Explore the vast range of titles available.

Leveraging high‐resolution 7 T magnetic resonance imaging (MRI) and vessel distance mapping (VDM), the arterial supply patterns and dominances of the motor cortex, which could previously only be studied postmortem, were assessed in vivo and fully noninvasively. Beyond vessel patterns and dominances, the potential relation between the vascularization and the motor cortex thickness was studied. Twenty‐one healthy participants underwent 7 T MRI scans to map arterial supply and motor cortex at 0.45 mm isotropic resolution. The motor cortex vasculature was segmented manually with vessel‐specific labels. VDM was utilized to estimate the vessel‐specific supply regions and, subsequently, assess vessel patterns and dominances. Statistical tests were applied to test if the vasculature impacts mean motor cortical thickness estimates. Vessel patterns, that is the presence of supplying vessels, were classified as three‐, four‐, and five‐vessel patterns with a prevalence of 26.3%, 50.0%, and 23.7%, respectively. Vessel dominance, that is the ratio of supply volumes, of the ACA branches showed dominance of the pericallosal artery, callosomarginal artery, and equal contribution, in 34.2%, 34.2%, and 31.6% of the cases, respectively. For the MCA groups, the prevalence of precentral group dominance, central group dominances, and equal contribution was 13.2%, 34.2%, and 52.6%, respectively. Although the central and precentral groups were found in all hemispheres, the postcentral group was found in 28.9% of hemispheres with highly variable supply contribution. Statistical tests returned no significance for the effect of vessel patterns and dominances on the mean motor cortex thickness. With 7 T MRI and VDM, the motor cortex vascularization can be assessed fully noninvasively and longitudinally while providing overall good concordance with previous post mortem studies. Our comprehensive analysis of arterial motor cortex vascularization showed considerable variability between hemispheres, rendering the usage of pattern‐specific atlases and analysis more suitable than single normative representations. The successful translation from post mortem to in vivo enables the study of vascular reserve in disorders affecting the motor cortex, such as ALS, and can be translated to other brain regions and neurodegenerative diseases in the future. To elucidate structural patterns in human motor cortex vascularization, vessel distance mapping (VDM) is utilized to identify vessel territories from 7 T MRI. Subsequent transformation into MNI space facilitates the creation of vessel pattern‐specific atlases, enabling noninvasive mapping of each artery's supply based on the underlying vascularization.

Seventy years following the discovery of peroxisomes, their complete proteome, the peroxi‐ome, remains undefined. Uncovering the peroxi‐ome is crucial for understanding peroxisomal activities and cellular metabolism. We used high‐content microscopy to uncover peroxisomal proteins in the model eukaryote – Saccharomyces cerevisiae . This strategy enabled us to expand the known peroxi‐ome by ~40% and paved the way for performing systematic, whole‐organellar proteome assays. By characterizing the sub‐organellar localization and protein targeting dependencies into the organelle, we unveiled non‐canonical targeting routes. Metabolomic analysis of the peroxi‐ome revealed the role of several newly identified resident enzymes. Importantly, we found a regulatory role of peroxisomes during gluconeogenesis, which is fundamental for understanding cellular metabolism. With the current recognition that peroxisomes play a crucial part in organismal physiology, our approach lays the foundation for deep characterization of peroxisome function in health and disease. Synopsis High‐content imaging analysis uncovers the peroxisomal proteome of Saccharomyces cerevisiae . Systematic, whole‐organellar proteome assays reveal new players in peroxisome organization, targeting, and function. Targeting dependency assays unveiled non‐canonical targeting routes of Pex5‐dependent cargo proteins. Metabolomic analysis of each peroxisomal mutant revealed the role of several newly‐identified resident enzymes. Targeted inspection of two newly identified peroxisomal proteins uncovered a regulatory role of peroxisomes during gluconeogenesis. Graphical Abstract High‐content imaging analysis uncovers the peroxisomal proteome of Saccharomyces cerevisiae . Systematic, whole‐organellar proteome assays reveal new players in peroxisome organization, targeting, and function.

