High Temporal Resolution Research Articles

Patch-scale habitat dynamics: three metrics to assess ecological impacts of frequent hydropeaking

Human activities significantly alter natural river flows, impacting ecosystem functioning and biodiversity worldwide. Hydropeaking, resulting from intermittent on-demand hydropower generation, introduces sub-daily flow fluctuations exceeding natural variability. While the effects of single hydropeaking events are well-studied, the cumulative impacts of frequent hydropeaking requires further exploration. This study aims to develop metrics that captures changes in habitat dynamics at the patch scale (i.e. individual micro-habitats within the habitat mosaic) due to reoccurring hydropeaking. Using hydrodynamic simulations, we introduce three patch-scale metrics to quantify habitat dynamics with high spatial (0.5 m) and temporal (10 min) resolution: (M1) Habitat probability within patches, assessing spatio-temporal diversity of habitats; (M2) Habitat shifts within patches, evaluating habitat persistence for sessile organisms (e.g. vegetation, invertebrates); and (M3) Spatial shifts of habitats, indicating habitat relocation affecting mobile species (e.g. adult fish). Using eight hydro-morphological scenarios representing different levels of anthropogenic modification of flow and morphology, we demonstrate that these metrics effectively quantify changes in habitat dynamics at patch-scale. The results highlight the ecological relevance of these metrics and their potentially utility for river management. By identifying areas susceptible to ecological impacts, these metrics may serve as tools for hydropeaking mitigation, enabling more targeted and spatially explicit habitat management and restoration.

Studying the structure and function of nucleosomes by atomic force microscopy

Chromatin is an epigenetic platform for the implementation of DNA-dependent processes. The nucleosome, as the basic level of chromatin compaction, largely determines its properties and structure. When studying the structure and functions of nucleosomes, physicochemical tools are actively used, such as magnetic and optical “tweezers,” “DNA curtains,” nuclear magnetic resonance, X-ray diffraction analysis and cryoelectron microscopy, as well as optical methods based on FRET. Despite the fact that these approaches make it possible to determine a wide range of structural and functional characteristics of chromatin and nucleosomes with high spatial and temporal resolution, atomic-force microscopy (AFM) complements the capabilities of these methods. This review presents the results of structural studies of nucleosomes in view of the development of the AFM method. The capabilities of AFM are considered in the context of the use of other physicochemical approaches.

A broadband hyperspectral image sensor with high spatio-temporal resolution

Hyperspectral imaging provides high-dimensional spatial–temporal–spectral information showing intrinsic matter characteristics1–5. Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible–near-infrared (400–1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.

General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

Triggering mechanics and early warning for snowmelt-rainfall-induced loess landslide.

Loess landslides represent a significant natural hazard, especially in arid and semi-arid regions where slopes are exposed to prolonged snowmelt and rainfall infiltration. To analyze landslide stability and conduct early warning evaluations effectively, a robust monitoring system and appropriate equipment are crucial. This study utilizes historical data from the Kalahaisu landslide in Xinyuan County, Ili region, which was monitored using various techniques. The research assesses the time resolution, spatial resolution, and data accuracy of these monitoring methods. Additionally, the study explores the landslide disaster patterns and early warning indicators for the Kalahaisu landslide. It suggested that the deformation of the rainfall- and snowmelt- landslide is not only affected by the seasonal climates but also closely related to the internal structures of the loess. The other observation is that the respective time and spatial resolution of monitoring instrument would significantly affect the the interpretation time of the landslide deformation. Instruments with higher spatial resolution can more effectively identify unstable areas within a landslide, while instruments with higher temporal resolution can provide detailed time-series deformation data. Therefore, real-time and comprehensive monitoring of landslides using a suitable combination of monitoring equipment can provide strong data support for landslide stability evaluation and deformation trend prediction. Full-area monitoring techniques, such as Synthetic Aperture Radar (SAR) and equipment that measures changes in internal properties like deep water content, deep displacement, and pore pressure, offer a more comprehensive and effective approach to monitoring and interpreting landslide deformation. The insights gained from this research may enhance the prediction and prevention of loess landslides in the Ili River valley.

