High Temporal Resolution Images Research Articles

Realistic Aspects of Cardiac Ultrasound in Rats: Practical Tips for Improved Examination.

Echocardiography is a reliable and non-invasive method for assessing cardiac structure and function in both clinical and experimental settings, offering valuable insights into disease progression and treatment efficacy. The successful application of echocardiography in murine models of disease has enabled the evaluation of disease severity, drug testing, and continuous monitoring of cardiac function in these animals. However, there is insufficient standardization of echocardiographic measurements for smaller animals. This article aims to address this gap by providing a guide and practical tips for the appropriate acquisition and analysis of echocardiographic parameters in adult rats, which may also be applicable in other small rodents used for scientific purposes, like mice. With advancements in technology, such as ultrahigh-frequency ultrasonic transducers, echocardiography has become a highly sophisticated imaging modality, offering high temporal and spatial resolution imaging, thereby allowing for real-time monitoring of cardiac function throughout the lifespan of small animals. Moreover, it allows the assessment of cardiac complications associated with aging, cancer, diabetes, and obesity, as well as the monitoring of cardiotoxicity induced by therapeutic interventions in preclinical models, providing important information for translational research. Finally, this paper discusses the future directions of cardiac preclinical ultrasound, highlighting the need for continued standardization to advance research and improve clinical outcomes to facilitate early disease detection and the translation of findings into clinical practice.

What do glass sponges do when no one is looking? Vazella pourtalesii: Responses to sediment deposition, passive locomotion, and contracting behavior

Behavioral response of deep-sea sponges can provide crucial insights into the mechanisms shaping energy fluxes and ecosystem functioning. Although some advances have been made, the behavior of deep-sea Hexactinellid still remain widely unknown. In the present study we address the glass sponge Vazella pourtalesii behavior. High-temporal resolution imaging and environmental data were acquired with an autonomous lander deployed in the Sambro Bank Sponge Conservation Area (Scotian Shelf) at a depth of 150 m, representing the upper limit of this deep-sea species' distribution and what is commonly regarded as the deep sea.For 94 days, a V. pourtalesii individual was monitored, providing quantitative information on its response to sediment deposition triggered by a storm, as well as on its passive locomotion and contractive behavior. Sediment was cleared from its surface within 72 h, which is highly relevant for its filtering capacity, indicating that this sponge species can cope with high suspended sediment concentrations. This enables it to occur on sedimentary environments like the Scotian Shelf. As observed in other deep-sea hexactinellids, the monitored individual engaged in rhythmic contractions, which appear to be driven by physiological process/es rather than environmental factors. During the study period, strong bottom currents (>37 cm/s) toppled and displaced the monitored individual several times. Despite changes in position and orientation that can negatively impact the filtering capacity of sponges, no signs of deterioration were observed. V. pourtalesii's vase-like body morphology and attachment to cobbles, as a gravitational center, may allow it to have a more homogeneous interaction with currents which may permit them to better cope with positional changes. Overall, this study highlights that deep-sea Porifera display a wide array of phenological changes in response to both biotic and abiotic factors.

Single-shot spatial light interference microscopy for dynamic monitoring of membrane fluctuations.

Single-shot spatial light interference microscopy (SS-SLIM) with a pair of non-polarizing beam splitters is proposed for substantially enhancing the speed and efficiency of conventional SLIM systems. Traditional methods are limited by the need for multiple-frame serial modulation and acquisition by spatial light modulators and detectors. Our approach integrates non-polarizing beam splitters to simultaneously capture four phase-shifted intensity images, increasing the imaging speed by at least fourfold while maintaining high quality. This capability is crucial for effectively monitoring the dynamic fluctuations of red blood cell membranes. Furthermore, the potential applications of the SS-SLIM system in biomedical research are demonstrated, particularly in scenarios requiring high temporal resolution and label-free imaging.

