for the automotive industry's newly emergent restrictive and dynamic demand.

Bibliometric Analysis of Probiotic Bacillus in Food Science: Evolution of Research Trends and Systematic Evaluation.

With the in-depth and comprehensive research on probiotic Bacillus, it has become a hot topic in food science. However, the current status of research using bibliometric analysis to assess the application of probiotic Bacillus in food science has not been comprehensively reviewed. The Web of Science (WOS) database was used in this review's bibliometric analysis to determine the hotspots for research as well as the extent of completed experiments. Furthermore, a systematic review was conducted on the research hotspots of probiotic Bacillus in food science. The comprehensive analysis showed it was a growing and global research field. The keywords with high frequency mainly included "spore," "strain," and "production," which were research hot topics in the last decade. The application of the spore form or nutrient cells of probiotic Bacillus in industrialized food production through nutrient fortification, fermentation agents, and highly efficient synthesis of metabolites showed great development potential.

The added value and potential of long-term radio occultation data for climatological wind field monitoring

Abstract. Global long-term stable 3D wind fields provide valuable information for climate-oriented analyses of the dynamics of the atmosphere. Their monitoring remains a challenging task given the shortcomings of available observations. One promising option for progress is the use of radio occultation (RO) satellite data, which enable deriving dynamics based on thermodynamic data. In this study we focus on three main goals, explored through the fifth version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) and RO datasets, using monthly-mean January and July data over 2007–2020. Our focus is on a 2.5° × 2.5° spatial synoptic scale over the free troposphere to the mid-stratosphere (i.e. 800–10 hPa). First, by comparing ERA5-derived geostrophic and gradient wind speeds to the original ERA5 ones, we examine the regions of validity of the studied approximations at a given synoptic-scale resolution. Second, to assess the possible added value of the RO-derived climatic winds in terms of their long-term stability, we test their consistency with the corresponding ERA5-derived winds. Third, by comparing the RO climatic winds to the original ERA5 winds, we evaluate the potential benefit of RO as an additional dataset for wind analyses and climate monitoring. With this three-step analysis, we decompose the total wind speed bias into the contributions from the approximation and the systematic difference between the RO and ERA5 datasets. We find that the geostrophic approximation is a valid method to estimate winds in the free troposphere, while the gradient wind approximation works better in the lower stratosphere. Both approximations generally work well over the mentioned altitudes, within an accuracy of 2 m s−1 for the latitudes 5–82.5°. Exceptions are found in winter in the monsoonal area and above larger mountain ranges in the free troposphere, as well as above the northern polar regions in the mid-stratosphere. RO- and ERA5-derived geostrophic winds mostly showed good agreement (within 2 m s−1). However, temporal change in the systematic difference higher than 0.5 m s−1 per decade was found. This points to a possible impact of changes in the source of the assimilated data in ERA5. The overall high accuracy of the monthly-mean wind fields, backed by the long-term stability and fine vertical resolution of the underlying RO data, highlights the added value and potential benefit of RO-derived climatic winds for climate monitoring and analyses.

Application and Prospect of Bio-antibiotics and mRNA Vaccines in the Prevention and Treatment of Bacterial Infections and Infectious Diseases

This paper delves into the application and prospects of bio-antibiotics and mRNA vaccines in preventing and treating bacterial infections and infectious diseases. Against the backdrop of the increasingly serious antibiotic resistance issue, the effectiveness of traditional antibiotic treatments is challenged, while bio-antibiotics and mRNA vaccines, as novel treatment methods, are emerging. Bio-antibiotics, derived from organisms or bioengineered, possess advantages such as antibacterial activity, biocompatibility, low toxicity, and reduced resistance risk, showing broad prospects in treating multi-drug-resistant bacteria infections, special types of infections, and preventive medication. mRNA vaccines, by introducing mRNA encoding pathogen antigens to stimulate the human immune response, are characterized by rapid development, high efficiency, and safety, playing a crucial role in infectious disease prevention and control, especially during the COVID-19 pandemic when the successful development and widespread application of mRNA vaccines significantly reduced the infection rate and mortality. The article also analyzes successful cases of bio-antibiotics and mRNA vaccines in practical applications and the challenges they face, such as extraction and purification techniques, stability, delivery systems, long-term safety, and regulatory policies, and proposes future research directions and prospects. In conclusion, bio-antibiotics and mRNA vaccines, as novel treatment, have great application potential and will bring new hope and opportunities to the global public health field.

An Efficient Retinal Fluid Segmentation Network Based on Large Receptive Field Context Capture for Optical Coherence Tomography Images.

Optical Coherence Tomography (OCT) is a crucial imaging modality for diagnosing and monitoring retinal diseases. However, the accurate segmentation of fluid regions and lesions remains challenging due to noise, low contrast, and blurred edges in OCT images. Although feature modeling with wide or global receptive fields offers a feasible solution, it typically leads to significant computational overhead. To address these challenges, we propose LKMU-Lite, a lightweight U-shaped segmentation method tailored for retinal fluid segmentation. LKMU-Lite integrates a Decoupled Large Kernel Attention (DLKA) module that captures both local patterns and long-range dependencies, thereby enhancing feature representation. Additionally, it incorporates a Multi-scale Group Perception (MSGP) module that employs Dilated Convolutions with varying receptive field scales to effectively predict lesions of different shapes and sizes. Furthermore, a novel Aggregating-Shift decoder is proposed, reducing model complexity while preserving feature integrity. With only 1.02 million parameters and a computational complexity of 3.82 G FLOPs, LKMU-Lite achieves state-of-the-art performance across multiple metrics on the ICF and RETOUCH datasets, demonstrating both its efficiency and generalizability compared to existing methods.

Spontaneous blinking and brain health in aging: Large-scale evaluation of blink-related oscillations across the lifespan.

Blink-related oscillations (BROs) are newly discovered neurophysiological brainwave responses associated with spontaneous blinking, and represent environmental monitoring and awareness processes as the brain evaluates new visual information appearing after eye re-opening. BRO responses have been demonstrated in healthy young adults across multiple task states and are modulated by both task and environmental factors, but little is known about this phenomenon in aging. To address this, we undertook the first large-scale evaluation of BRO responses in healthy aging using the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository, which contains magnetoencephalography (MEG) data from a large sample (N = 457) of healthy adults across a broad age range (18-88) during the performance of a simple target detection task. The results showed that BRO responses were present in all age groups, and the associated effects exhibited significant age-related modulations comprising an increase in sensor-level global field power (GFP) and source-level theta and alpha spectral power within the bilateral precuneus. Additionally, the extent of cortical activations also showed an inverted-U relationship with age, consistent with neurocompensation with aging. Crucially, these age-related differences were not observed in the behavioral measures of task performance such as reaction time and accuracy, suggesting that blink-related neural responses during the target detection task are more sensitive in capturing aging-related brain function changes compared to behavioral measures alone. Together, these results suggest that BRO responses are not only present throughout the adult lifespan, but the effects can also capture brain function changes in healthy aging-thus providing a simple yet powerful avenue for evaluating brain health in aging.