Limited Spatial Resolution Research Articles

Deep learning enhanced quantum holography with undetected photons

Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments.

Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data.

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ineffective. Although feature extraction methods have been applied, the chemical meaning of the compressed variables is difficult to interpret, limiting their further applications. An unmixing autoencoder (UAE) was developed in this work for the separation of the mixed spectra in HSI. The proposed model is composed of an encoder and a fully connected (FC) layer. The former is used to compress the input spectrum into several variables, and the latter is employed to reconstruct the spectrum. Combining reconstruction loss and sparse regularization, the weights and the spectral profiles of the components will be encoded in the compressed variables and the connection weights of FC, respectively. A simulated and three experimental HSI data sets were adopted to investigate the performance of the UAE model. The spectral components were successfully obtained, from which the handwriting under papers was revealed from the image of near-infrared (NIR) diffusive reflectance spectroscopy, and the images of lipids, proteins, and nucleic acids were reconstructed from the Raman and stimulated Raman scattering (SRS) images.

A Simulation Study of Low-Intensity Focused Ultrasound for Modulating Rotational Sense Through Acoustic Streaming in Semicircular Canal: A Pilot Study

This study explores the feasibility of using low-intensity focused ultrasound (LIFU) to induce rotational sensations in the human semicircular canal (SCC) through the acoustic streaming effect. Existing vestibular stimulation methods, such as galvanic vestibular stimulation (GVS), caloric vestibular stimulation (CVS), and magnetic vestibular stimulation (MVS), face limitations in spatial and temporal resolution, with unclear mechanisms. This study investigates whether LIFU can overcome these limitations by modulating endolymph motion within SCC. A 3D finite element model was constructed to simulate the effects of LIFU-induced acoustic streaming on SCC (particularly the endolymph), with thermal effects evaluated to ensure safety. Fluid–structure interaction (FSI) was used to analyze the relationship between endolymph flow and cupula deformation. By adjusting the focal point of the ultrasound transducer, we were able to alter fluid flow pattern, which resulted in variations in cupula displacement. The results demonstrated that LIFU successfully induces fluid motion in SCC without exceeding thermal safety limits (<1 °C), suggesting its potential for controlling rotational sensations, with cupula displacement exceeding 1 μm. This novel approach enhances the understanding of LIFU’s thermal and neuromodulatory effects on the vestibular system, and thereby offers promising implications for future therapeutic applications.

Challenges and opportunities when studying movement ecology in science and practical conservation

Movement is a key mechanism influencing biodiversity patterns and ecosystem processes. Movement ecology science aims to understand the causal relationships between environmental conditions, animal movements, interactions and coexistence of species, as well as effects of movement patterns on ecosystem processes. In contrast, practical conservation primarily aims to understand organisms' movements to improve species management, protection, legal monitoring or risk assessment, and species-habitat interactions. Despite the many studies of movement ecology in basic and applied sciences as well as in practical conservation in terrestrial ecosystems, knowledge gain and transfer between disciplines are limited. Better integration and linking of both disciplines would result in diverse science-practice synergies, but these are currently constrained by numerous challenges that need to be overcome. From a scientific perspective, knowledge gain from practice is limited by multitude of case studies with limited spatial and temporal resolution. This can be overcome by improving access and combining the diversity of data for a research area that often deals with small sample sizes. From a practical perspective, the movement ecology framework which is often dedicated to basic research, and access and language barriers of scientific publications limit the application of scientific results in practice. Here movement ecologists should be encouraged to consider conservation issues more frequently in addition to basic research. The transfer of scientific results could be improved by scientists providing sufficient details for practitioners to extract relevant information and publish at least an open-access abstract in local language with clear management recommendations. Further, the use of open-access repositories allows both, scientists and practitioners an overview of the multitude of studies and helps to share data in order to derive general conclusions. Challenges impacting science and practice can be conceptual, organisational and technical in nature. Such constrains can be overcome, for example, by providing verified trapping protocols, using recent technological developments and analytical methods combined with trainings on these state-of-the-art tracking and analysing tools. In particular, collaborative project planning between scientists and practitioners can help to improve the sampling design of applied studies and broaden the data base for science in order to significantly advance the movement ecology framework and gain comprehensive knowledge for practical conservation.