Experimental study on the penetration and evaporation characteristics of a liquid kerosene jet in the supersonic crossflow

The penetration and evaporation characteristics of a liquid kerosene jet in the supersonic crossflow were experimentally investigated in this study. The experiments were carried out in both cold and high-enthalpy inflows. Detailed spray images were obtained using planar laser scattering techniques. The structures of the spray field were further analyzed on the basis of high spatial and temporal resolution images. The results show that the atomization and evaporation characteristics of a liquid kerosene jet are related to the crossflow temperature, liquid–gas momentum flux ratio, and injection distance. It is found that the breakup process of a liquid jet is accelerated in the high-enthalpy inflow. To accurately describe the maximum flow distance along the direction that kerosene can reach in the state of droplets, the survival distance is defined. It is revealed that the penetration depth and survival distance of the liquid kerosene jet decrease clearly with increase in the crossflow temperature. For the cavity-based combustor, the liquid kerosene jet can mix more sufficiently in the cavity region by reducing the injection distance and liquid–gas momentum flux ratio.

Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Climate warming has induced the thawing of permafrost, which increases the probability of thaw slump occurrences in permafrost regions of the Qinghai–Tibet Engineering Corridor (QTEC). As a key and important corridor, thaw slump distribution is widespread, but research into effectively using neural networks to predict thaw slumping remains insufficient. This study automated the identification of thaw slumps within the QTEC and investigated their environmental factors and susceptibility assessment. We applied a deep learning-based semantic segmentation method, combining U-Net with ResNet101, to high spatial and temporal resolution images captured by the Gaofen-1 images. This methodology enabled the automatic delineation of 455 thaw slumps within the corridor area, covering 40,800 km², with corresponding precision, recall, and F1 scores of 0.864, 0.847, and 0.856, respectively. Subsequently, employing a radial basis function neural network model on this inventory of thaw slumps, we investigated environmental factors that could precipitate the occurrence of thaw slumps and generated sensitivity maps of thaw slumps along the QTEC. The model demonstrated high accuracy, and the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve reached 0.95. The findings of the study indicate that these thaw slumps are predominantly located on slopes with gradients of 1–18°, distributed across mid-elevation regions ranging from 4500 to 5500 m above sea level. Temperature and precipitation were identified as the predominant factors that influenced the distribution of thaw slumps. Approximately 30.75% of the QTEC area was found to fall within high to extremely high susceptibility zones. Moreover, validation processes confirmed that 82.75% of the thaw slump distribution was located within areas of high or higher sensitivity within the QTEC.

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Mitochondrial Ca2+-coupled generation of reactive oxygen species causes endothelial dysfunction in Cantú syndrome

Cantú syndrome is a multisystem disorder caused by gain-of-function (GOF) mutations in KCNJ8 and ABCC9, the genes encoding the pore-forming inward rectifier Kir6.1 and regulatory sulfonylurea receptor SUR2B subunits of vascular ATP-sensitive K+ channels (KATP). In this study, we investigated the effects of Cantú syndrome on the vascular endothelium using mutant mice that model the disease. We found that endothelium-dependent dilation was impaired in small mesenteric arteries from Cantú mice. Loss of endothelium-dependent vasodilation led to increased vasoconstriction in response to intraluminal pressure and to the adrenergic receptor agonist phenylephrine. We also found that KATP GOF and acute activation of KATP channels with pinacidil increased the amplitude and frequency of wave-like Ca2+ events that were generated in the endothelium in response to the vasodilator agonist carbachol (CCh). Elevated cytosolic Ca2+ signaling activity in arterial endothelial cells from Cantú mice was associated with high mitochondrial [Ca2+], reactive oxygen species (ROS) generation, and peroxynitrite levels. Scavenging intracellular and mitochondrial ROS restored endothelium-dependent vasodilation in the arteries of mice with KATP GOF mutations. We conclude that mitochondrial Ca2+ overload and ROS generation cause endothelial dysfunction in mice with Cantú syndrome. This study was supported by grants from NHLBI (R35HL155008) and NIGMS (P20GM130459) to S.E., and NIH (R35HL140024) to C.G.N., and NIH (R00HL150277) to C.M. The Transgenic Genotyping and Phenotyping Core and the High Spatial and Temporal Resolution Imaging Core at the COBRE Center for Molecular and Cellular Signaling in the Cardiovascular System, University of Nevada, Reno is maintained by grants from NIH/NIGMS (P20GM130459). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement.