X‐rays can penetrate deeply into biological cells and thus allow for examination of their internal structures with high spatial resolution. In this study, X‐ray phase‐contrast imaging and tomography is combined with an X‐ray‐compatible optical stretcher and microfluidic sample delivery. Using this setup, individual cells can be kept in suspension while they are examined with the X‐ray beam at a synchrotron. From the recorded holograms, 2D phase shift images that are proportional to the projected local electron density of the investigated cell can be calculated. From the tomographic reconstruction of multiple such projections the 3D electron density can be obtained. The cells can thus be studied in a hydrated or even living state, thus avoiding artifacts from freezing, drying or embedding, and can in principle also be subjected to different sample environments or mechanical strains. This combination of techniques is applied to living as well as fixed and stained NIH3T3 mouse fibroblasts and the effect of the beam energy on the phase shifts is investigated. Furthermore, a 3D algebraic reconstruction scheme and a dedicated mathematical description is used to follow the motion of the trapped cells in the optical stretcher for multiple rotations. Biological cells in suspension are manipulated using an optical stretcher and imaged using X‐ray phase‐contrast tomography.

Photoacoustic imaging is an advanced biomedical imaging technique that combines optical excitation and ultrasound detection to provide molecular functional information of biological tissues in vivo. From the underlying principles, the spatial resolution and imaging depth of the photoacoustic imaging system can be adjusted. In recent years, photoacoustic imaging has gained increasing attention for its potential in high‐resolution microscopy, particularly in the context of cellular visualization. A wide range of techniques, including laser scanning, wavelength tuning, transducer design, and data acquisition strategy, have been explored to enhance resolution in photoacoustic imaging. These advances have enabled detailed imaging of cell nuclei, organelles, chromophores, and molecular distributions. This review highlights recent advancements in high‐resolution photoacoustic imaging techniques aimed at visualizing cellular structures. We explore key system configurations, imaging methodologies, and representative biomedical applications, including label‐free histopathological imaging, biochemical mapping, deep‐tissue microscopy, and super‐resolution imaging techniques. By summarizing these achievements, we aim to highlight the current state and future potential of photoacoustic imaging as a critical tool in cellular and molecular biomedical research. Photoacoustic imaging has gained increasing attention for its potential in high‐resolution microscopy, particularly in the context of cellular visualization. This review highlights recent advancements in high‐resolution photoacoustic imaging techniques aimed at visualizing cellular structures, particularly explore key system configurations, imaging methodologies, and representative biomedical applications.

A fixed-receiver mobile-transmitter passive bistatic synthetic aperture radar (MF-PB-SAR) system, which uses the Sentinel-1 SAR satellite as its non-cooperative emitting source, has been developed by using embedded software-defined radio (SDR) hardware for high-resolution imaging of the targets in a local area in this study. Firstly, Sentinel-1 and the designed system are introduced. Then, signal model, signal pre-processing methods, and effective target imaging methods are presented. At last, various experiment results of target imaging obtained at different locations are shown to validate the developed system and the proposed methods. It was found that targets in a range of several kilometers can be well imaged.