The Field Automatic Insect Recognition-Device-A Non-Lethal Semi-Automatic Malaise Trap for Insect Biodiversity Monitoring: Proof of Concept.

Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems face various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel nonlethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and nonspecific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated its potential as a cost-effective, nonlethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.

Direct observation of the evolution behavior of micro to nanoscale precipitates in austenitic heat-resistant steel via electron channeling contrast imaging

Precipitation directly determines the high-temperature properties of the austenitic heat-resistant steels. The precipitations of MX, Z-phase, M23C6 and σ-phase, ranging from micrometers to nanometers, are commonly characterized using the scanning electron microscopy (SEM) and transmission electron microscopy (TEM). However, details of their evolution behavior are still unclear due to the limited SEM resolution using the traditional sampling method and the limited spatial resolution of TEM. In this work, field emission SEM-based electron channel contrast imaging (ECCI) techniques with flexible sampling routes were introduced to observe the precipitation evolution behavior of HR3C steel after aging at 700 °C for 8095 h. We showed that the coarse primary-MX and Z-phase in the as-received steel are relatively stable during aging. The coarse M23C6 and tiny secondary Z-phase dispersions were rapidly formed along the grain/twin boundaries and within dislocation arrays inside the grain interior, respectively. We further found that the M23C6 at grain boundaries would change from continuous to semi-continuous due to the formation of σ-phases, while in twin boundaries, it would become continuous over aging. Moreover, we showed that the σ-phases were in-situ transformed from M23C6 at the grain boundaries via its dissolution, facilitating the nucleation of tiny Z-phase inside the σ-phase grains, and this phenomenon has not been reported so far. We have demonstrated that ECCI with an electrolytic-polishing-based sampling route is effective in revealing multi-scale precipitation with high-throughput efficiency, and it allows the direct observation of the complex behavior of precipitates down to the nanoscale using a bulk sample. This method can be used as an efficient way for the quantitative microstructure study.

Combined drug delivery and treatment monitoring using a single high frequency ultrasound system

Ultrasound-mediated drug delivery is typically performed using transducers with center frequencies ≤ 1 MHz to promote acoustic cavitation. Such frequencies are not commonly used for diagnostic ultrasound due to limited spatial resolution. Therefore, delivery and monitoring of therapeutic ultrasound typically requires two transducers to enable both treatment and imaging. This study investigates the feasibility of using a single commercial ultrasound imaging transducer operating at 5 MHz for both drug delivery and real-time imaging. We compared a single-transducer system (STS) at 5 MHz with a conventional dual-transducer system (DTS) using a 1.1 MHz therapeutic transducer and an imaging probe. in vitro experiments demonstrated that the STS could achieve comparable extravasation depth and area as the DTS, with higher drug deposition observed at 5 MHz. Additionally, extravasation patterns were influenced by peak negative pressure (PNP) and duty cycle, with the narrower beam width at 5 MHz offering potential advantages for targeted drug delivery. in vivo experiments in a murine bladder cancer model confirmed the efficacy of the STS for real-time imaging and drug delivery, with cavitation dose correlating with drug deposition. The results suggest that a single-transducer approach may enhance the precision and efficiency of ultrasound-mediated drug delivery, potentially reducing system complexity and cost.