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

64 picosecond time resolved time-correlated single photon counting imaging.

High-speed imaging of dynamic scenes is a challenging and important task in many applications. However, conventional imaging methods based on charge coupled devices or complementary metal oxide semiconductors have limitations in temporal resolution and photon sensitivity. To address this problem, we propose a novel high-speed imaging scheme that combines single-pixel imaging with single photon detection and time-correlated single photon counting. Our scheme can achieve high-speed imaging with 64ps resolution by repeating the motion scenes and using binary outputs from single photon detectors. We demonstrate our scheme by reconstructing the switching process of a digital micro-mirror device and a liquid crystal spatial light modulator. Our scheme can be further improved to 1ps resolution by using a more accurate time-correlated single photon counting system. Moreover, our scheme can adapt to different speed scenes by adjusting the temporal resolution and reducing the sampling time. Our high temporal resolution imaging scheme further expands the application areas of single-pixel imaging and provides solutions for scenes requiring single photon detection and higher temporal resolution, such as reproducible chemical reaction processes imaging, cellular or sub-cellular bio imaging, single-molecule imaging of rotary motors, high-speed equipment inspection, and other periodic high-speed scenes imaging.

Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection

Abstract. The frequency and intensity of coastal flooding is expected to accelerate in low-elevation coastal areas due to sea level rise. Coastal flooding due to wave overtopping affects coastal communities and infrastructure; however, it can be difficult to monitor in remote and vulnerable areas. Here we use a camera-based system to measure beach and back-beach flooding as part of the after-storm recovery of an eroded beach on the Texas coast. We analyze high-temporal resolution images of the beach using convolutional neural network (CNN)-based semantic segmentation to study the stochastic properties of flooding events. In the first part of this work, we focus on the application of semantic segmentation to identify water and overtopping events. We train and validate a CNN with over 500 manually classified images and introduce a post-processing method to reduce false positives. We find that the accuracy of CNN predictions of water pixels is around 90 % and strongly depends on the number and diversity of images used for training.

A satellite chronology of plumes from the April 2021 eruption of La Soufrière, St Vincent