We present an integrated algorithm for high‐resolution crustal‐scale imaging utilizing long‐offset wide‐angle ocean‐bottom seismometer (OBS) data and short‐offset multichannel streamer (MCS) data. The algorithm adopts a two‐step imaging strategy, initially using the OBS data to enhance deep structure imaging and capture long‐wavelength features of the migration velocity. Subsequently, the MCS data are migrated to recover detailed short‐wavelength components and shallow structures using the migration velocity model modified by the OBS result. Both steps employ the least‐squares reverse time migration (LSRTM) method with appropriate regularization techniques. The algorithm is implemented using the Bregmanized operator splitting (BOS) approach, known for its efficiency and adaptability in handling non‐smooth regularization, such as total‐variation (TV) constraints. To improve computational efficiency, compressed sensing is employed to store incident wavefields at half their actual size and reconstruct them accurately when required. Convergence is expedited through the use of preconditioners and the Anderson acceleration method. The proposed algorithm is validated through large‐scale numerical examples, demonstrating its robustness even with an initially imprecise velocity model. Results showcase improved resolution and accuracy achieved through the integration of OBS and MCS data. This study offers a comprehensive framework for crustal‐scale imaging, addresses computational complexities, and provides enhanced imaging capabilities. Plain Language Summary We present a method for high‐resolution crustal‐scale imaging using a combination of long‐offset wide‐angle data from stationary receivers deployed on the seafloor with short‐offset data from floating receivers. Our method is structured as a two‐step imaging algorithm that exploits the unique strengths of each data set. In the first step, the stationary recorded data is used to recover the long‐wavelength features. Subsequently, we integrate the floating receiver data with the preceding image to recover the short‐wavelength components, adding details. The efficacy of our method relies on the utilization of least‐squares reverse time migration, which is enhanced by incorporating appropriate regularization techniques. This enables us to address critical challenges such as memory overhead, convergence speed, and image stability. These key challenges are addressed by incorporating three advanced strategies: compressed sensing theory, Anderson acceleration, and total variation regularization. Accordingly, we enhance computational efficiency, expedite convergence, and elevate imaging quality, which helps provide accurate, high‐resolution images of the subsurface that facilitate better geological interpretation of deep structures and therefore improve our understanding of the target's regional geodynamic context. Moreover, this algorithm allows imaging with less initial knowledge about the image and more robustness to the frequency range. Key Points Enhanced crustal‐scale seismic imaging from academic data Ocean bottom seismometer and multi‐channel streamer data integration High‐resolution least‐squares reverse time migration

In this work, 3D polymeric atomic force microscopy (AFM) tips, referred to as 3DTIPs, are manufactured with great flexibility in design and function using two‐photon polymerization. With the technology holding a great potential in developing next‐generation AFM tips, 3DTIPs prove effective in obtaining high‐resolution and high‐speed AFM images in air and liquid environments, using common AFM modes. In particular, it is shown that the 3DTIPs provide high‐resolution imaging due to their extremely low Hamaker constant, high speed scanning rates due to their low quality factor, and high durability due to their soft nature and minimal isotropic tip wear; the three important features for advancing AFM studies. It is also shown that refining the tip end of the 3DTIPs by focused ion beam etching and by carbon nanotube inclusion substantially extends their functionality in high‐resolution AFM imaging, reaching angstrom scales. Altogether, the multifunctional capabilities of 3DTIPs can bring next‐generation AFM tips to routine and advanced AFM applications, and expand the fields of high speed AFM imaging and biological force measurements. Multipurpose polymeric atomic force microscopy (AFM) tips, named 3DTIPs, are presented with low Hamaker constant for high resolution, high speed AFM imaging. The 3DTIPs are generated with great flexibility in design, material, and function allowing better control over resonance frequency, quality factor, and spring constant. Their tip ends are further post‐processed to extend their functionality in high resolution AFM imaging.

Higher spatial and temporal resolutions are two important performance parameters in an imaging system. However, due to hardware limitations, the two resolutions are usually mutually restricted. To meet this challenge, we propose a spatiotemporal compressive imaging (STCI) system to reconstruct high-spatiotemporal-resolution images from low-resolution measurements. For STCI, we also designed a novel reconstruction network for multiple compression ratio (CR). To verify the effectiveness of our method, we implemented simulation and optical experiments, respectively. The experiment results show that our method can effectively reconstruct high-spatiotemporal-resolution target scenes for nine different CRs. With the maximum spatiotemporal CR of 128:1, our method can achieve a reconstruction accuracy of 28.28 dB.

Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet-50 and VGG-16. Three techniques were used to assess the models’ performance: model training evaluation, evaluation using visual interpretation, and ground sampling inspections. The study identified three characteristics needed for detection and health classification: crown size, color, and density. The optimal altitude to capture images for detection and classification was determined to be 100 m, although the model showed satisfactory performance up to 140 m. For oil palm tree detection, healthy tree identification, and unhealthy tree identification, Resnet-50 obtained F1-scores of 95.09%, 92.07%, and 86.96%, respectively, with respect to visual interpretation ground truth and 97.67%, 95.30%, and 57.14%, respectively, with respect to ground sampling inspection ground truth. Resnet-50 yielded better F1-scores than VGG-16 in both evaluations. Therefore, the proposed method is well suited for the effective management of crops.