Flexible bioelectronic systems with large-scale temperature sensor arrays for monitoring and treatments of localized wound inflammation

Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format. Here, we report a conformal, scalable device package that integrates a flexible amorphous silicon-based temperature sensor array and drug-loaded hydrogel for the healing process. This system can enable the spatial temperature mapping at submillimeter resolution and high sensitivity of 0.1 °C, for dynamically localizing the inflammation regions associated with temperature change, automatically followed with heat-triggered drug delivery from hydrogel triggered by wearable infrared light-emitting-diodes. We establish the operational principles experimentally and computationally and evaluate system functionalities with a wide range of targets including live animal models and human subjects. As an example of medical utility, this system can yield closed-loop monitoring/treatments by tracking of temperature distribution over wound areas of live rats, in designs that can be integrated with automated wireless control. These findings create broad utilities of these platforms for clinical diagnosis and advanced therapy for wound healthcare.

3D-GloBFP: the first global three-dimensional building footprint dataset

Abstract. Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally at the grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analysis and the ability to generate actionable insights at finer scales. In this study, we developed a global building height map at the building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote-sensing features and building morphology features to develop height estimation models using the extreme gradient boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the three-dimensional (3D) Global Building Footprints (3D-GloBFP) dataset for the year 2020. Our evaluation results show that the height estimation models perform exceptionally well at a global scale, with R2 values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) ranging from 1.9 to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3, accounting for 23.9 % of the global total), followed by the USA (3.90×1011 m3, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint-scale height map (3D-GloBFP) reveals the significant heterogeneity in urban built-up environments, providing valuable insights for studies on urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at https://doi.org/10.5281/zenodo.11319912 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP; Che et al., 2024c), https://doi.org/10.5281/zenodo.11397014 (Building height of Asia in 3D-GloBFP; Che et al., 2024a), and https://doi.org/10.5281/zenodo.11391076 (Building height of Europe in 3D-GloBFP; Che et al., 2024b).

Bi-Plane Multicolor Scanning Illumination Microscopy with Multispot Excitation and a Distorted Diffraction Grating.

Multifocus microscopy has previously been demonstrated to provide volumetric information from a single shot. However, the practical application of this method is challenging due to its weak optical sectioning and limited spatial resolution. Here, we report on the combination of a distorted diffraction grating and multifocal scanning illumination microscopy to improve spatial resolution and contrast. DG is introduced in the emission path of the multifocal scanning illumination microscopy, which splits the fluorescence signal from different sample layers into different diffraction orders. After postprocessing, super-resolution wide-field images of different sample layers can be reconstructed from single 2D scanning.

Abstract 4140123: Real-time imaging of microvasculature obstruction and the vasculoprotection of nitric-oxide-donor nanoparticles during acute myocardial ischemia/reperfusion injury

Background/Introduction: Microvascular obstruction (MVO), due to damage to the coronary microvasculature, is a key determinant of infarct size, heart failure and poor outcomes following acute myocardial infarction, and there is currently no treatment for preventing MVO. Real-time in vivo imaging of MVO in the beating rodent heart is challenging due to the limited spatial and temporal resolution from movement artifacts. Here, we apply, for the first time, fiber-optic confocal laser endomicroscopy (CLM) for real-time imaging of the microvasculature in a beating murine heart with acute ischemia/reperfusion injury (IRI), and then monitoring the development of MVO. Methods: An in vivo murine acute myocardial IRI model (45 min ligation of left coronary artery (LCA) and 30 min reperfusion) was applied. At 10 min prior to ischaemia, 150 µl Dextran-FITC (150 kDa, 10 mg/ml) was injected retro-orbitally, and then CLM imaging with a flexible miniprobe (ProFlex S-1500 with CellVizio system) was applied to the epicardial surface at multiple sites at 5 min post-injection (baseline), 30 min post-ischemia and 30 min post-reperfusion. A nitric oxide donor(NO) nanoparticle (NONP) was synthesized and IV bolus injected into IRI mice 5min prior to reperfusion to prevent MVO. Results: We confirmed visualization of the macro- and microvasculature at various sites on the epicardial surface of the beating heart. Next, we observed reduced microvasculature blood flow below LCA ligature as evidenced by reduced or even totally absence of FITC within the vessels at 30min post-ischemia. The microvasculature at the non-ischemic myocardium was unaffected. Furthermore, at 30 min post-reperfusion, we visualised patchy areas of reduced FITC signal suggesting MVO, and damaged microvasculature as evidenced by leakage of FITC outside the vessel. Interestingly, NONP treatment preserved the microvascular network and prevented MVO at 30 min post-reperfusion with even greater FITC, suggesting increased microvascular blood flow and penetration into cardiac tissue because of the vasodilatory effect of NO in the ischemic area. Conclusion: With CellVizio CLM system, we have demonstrated the MVO development during IRI, and damage to the microvasculature with leakage of dye from vessels into cardiac interstitium, thereby providing a pre-clinical platform to test novel therapeutic agents for preventing MVO. Importantly, we have shown an effective MVO prevention with NO-donor nanoparticle following IRI in mice.