Abstract. Satellite instruments play a valuable role in detecting, monitoring and characterising emissions of ash and gas into the atmosphere during volcanic eruptions. This study uses two satellite instruments, the Infrared Atmospheric Sounding Interferometer (IASI) and the Advanced Baseline Imager (ABI), to examine the plumes of ash and sulfur dioxide (SO2) from the April 2021 eruption of La Soufrière, St Vincent. The frequent ABI data have been used to construct a 14 d chronology of a series of explosive events at La Soufrière, which is then complemented by measurements of SO2 from IASI, which is able to track the plume as it is transported around the globe. A minimum of 35 eruptive events were identified using true, false and brightness temperature difference maps produced with the ABI data. The high temporal resolution images were used to identify the approximate start and end times, as well as the duration and characteristics of each event. From this analysis, four distinct phases within the 14 d eruption have been defined, each consisting of multiple explosive events with similar characteristics: (1) an initial explosive event, (2) a sustained event lasting over 9 h, (3) a pulsatory phase with 25 explosive events in a 65.3 h period and (4) a waning sequence of explosive events. It is likely that the multiple explosive events during the April 2021 eruption contributed to the highly complex plume structure that can be seen in the IASI measurements of the SO2 column amounts and heights. The bulk of the SO2 from the first three phases of the eruption was transported eastwards, which based on the wind direction at the volcano implies that the SO2 was largely in the upper troposphere. Some of the SO2 was carried to the south and west of the volcano, suggesting a smaller emission of the gas into the stratosphere, there being a shift in wind direction around the height of the tropopause. The retrieved SO2 heights show that the plume had multiple layers but was largely concentrated between 13 and 19 km, with the majority of the SO2 being located in the upper troposphere and around the height of the tropopause, with some emission into the stratosphere. An average e-folding time of 6.07±4.74 d was computed based on the IASI SO2 results: similar to other tropical eruptions of this magnitude and height. The SO2 was trackable for several weeks after the eruption and is shown to have circulated the globe, with parts of it reaching as far as 45∘ S and 45∘ N. Using the IASI SO2 measurements, a time series of the total SO2 mass loading was produced, with this peaking on 13 April (descending orbits) at 0.31±0.09 Tg. Converting these mass values to a temporally varying SO2 flux demonstrated that the greatest emission occurred on 10 April with that measurement incorporating SO2 from the second phase of the eruption (sustained emission) and the beginning of the pulsatory phase. The SO2 flux is then shown to fall during the later stages of the eruption: suggesting a reduction in eruptive energy, something also reflected in ash height estimates obtained with the ABI instrument. A total SO2 emission of 0.63±0.5 Tg of SO2 has been derived, although due to limitations associated with the retrieval, particularly in the first few days after the eruption began, this, the retrieved column amounts and the total SO2 mass on each day should be considered minimum estimates. There are a number of similarities between the 1979 and 2021 eruptions at La Soufrière, with both eruptions consisting of a series of explosive events with varied heights and including some emission into the stratosphere. These similarities highlight the importance of in-depth investigations into eruptions and the valuable contribution of satellite data for this purpose; as these studies aid in learning about a volcano's behaviour, which may allow for better preparation for future eruptive activity.

Advances in Ultrafast Fiber Lasers for Multiphoton Microscopy in Neuroscience

Multiphoton microscopy (MPM) has emerged as a vital tool in neuroscience, enabling deeper imaging with a broader field of view, as well as faster and sub-cellular resolution. Recent innovations in ultrafast fiber laser technology have revolutionized MPM applications in living brains, offering advantages like cost-effectiveness and user-friendliness. In this review, we explore the progress in ultrafast fiber laser technology, focusing on its integration into MPM for neuroscience research. We also examine the utility of femtosecond fiber lasers in fluorescence and label-free two- and three-photon microscopy applications within the field. Furthermore, we delve into future possibilities, including next-generation fiber laser designs, novel laser characteristics, and their potential for achieving high spatial and temporal resolution imaging. We also discuss the integration of fiber lasers with implanted microscopes, opening doors for clinical and fundamental neuroscience investigations.

Snow Cover Detection Using Multi-Temporal Remotely Sensed Images of Fengyun-4A in Qinghai-Tibetan Plateau

Differentiating between snow and clouds presents a formidable challenge in the context of mapping snow cover over the Qinghai–Tibetan Plateau (QTP). The frequent presence of cloudy conditions severely complicates the discrimination of snow cover from satellite imagery. To accurately monitor the spatiotemporal evolution of snow cover, it is imperative to address these challenges and enhance the segmentation schemes employed for snow cover assessment. In this study, we devised a pixel-wise classification algorithm based on Support Vector Machine (SVM) called the 3-D Orientation Gradient algorithm (3-D OG), which captures the variations of the gradient direction of snow and clouds in spatiotemporal dimensions based on geostationary satellite “Fengyun-4A” (FY-4A) multi-spectral and multi-temporal optical imagery. This algorithm assumes that the speed and direction of clouds and snow are different in the process of movement leading to their discrepancy of gradient characteristics in time and space. Therefore, in this algorithm, the gradient of the images in the spatiotemporal dimensions is calculated first, and then the movement angle and trend are obtained based on that. Finally, the feature space is composed of the multi-spectral image, gradient image, and movement feature maps, which are used as the input of the SVM. Our results demonstrate that the proposed algorithm can identify snow and clouds more accurately during snowfall by utilizing the FY-4A’s high temporal resolution image. Weather station data, which was collected during snowstorms in the QTP, were used for evaluating the accuracy of our algorithm. It is demonstrated that the overall accuracy of snow cover segmentation by using the 3-D OG algorithm is improved by at least 12% and 10% as compared to snow products of Fengyun-2 and MODIS, respectively. Overall, the proposed algorithm has overcome the axial swing errors existing in Geostationary satellites and is successfully applied to cloud and snow segmentation in QTP. Furthermore, our study underscores that the visible and near-infrared bands of Fengyun-4A can be used for near real-time snow cover monitoring with high performance using the 3-D OG algorithm.

