High-resolution Imaging Research Articles

Dysfunction of the magnocellular subdivision of the visual thalamus in developmental dyslexia.

Developmental dyslexia (DD) is one of the most common learning disorders, affecting millions of children and adults worldwide. To date, scientific research has attempted to explain DD primarily based on pathophysiological alterations in the cerebral cortex. In contrast, several decades ago, pioneering research on five post-mortem human brains suggested that a core characteristic of DD might be morphological alterations in a specific subdivision of the visual thalamus - the magnocellular LGN (M-LGN). However, due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans, this finding was never confirmed in-vivo, and its relevance for DD pathology remained highly controversial. Here, we leveraged recent advances in high-resolution magnetic resonance imaging (MRI) at high field strength (7 Tesla) to investigate the M-LGN in DD in-vivo. Using a case-control design, we acquired data from a large sample of young adults with DD (n = 26; age 28 ± 7 years; 13 females) and matched control participants (n = 28; age 27 ± 6 years; 15 females). Each participant completed a comprehensive diagnostic behavioral test battery and participated in two MRI sessions, including three functional MRI experiments and one structural MRI acquisition. We measured blood-oxygen-level-dependent responses and longitudinal relaxation rates to compare both groups on LGN subdivision function and myelination. Based on previous research, we hypothesized that the M-LGN is altered in DD and that these alterations are associated with a key DD diagnostic score, i.e., rapid letter and number naming (RANln). The results showed aberrant responses of the M-LGN in DD compared to controls, which was reflected in a different functional lateralization of this subdivision between groups. These alterations were associated with RANln performance, specifically in male DD. We also found lateralization differences in the longitudinal relaxation rates of the M-LGN in DD relative to controls. Conversely, the other main subdivision of the LGN, the parvocellular LGN (P-LGN), showed comparable blood-oxygen-level-dependent responses and longitudinal relaxation rates between groups. The present study is the first to unequivocally show that M-LGN alterations are a hallmark of DD, affecting both the function and microstructure of this subdivision. It further provides a first functional interpretation of M-LGN alterations and a basis for a better understanding of sex-specific differences in DD with implications for prospective diagnostic and treatment strategies.

DNA-PAINT Probe Modifications Support High-Resolution Imaging with Shorter Binding Domains.

DNA-based Points Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) is an effective super resolution microscopy technique, and its optimization is key to improve nanoscale detection. The state-of-the-art improvements that are at the base of this optimization have been first routinely validated on DNA nanostructure devices before being tested on biological samples. This allows researchers to finely tune DNA-PAINT imaging features in a more controllable in vitro environment. Dye-labeled oligonucleotide probes with short hybridization domains can expand DNA-PAINT's detection by targeting short nucleotide sequences and improving resolution, speed, and multiplexing. However, developing these probes is challenging as their brief bound state makes them difficult to capture under routine imaging conditions. To extend dwell binding times and promote duplex stability, we introduced structural and chemical modifications to our imager probes. The modifications included mini-hairpins and/or Bridged Nucleic Acids (BNA); both of which increase the thermomechanical stability of a DNA duplex. Using this approach we demonstrate DNA-PAINT imaging with approximately 5 nm resolution using a 4-nucleotide hybridization domain that is 43% shorter than previously reported probes. Imager probes with such short hybridization domains are key for improving detection on DNA nanostructure devices because they have the capability to target a larger number of binding domains per localization unit. This is essential for metrology applications such as Nucleic Acid Memory (NAM) where the information density is dependent on the binding site length. The selected imager probes reported here present imaging resolution equivalent to current state-of-the-art DNA-PAINT probes, creating a strategy to image shorter DNA domains for nanoscience and nanotechnology alike.

Compact broadband high-resolution real-time four-dimensional imaging spectrometer

A broadband high-resolution real-time four-dimensional imaging spectrometer (HRRFDIS) is presented, which can acquire both broadband fine spectra and high-resolution three-dimensional (3D) spatial images of a 3D object in real time. The HRRFDIS consists of a first microlens array arranged in a plane to achieve orthographic view spatial imaging, a second microlens array arranged on a conical surface to measure the depth and to achieve 360-degree side-view spatial imaging, multiple optical fibers, a collimating microlens array arranged in a straight line, a parallel planar transmission grating pair to obtain high spectral resolution over a broadband spectral range, and an area-array detector. Compared with the scanning four-dimensional imaging spectrometer (FDIS), the HRRFDIS can obtain a broadband high-resolution four-dimensional dataset using only one frame of data, and it is more stable, compact, small-sized, and lightweight. Compared to the staring FDIS using a liquid crystal filter and requiring at least one modulation period of liquid crystal to acquire a complete hyperspectral image, the HRRFDIS can acquire a complete broadband hyperspectral image in real time. Compared to existing snapshot FDIS, the HRRFDIS can achieve much higher spectral resolution, especially over a broadband spectral range. The HRRFDIS is a unique concept that is the first to obtain both high-resolution broadband spectral information and high-resolution 3D spatial information in real time, to the best of our knowledge. The HRRFDIS will be suitable for real-time measurement of 3D objects in the ultraviolet to infrared spectral range.