Partial volume correction for Lu-177-PSMA SPECT

BackgroundThe limited spatial resolution in SPECT images leads to partial volume effect (PVE), degrading the subsequent dosimetric accuracy. We aim to quantitatively evaluate PVE and partial volume corrections (PVC), i.e., recovery coefficient (RC)-PVC (RC-PVC), reblurred Van-Cittert (RVC) and iterative Yang (IY), in 177Lu-PSMA-617 SPECT images.MethodsWe employed a geometrical cylindrical phantom containing five spheres (diameters ranging from 20 to 40 mm) and 40 XCAT phantoms with various anatomical variations and activity distributions. SIMIND Monte Carlo code was used to generate realistic noisy projections. In the clinical study, sequential quantitative SPECT/CT imaging at 4 time-points post 177Lu-PSMA-617 injections were analyzed for 10 patients. Iterative statistical reconstruction methods were used for reconstruction with attenuation, scatter and geometrical collimator detector response corrections, followed by post-filters. The RC-curves were fit based on the geometrical phantom study and applied for XCAT phantom and clinical study in RC-PVC. Matched and 0.5-2.0 voxels (2.54–10.16 mm) mismatched sphere masks were deployed in IY. The coefficient of variation (CoV) was measured on a uniform background on the geometrical phantom. RCs of spheres and mean absolute activity error (MAE) of kidneys and tumors were evaluated in simulation data, while the activity difference was evaluated in clinical data before and after PVC.ResultsIn the simulation study, the spheres experienced significant PVE, i.e., 0.26 RC and 0.70 RC for the 20 mm and 40 mm spheres, respectively. RVC and IY improved the RC of the 20 mm sphere to 0.37 and 0.75 and RC of the 40 mm sphere to 0.96 and 1.04. Mismatch in mask increased the activity error for all spheres in IY. RVC increased noise and caused Gibbs ringing artifacts. For XCAT phantoms, both RVC and IY performed comparably and were superior to RC-PVC in reducing the MAE of the kidneys. However, IY and RC-PVC outperformed RVC for tumors. The XCAT phantom study and clinical study showed a similar trend in the kidney and tumor activity differences between non-PVC and PVC.ConclusionsPVE greatly impacts activity quantification, especially for small objects. All PVC methods improve the quantification accuracy in 177Lu-PSMA SPECT.

Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

Sub-100 fs Formation of Dark Excitons in Monolayer WS2.

Two-dimensional semiconducting transition metal dichalcogenides are promising materials for optoelectronic applications due to their strongly bound excitons. While bright excitons have been thoroughly scrutinized, dark excitons have been much less investigated, as they are not directly observable with far-field spectroscopy. However, with their nonzero momenta, dark excitons are significant for applications requiring long-range transport or coupling to external fields. We access such dark excitons in WS2 monolayer using transient photoemission electron microscopy with subdiffraction limited spatial resolution (75 nm) and exceptionally high temporal resolution (13 fs). Image time series of the monolayer are recorded at several different fluences. We directly observe the ultrafast formation of dark K-Λ excitons occurring within 14-50 fs and follow their subsequent picosecond decay. We distinguish exciton dynamics between the monolayer's interior and edges and conclude that the picosecond-scale evolution of dark excitations is defect-mediated while intervalley scattering is not affected by the defects.