Open Foris Collect Earth: a remote sensing sampling survey of Azerbaijan to support climate change reporting in the land use, land use change, and forestry.

Land use, land use change, and forestry (LULUCF) are critical in climate change mitigation. Producing or collecting activity data for LULUCF is essential in developing national greenhouse gas inventories, national communications, biennial update reports, and nationally determined contributions to meet international commitments under climate change. Collect Earth is a free, publicly accessible software for monitoring dynamics between all land use classes: forestlands, croplands, grasslands, wetlands, settlements, and other lands. Collect Earth supports countries in monitoring the trends in land use and land cover over time by applying a sample-based approach and generating reliable, high-quality, consistent, accurate, transparent, robust, comparable, and complete activity data through augmented visual interpretation for climate change reporting. This article reports forest extent estimates in Azerbaijan, analyzing 7782 0.5-ha sampling units through an augmented visual interpretation of very high spatial and temporal resolution images on the Google Earth platform. The results revealed that in 2016, tree cover existed in 31.9% of total land, equal to 2,751,167ha and 1,301,188ha or 15.1% of the total land, with a 5.4% sampling error covered by forests. The estimate is 15 to 25% higher than the previous estimates, equal to 169,418 to 260,888ha of forest that was never reported in previous studies.

Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change

Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and developing relatively simple and 1 easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes.

Dynamic cell tracking using time-lapse MRI with variable temporal resolution Cartesian sampling.

Temporal resolution of time-lapse MRI to track individual iron-labeled cells is limited by the required data-acquisition time to fill k-space and to reach sufficient SNR. Although motion of slowly patrolling monocytes can be resolved, detection of fast-moving immune cells requires improved acquisition and reconstruction strategies. For accelerated MRI cell tracking, a Cartesian sampling scheme was designed, in which the fully sampled and undersampled k-space data for different acceleration factors were acquired simultaneously, and multiple undersampling ratios could be chosen retrospectively. Compressed-sensing reconstruction was applied using dictionary learning and low-rank constraints. Detection of iron-labeled monocytes was evaluated with simulations, rotating phantom experiments and in vivo mouse brain measurements at 9.4 T. Fully sampled and 2.4-times and 4.8-times accelerated images were reconstructed and had sufficient contrast-to-noise ratio (CNR) for single cells to be resolved and followed dynamically. The phantom experiments showed an improvement in CNR of 6.1% per μm/s in the 4.8-times undersampled images. Geometric distortion of cells caused by motion was visibly reduced in the accelerated images, which enabled detection of moving cells with velocities of up to 7.0 μm/s. In vivo, additional cells were resolved in the accelerated images due to the improved temporal resolution. The easy-to-implement flexible Cartesian sampling scheme with compressed-sensing reconstruction permits simultaneous acquisition of both fully sampled and high temporal resolution images. The CNR of moving cells is effectively improved, enabling the recovery of high velocity cells with sufficient contrast at virtually no cost.

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review.