Measuring and modelling directional effects in the frame of TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope)

Abstract. TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose thermal infrared (TIR) Earth Observation (EO) mission designed to deliver images at high spatial (60 m) and temporal (3 days) resolutions. Its launch is foreseen in 2026 with a nominal mission duration of 5 years. This Indo-French polar-orbiting mission will overcome the limitations of thermal-optical observations from Landsat series and ASTER: low revisit, morning observations. TRISHNA scenes will be fully harnessed to pioneer the first cutting-edge high-resolution global maps of the land surface temperature (LST) and land surface emissivity (LSE) of natural and managed agroecosystems, man-made structures, water bodies, bare soils, rocks, snow, ice and sea. The quality of the preprocessing (radiometric calibration, atmospheric and directional corrections) is mandatory to satisfy the specifications with an expected precision of 1 K on LST in order to meet the targeted objectives. For such, it will be necessary to correct LST from directional effects with emphasis on the hot spot effect that can have an impact on LST up to several K. This is the goal of the TIRAMISU project to analyze TIR multiangular signatures over various biomes thanks to a pivoting system of cameras and a multi-model approach (1D, 3D, paramaterization). The modeling tools include 3 categories of models: SCOPE 1D, DART 3D and parametric BRDF models that are computationally efficient to perform inversion at global scale.

Heat application in live cell imaging.

Thermal heating of biological samples allows to reversibly manipulate cellular processes with high temporal and spatial resolution. Manifold heating techniques in combination with live-cell imaging were developed, commonly tailored to customized applications. They include Peltier elements and microfluidics for homogenous sample heating as well as infrared lasers and radiation absorption by nanostructures for spot heating. A prerequisite of all techniques is that the induced temperature changes are measured precisely which can be the main challenge considering subcellular structures or multicellular organisms as target regions. This article discusses heating and temperature sensing techniques for live-cell imaging, leading to future applications in cell biology.

In vitro/In vivo oxygen electrochemical nanosensor for bioanalysis

Direct in vivo monitoring of O2 is critical in the study of numerous biological processes, and the development of novel highly sensitive techniques for O2in vivo detection is of great potential importance. Electrochemical amperometric sensors are some of the most promising, easy to operate, inexpensive, sensitive and have a high temporal resolution. As part of this work, we have demonstrated a direct rapid method for molecular oxygen detection inside multicellular spheroids in vitro and rat brain in vivo. External and internal oxygen profiles in human adenocarcinoma MCF-7 spheroids were studied using developed nanoelectrode. The size of spheroids was shown to affect the level of hypoxia inside them, and also affected the oxygen consumption near spheroids in solution. To demonstrate the in vivo relevance of the novel sensor we used it to measure the oxygen concentrations in the superficial layers of the rat brain. This study demonstrates a minimally invasive electrochemical method for real-time oxygen profiling in vivo.

SpectraTrack: megapixel, hundred-fps, and thousand-channel hyperspectral imaging

Hyperspectral imaging (HSI) finds broad applications in various fields due to its substantial optical signatures for the intrinsic identification of physical and chemical characteristics. However, it faces the inherent challenge of balancing spatial, temporal, and spectral resolution due to limited bandwidth. Here we present SpectraTrack, a computational HSI scheme that simultaneously achieves high spatial, temporal, and spectral resolution in the visible-to-near-infrared (VIS-NIR) spectral range. We deeply investigated the spatio-temporal-spectral multiplexing principle inherent in HSI videos. Based on this theoretical foundation, the SpectraTrack system uses two cameras including a line-scan imaging spectrometer for temporal-multiplexed hyperspectral data and an auxiliary RGB camera to capture motion flow. The motion flow guides hyperspectral reconstruction by reintegrating the scanned spectra into a 4D video. The SpectraTrack system can achieve around megapixel HSI at 100 fps with 1200 spectral channels, demonstrating its great application potential from drone-based anti-vibration video-rate HSI to high-throughput non-cooperative anti-spoofing.