Mitigating coarse spatial reconstruction to generate missing bands for the HLS dataset

Abstract. The Harmonized LandSat-Sentinel (HLS) dataset has significantly advanced Earth Observation by integrating data from Landsat and Sentinel satellites. However, challenges persist in achieving spectral band parity between LandSat and Sentinel-derived HLS products. This paper presents an extended investigation aimed at enhancing spatial reconstruction accuracy to enable spectral band parity within HLS products. Building upon our previous work, which utilized generative neural networks to address partial feature mismatches between S30 and L30 products, we introduce a refined approach that fully integrates a Self-Supervised Learning (SSL)-pretrained encoder into a U-Net architecture. This method aims to access multi-scale features and improve spatial reconstruction accuracy, addressing the limitations in spatial resolution observed in our earlier study. Our methodology incorporates a comprehensive ablation study to assess various SSL-pretrained backbone architectures. Preliminary results demonstrate significant improvements in spatial reconstruction accuracy compared to our previous work. The adapted U-Net architecture, leveraging SSL-pretrained encoders, shows enhanced capability in capturing intricate spatial features within the HLS dataset. Our experiments demonstrate a substantial improvement in spatial resolution and feature reconstruction for L30 products, particularly in bands not natively present in Landsat data, paving the way for more accurate multi-sensor analyses.

Impact of Tonga volcanic eruption on the ionosphere based on ground-based GNSS and COSMIC occultation data

The eruption of large volcanoes may potentially disturb the upper atmosphere, causing disturbances in ionospheric plasma and triggering irregular changes in the Total Electron Content (TEC). The GNSS ionospheric inversion method has been used to analyze ionospheric anomalies caused by the volcano, while it was limited to the spatial distribution of ground-based GNSS. The joint observation with space-based GNSS can further improve the spatio-temporal distribution of ionospheric monitoring. The Tonga volcano erupted on January 15th, 2022, is the largest volcanic eruption since the beginning of the 21st century and generated intense acoustic, atmospheric gravity waves and ionospheric disturbance. In this paper, we used the GIM products, ground-based GNSS station data around the volcanic eruption point and COSMIC occultation data to analyze the short-term TEC change. The experiments showed that: the average TEC of GIM grid data on eruption day dropped by 7.5 TECu compared to the last day which was substantially larger than other three days. The TEC extracted from nearby tracking stations was smaller than GIM and the difference was inversely proportional to the distance between the station and the volcanic eruption site. The average daily ionospheric TEC obtained from the nearest TONG station was the biggest, as much as 3.44 TECu lower. The peak electron density of COSMIC occultation data gradually diminished and then recovered with TEC about 7.32 TECu lower than GIM products. The experments showed that there was a good consistency in the TEC obtained from space-based and ground-based GNSS, the Tonga volcano produced a huge TEC change over 10 TECu or even bigger. As a global grid product, GIM underestimated ionospheric changes due to its limited spatial resolution, which needs further improvement.

Processing of self-related thoughts in experienced users of classic psychedelics: A source localisation EEG study

BackgroundPsychedelics have gained increasing interest in scientific research due to their ability to induce profound alterations in perception, emotional processing and self-consciousness. However, the research regarding the functioning of individuals who use psychedelics in naturalistic contexts remains limited. AimsHere we aim to explore psychological and neurophysiological differences between naturalistic psychedelics users and non-users in terms of processing of self-related thoughts. MethodsWe use behavioural testing combined with electroencephalography (EEG) with source localisation. To mitigate potential confounding effects of personality traits and personal history which makes one willing to take psychedelics, we compared users to individuals who did not take psychedelics, but are intending to do so in the future. To ensure robustness of our results, we included two datasets collected at two different laboratories. ResultsThe results from Dataset I (N = 70) suggest that during self-related thoughts psychedelics users exhibit weaker increases in alpha and beta power in comparison to non-users, primarily in brain regions linked to processing of self-related information and memory (such as posterior cingulate cortex). However, analysis of Dataset II (N = 38) did not replicate between-group effects, possibly due to the smaller sample size and spatial resolution limitations. ConclusionsWhile non-replicability restricts interpretation of our findings, our research expands the ongoing discussion on strength and duration of the psychedelic effects, specifically in brain circuits associated with self-related processing, and its relationship to well-being. Our results fit into growing scepticism about the specificity of the role of default-mode network hubs in changes associated with psychedelics experience.