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

Occurrence and behavioral rhythms of the endangered Acadian redfish (Sebastes fasciatus) in the Sambro Bank (Scotian Shelf)

The genus Sebastes is a morphologically and ecologically diverse genus of rockfish characterized by high longevity, late-maturity and low natural mortality. On the northwest Atlantic continental shelf, the Acadian redfish (Sebastes fasciatus) is the most common rockfish species above 300 m depth. This species has been widely exploited resulting in the depletion or collapse of most of its stocks. Management of long-lived species with intricate life-history characteristics is challenging and requires highly integrated biological and oceanographic monitoring, which allow the identification of environmental drivers and demographic and behavioral trends. The present study uses high-temporal resolution imaging and environmental data, acquired with an autonomous lander deployed for 10-months at the Sambro Bank Sponge Conservation Area (Scotian Shelf) to elucidate S. fasciatus temporal dynamics and behavioral trends in response to near-bed environmental conditions. S. fasciatus, mostly displayed passive locomotion and static behaviors, in common with other shelf-dwelling Sebastes species. Structural complexity provided by sponges positively influenced S. fasciatus presence. Fish used sponges to avoid being dragged by bottom currents. Hydrodynamics appear to act as a synchronizing factor conditioning its swimming behavior. S. fasciatus total counts exhibited a seasonal shift in rhythm’s phase likely reflecting changes in lifestyle requirements. This study provides new insights on S. fasciatus dynamics and behavior. Nonintrusive monitoring approaches, such as the one used in this study, will be key to monitor this threatened species populations. Especially, since it is expected that S. fasciatus will experience distribution shifts to higher latitudes due to future climate stressors.

Spectral–Temporal Fusion of Satellite Images via an End-to-End Two-Stream Attention With an Effective Reconstruction Network

Due to technical and budget constraints on current optical satellites, the acquisition of satellite images with the best resolutions is not practicable. In this article, aiming to produce products with high spectral (HS) and temporal resolutions, we introduced a two-stream spectral–temporal fusion technique based on attention mechanism called STA-Net. STA-Net aims to combine high spectral and low temporal (HSLT) resolution images with low spectral and high temporal (LSHT) resolution images to generate products with the best characteristics. The proposed technique involves two stages. In the first one, two fused images are generated by a two-stream architecture based on residual attention blocks. The temporal difference estimator stream estimates the temporal difference between HS images at desired and neighboring dates. The reflectance difference estimator is the second stream. It predicts the reflectance difference between the input images (HS–LS) to map LS images into HS products. In the second stage, a reconstruction network combines the latter two-stream outputs via an effective learnable weighted-sum strategy. The two-stage model is trained in an end-to-end fashion using an effective loss function to ensure the best fusion quality. To the best of our knowledge, this work represents the first attempt to address the spectral–temporal fusion using an end-to-end deep neural network model. Experimental results conducted on two actual datasets of Sentinel-2 (HSLT:10 spectral bands and long revisit period) and Planetscope (LSHT: four spectral bands and daily images) images, which proved the effectiveness of the proposed technique with respect to baseline technique.

High-Resolution Planetscope Imagery and Machine Learning for Estimating Suspended Particulate Matter in the Ebinur Lake, Xinjiang, China

Ebinur Lake is a shallow lake and vulnerable to strong winds, which can lead to drastic changes in suspended particulate matter (SPM). High spatial and temporal resolution images are therefore urgently needed for SPM monitoring over the Ebinur Lake. Hence, a high-efficiency inversion model of estimating SPM from high-resolution images using machine learning is essential to increase the amount of extracted information through band combinations quadratic optimization. This article aims to evaluate the capability of the PlanetScope images and four machine learning approaches for estimating SPM of the Ebinur Lake. The specific objectives include: to obtain the sensitive bands and band combinations for SPM using correlation analysis; to quadratically optimize the combination pattern of sensitive bands using a linear model; and to compare the accuracy of traditional linear model and machine learning models in estimating SPM. The results of the study confirm that after linear model quadratic optimization, the band combinations of B3*B4, (B2+B3)/ (B2-B3), (B3+B4)*(B3+B4), and (B3-B2)/(B2/B3) have higher accuracy than that of the single band model. By inputting the preferred four-band combinations into the partial least squares, random forest, extreme gradient boosting, gradient boosting decision tree, and categorical boosting (CatBoost) models, the performance of the SPM inversion based on PlanetScope images is better than the traditional linear model. Validation of the inversion maps with observations further indicates that the CatBoost model performed the best.