Recovery of coronal dimmings

Context. Coronal dimmings are regions of reduced emission in the lower corona observed in the wake of coronal mass ejections (CMEs), representing their footprints. Studying the lifetime evolution of coronal dimmings helps us to better understand the recovery and replenishment of the corona after large-scale eruptions. Aims. We study the recovery of dimmings on different spatial scales to enhance our understanding of the replenishment and dynamics of the corona after CMEs. Methods. In order to investigate the long-term evolution of coronal dimming and its recovery, we propose two approaches that focus on both the global and the local evolution of dimming regions: the fixed mask approach and the pixel boxes approach. We present four case studies (September 6, 2011; March 7, 2012; June 14, 2012; and March 8, 2019) in which a coronal dimming is associated with a flare/CME eruption. We analyzed each event with the same methodology, using extreme-ultraviolet filtergrams from the Solar Dynamics Observatory’s Atmospheric Imaging Assembly (SDO/AIA) and Solar TErrestrial RElations Observatory’s Extreme UltraViolet Imager (STEREO/EUVI) instruments. We identified the dimming region by image segmentation, then restricted the analysis to a specific portion of the dimming and tracked the time evolution of the dimming brightness and area. In addition, we study the behavior of small subregions inside the dimming area, of about 3 × 3 pixels, to compare the recovery in different regions of the dimming. Results. Three out of the four cases show a complete recovery 24 hours after the flare/CME eruption. The primary recovery mechanism identified in the observations is the expansion of coronal loops into the dimming region. The recovery of the brightness follows a two-step trend, with a steeper and quicker segment followed by a slower one. In addition, some parts of the dimming, which may be core dimmings, are still present at the end of the analysis time and do not recover within 3 days, whereas the peripheral regions (secondary dimmings) show a full recovery. Conclusions. The high temporal and spatial resolution of SDO/AIA observations combined with multi-view data of the STEREO/EUV instrument reveal high-situated coronal loops expanding after CME eruptions, which cover dimming regions and gradually increase their intensity. Our developed approaches enable the analysis of dimmings alongside these bright structures, revealing different timescales of recovery for core and secondary or twin dimming regions. Combined with magnetic field modeling, these methods lay the foundation for further systematic analysis of dimming recovery and enhance the knowledge gained from already-analyzed events.

Thermal helium beam diagnostic for 2D profile measurements in the divertor of ASDEX Upgrade.

A new thermal helium beam diagnostic has been implemented in the outer lower divertor of the ASDEX Upgrade tokamak. The purpose of this diagnostic is to measure two-dimensional profiles of electron density (ne) and temperature (Te) with high temporal and spatial resolution. The geometry of the lines of sight is chosen to avoid the influence of prompt recycling and to optimize the resolution without significantly impacting the divertor structure. Moreover, the effect of long-term helium recycling has been analyzed, and its amplitude compared to the active signal is negligible. Finally, the reconstruction of ne and Te is done via a collisional radiative model, while a static and a dynamic model were implemented and compared with SOLPS simulations as well as divertor Thomson scattering data. Furthermore, a new 2D parameterization of the outer divertor volume, which is required for the dynamic model, was developed. Due to its fast and local ne and Te profile measurements, the diagnostic is suitable for investigating fast processes such as divertor transitions and filaments.

Citizen-Based Water Quality Monitoring: Field Testing a User-Friendly Sensor for Phosphate Detection in Global Surface Waters.