Decoding the brain: EEG applications in sensory processing, emotion recognition, and pain assessment

This work explores the diverse applications of electroencephalography (EEG) across sensory processing, emotion recognition, and pain assessment. It analyzes recent studies employing EEG to investigate auditory perception, visual processing, emotional states, and pain experiences, highlighting common methodological approaches such as preprocessing techniques, feature extraction methods, and classification algorithms. Key findings reveal EEG’s potential in decoding complex brain states, from identifying neural correlates of music perception to detecting pain signals, and discuss the advancement of machine learning techniques, particularly deep learning, in enhancing the accuracy and real-time capabilities of EEG-based systems. The paper also addresses challenges in EEG research, such as limited spatial resolution and the need for larger, more diverse datasets, and proposes future directions emphasizing multimodal integration, standardization of protocols, and the development of user-friendly tools for clinical applications. This paper underscores EEG’s significant role in advancing our understanding of brain function and its potential to revolutionize neurological diagnostics and treatments.

Scalp surface Laplacian potential monitoring system based on novel hydrogel active tri-polar concentric ring electrodes

Electroencephalography is valued in brain research for its high temporal resolution and user-friendly nature. However, limitations in spatial resolution and susceptibility to biological artifacts restrict its broader application. The surface Laplacian potential obtained through tri-polar concentric ring electrodes offers a solution by mitigating biological artifact interference and enhancing spatial resolution while preserving essential information. In this study, an active tri-polar concentric ring electrode based on conductive hydrogel and a 3-lead surface Laplacian potential monitoring system designed for use with the electrode were proposed. The part of the electrode that came into contact with the human body was the hydrogel with high electrical conductivity. The contact surface curvature was designed based on the real forehead curvature of 22 subjects, resulting in a normalized electrode-skin contact impedance of 30.95±16.76KΩ*cm2 at 10 Hz, which was lower than commercial disposable wet electrodes and remained stable over a 10-hour period of long-term wear. And the electrode still exhibited excellent performance after being left for 20 days. Additionally, the internal noise of the system was about 1.9μV, which was comparable to that of the FDA-approved equipment. The signal analysis results demonstrated that the surface Laplacian potential collected by the proposed electrode and system had better signal-to-noise ratio and spatial resolution, and had a good suppression effect on biological artifacts.

Highly Amplified Broadband Ultrasound in Antiresonant Hollow Core Fibers

High‐frequency broadband ultrasound in nested antiresonant hollow core fibers (NANFs) is investigated for the first time. NANFs have remarkable features enabling high‐resolution microscale optoacoustic imaging sensors and neurostimulators. Solid optical fibers have been successfully employed to measure and generate ultrasonic signals, however, they face issues concerning attenuation, limited frequency range, bandwidth, and spatial resolution. Herein, highly efficient ultrasonic propagation in NANFs from 10 to 100 MHz is numerically demonstrated. The induced pressures and sensing responsivity are evaluated in detail, and important parameters for the development of ultrasonic devices are reviewed. High pressures (up to 234 MPa) and sensing responsivities (up to −207 dB) are tuned over 90 MHz range by changing the diameters of two distinct NANF geometries. To the best of knowledge, this is the widest bandwidth reported using similar diameter fibers. The results are a significant advance for fiber‐based ultrasonic sensors and transmitters, contributing to improve their efficiency and microscale spatial resolution for the detection, diagnosis, and treatment of diseases in biomedical applications.