Widespread concern over surface water pollution has led to interest in developing easy-to-use accurate tools for citizen-based measurements that provide high spatial and temporal resolution while maintaining accuracy. Excessive anthropogenic phosphate significantly contributes to global eutrophication and necessitates regular on-site phosphate monitoring in surface waters. Traditional instrumentation for quantifying phosphate is labor-intensive, expensive, and performed in laboratories. Existing on-site testing methods relying on phosphomolybdenum blue (PMB) have limited sensitivity and stability under ambient conditions. To overcome these limitations, a novel low-cost, rapid, and user-friendly sensor for citizen-led phosphate monitoring in surface water is introduced and demonstrated with a global sampling campaign. The fast-flow microfluidic device provides user-friendly operation, achieving an environmentally relevant limit of detection (LoD) of 190 ppb, which is near the EPA-recommended maximum for phosphate. The dip-and-read operation reduces procedural steps while delivering accurate sample volume, making it well-suited for citizen-led science initiatives. This sensor exhibits high selectivity and prolonged stability for two months under ambient conditions. The sensor's performance was validated using the industry-standard UV-Vis method with 90% correlation. More than 1000 sensors were deployed in different continents, facilitating phosphate mapping in diverse water sources across multiple continents. The initiative covered much of the globe, including Thailand, Nepal, Brazil, Chile, the USA, and Germany. In some cases, phosphate levels exceeded legislative guidelines by 100-fold. Through the collaboration of citizen scientists, we analyzed regional topography and socioeconomic practices near water sources, identifying potential sources that could contribute to eutrophication in these areas.

Data-Supported Prediction of Surface Settlement Behavior on Opencast Mine Dumps Using Satellite-Based Radar Interferometry Observations

To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. This dual approach—leveraging advanced data analysis tools and precise modeling—yields valuable insights into spatial settlement behavior. In particular, classic time–settlement models are applied to the InSAR data through least square regression and Taylor approximation. The integration of both approaches enables the more robust, data-validated forecasts of key geotechnical indicators, such as the time to settlement stabilization and the expected maximum settlement over large areas. An application at a mine in central Germany illustrates the method by generating spatial predictions of the settlement behavior over more than 200 ha. In general, the results provide a comprehensive dataset for investigating other factors influencing the settlement behavior of opencast mine dumps.

High temporal resolution paleoclimate reconstruction by the analysis of growth patterns and stable isotopes of fossil shells of the long-lived bivalve Mercenaria stimpsoni from MIS 5e, 7 and 9

High temporal resolution paleoclimate reconstruction by the analysis of growth patterns and stable isotopes of fossil shells of the long-lived bivalve Mercenaria stimpsoni from MIS 5e, 7 and 9

Generation of heat and electricity load profiles with high temporal resolution for Urban Energy Units using open geodata

Generation of heat and electricity load profiles with high temporal resolution for Urban Energy Units using open geodata

Pain-Discriminating Information Decoded From Spatiotemporal Patterns of Hemodynamic Responses Measured by fMRI in the Human Brain.

Functional magnetic resonance imaging (fMRI) has been widely used in studying the neural mechanisms of pain in the human brain, primarily focusing on where in the brain pain-elicited neural activities occur (i.e., the spatial distribution of pain-related brain activities). However, the temporal dynamics of pain-elicited hemodynamic responses (HDRs) measured by fMRI may also contain information specific to pain processing but have been largely neglected. Using high temporal resolution fMRI (TR = 0.8 s) data acquired from 62 healthy participants, in the present study we aimed to test whether pain-distinguishing information could be decoded from the spatial pattern of the temporal dynamics (i.e., the spatiotemporal pattern) of HDRs elicited by painful stimuli. Specifically, the peak latency and the response duration were used to characterize the temporal dynamics of HDRs to painful laser stimuli and non-painful electric stimuli, and then were compared between the two conditions (i.e., pain and no-pain) using a voxel-wise univariate analysis and a multivariate pattern analysis. Furthermore, we also tested whether the two temporal characteristics of pain-elicited HDRs and their spatial patterns were associated with pain-related behaviors. We found that the spatial patterns of HDR peak latency and response duration could successfully discriminate pain from no-pain. Interestingly, we also observed that the Pain Vigilance and Awareness Questionnaire (PVAQ) scores were correlated with the average response duration in bilateral insula and secondary somatosensory cortex (S2) and could also be predicted from the across-voxel spatial patterns of response durations in the middle cingulate cortex and middle frontal gyrus only during painful condition but not during non-painful condition. These findings indicate that the spatiotemporal pattern of pain-elicited HDRs may contain pain-specific information and highlight the importance of studying the neural mechanisms of pain by taking advantage of the high sensitivity of fMRI to both spatial and temporal information of brain responses.

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.

Multiple independent components contribute to event-related potential correlates of conscious vision

Research has revealed two major event-related potential (ERP) markers of visual awareness: the earlier Visual Awareness Negativity (VAN, around 150–250 ms after stimulus onset), and the following Late Positivity (LP, around 300–500 ms after stimulus onset). Understanding the neural sources that give rise to VAN and LP is important in order to understand what kind of neural processes underlie conscious visual perception. Although the ERPs afford high temporal resolution, their spatial resolution is limited because multiple separate neural sources sum up at the scalp level. In the present study, we sought to characterize the locations and time-courses of independent neural sources underlying the ERP correlates of visual awareness by means of Independent Component Analysis (ICA). ICA allows identifying and localizing the temporal dynamics of different neural sources that contribute to the ERP correlates of conscious perception. The present results show that the cortical sources of VAN are localized to posterior areas including occipital and temporal cortex, while LP reflects a combination of multiple sources distributed among frontal, parietal and occipito-temporal cortex., Our findings suggest that conscious vision correlates with dynamically changing neural sources, developing in part in “accumulative fashion”: consciousness-related activity initially arises in few early sources and, subsequently, additional sources are engaged as a function of time.The results further suggest that even early latency neural sources that correlate with conscious perception may also associate with action-related processes.

Point spread function engineering enable resolution enhanced imaging for Interferenceless coded aperture correlation holography

The point spread function (PSF) of an optical system could characterize the resolving ability of the whole optical system for point light sources. Therefore, the imaging performance of the system could be significantly improved by regulating and optimizing the PSF. In this paper, we innovatively propose a single-exposure hologram resolution enhanced cross-correlation (RECC) method for Interferenceless coded aperture holography(I-COACH) system, circumventing the necessity to obtain the point spread hologram (PSH) of an ideal point object. The RECC method firstly acquires an approximate image of a large-size point object by Lucy-Richardson (LR) algorithm in lens imaging mode, and takes it as a PSF to acquire a PSH with ideal size of the I-COACH system by LR algorithm again, and finally acquires a reconstructed image by the single-exposure hologram RECC method. In the RECC method, the approximate ideal PSHs at different axial positions of the system are acquired by offline operation, therefore, it has a high imaging temporal resolution, and the imaging transverse resolution is not affected by the size of the point objects at the time of recording the PSH, which provides a high imaging signal-to-noise ratio and stable resolution. The proposed method provides powerful technical support for further extending the application field of the I-COACH system, and provides technical reference for other incoherent imaging.

Spiking Neural Networks for Real-Time Pedestrian Street-Crossing Detection Using Dynamic Vision Sensors in Simulated Adverse Weather Conditions

This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in dynamic, low-light, and high-contrast environments, this research evaluates the effectiveness of SNNs compared to traditional Convolutional Neural Networks (CNNs). The experimental setup involved a custom dataset from the CARLA simulator, designed to mimic real-world variability, including rain, fog, and varying lighting conditions. Additionally, the JAAD dataset was adopted to allow for evaluations using real-world data. The SNN models were optimized using Temporally Effective Batch Normalization (TEBN) and benchmarked against well-established deep learning models, concerning their accuracy, computational efficiency, and energy efficiency in complex weather conditions. This study also conducted a comprehensive analysis of energy consumption, highlighting the significant reduction in energy usage achieved by SNNs when processing DVS data. The results indicate that SNNs, when integrated with DVSs, not only reduce computational overhead but also dramatically lower energy consumption, making them a highly efficient choice for real-time applications in autonomous vehicles (AVs).