Structures of Liquid-Liquid Interfaces in Partially Miscible Systems Revealed by Soft X-ray Imaging.

The properties of liquid-liquid interfaces are intricately linked to its structure, with a particular focus on the concentration distribution within the interface. To obtain precise information regarding the concentration distribution, we have developed a high-resolution soft X-ray imaging method for liquid-liquid interfaces. This work focused on representative partially miscible systems, analyzing the interfacial concentration distribution profiles of water-alkanols under both steady-state and dynamic processes, and obtaining the diffusion coefficients of different water concentrations in alkanols. Significant disparities in concentration distributions and the concentration-related diffusion coefficients were observed despite comparable diffusion distances within the same system across different states. Meanwhile, it was found that alkanols exhibit adsorption phenomena at the interface. This newfound knowledge serves as a crucial stepping stone toward a deeper understanding of partially miscible systems. Our study opens a way to explore liquid-liquid interface information with high-resolution.

StrawSnake: A Real-Time Strawberry Instance Segmentation Network Based on the Contour Learning Approach

Automated harvesting systems rely heavily on precise and real-time fruit recognition, which is essential for improving efficiency and reducing labor costs. Strawberries, due to their delicate structure and complex growing environments, present unique challenges for automated recognition systems. Current methods predominantly utilize pixel-level and box-based approaches, which are insufficient for real-time applications due to their inability to accurately pinpoint strawberry locations. To address these limitations, this study proposes StrawSnake, a contour-based detection and segmentation network tailored for strawberries. By designing a strawberry-specific octagonal contour and employing deep snake convolution (DSConv) for boundary feature extraction, StrawSnake significantly enhances recognition accuracy and speed. The Multi-scale Feature Reinforcement Block (MFRB) further strengthens the model by focusing on crucial boundary features and aggregating multi-level contour information, which improves global context comprehension. The newly developed TongStraw_DB database and the public StrawDI_Db1 database, consisting of 1080 and 3100 high-resolution strawberry images with manually segmented ground truth contours, respectively, serves as a robust foundation for training and validation. The results indicate that StrawSnake achieves real-time recognition capabilities with high accuracy, outperforming existing methods in various comparative tests. Ablation studies confirm the effectiveness of the DSConv and MFRB modules in boosting performance. StrawSnake’s integration into automated harvesting systems marks a substantial step forward in the field, promising enhanced precision and efficiency in strawberry recognition tasks. This innovation underscores the method’s potential to transform automated harvesting technologies, making them more reliable and effective for practical applications.

Automatic Identification and Classification of Surface Defects in Small-Sized Optics

Abstract Precision optics are widely used in lighting systems, imaging systems and high precision inspection equipment. The presence of surface defects in optical components can seriously affect the design performance of the equipment, so the optical components must be rigorously inspected to eliminate defective optical components. Currently, engineering products are moving toward smaller sizes, resulting in smaller optical elements. However, realizing small-sized optical elements is a great challenge because it requires micrometer-scale high-resolution image acquisition and automatic detection of defects on their surfaces. A machine learning-based surface defect detection method is proposed for a wide variety of surface defects and small sizes. A dark-field micro-scattering imaging system is used to acquire dark-field images. Then image segmentation and feature analysis are performed on each image to extract the original feature data and compressed with relifF algorithm. Based on the compressed feature data, a classification model is built using a support vector mechine(SVM). The detection method can classify three kinds of defects, namely, dig, scratch, and scuff mark, with an accuracy of up to 98%. The experimental results of this study show that the method can effectively and accurately detect several microdefects occurring in small-size optical components, thus providing valuable insights for the optimization of mass production parameters of optical components.

The Role of Cone Beam Computed Tomography (CBCT) in the Diagnosis and Clinical Management of Medication-Related Osteonecrosis of the Jaw (MRONJ)

Medication-related osteonecrosis of the jaw (MRONJ) is a debilitating condition associated with antiresorptive and antiangiogenic medications that are frequently used in treating osteoporosis and cancers. With the ability to produce high-resolution images with a lower radiation dose, cone beam computed tomography (CBCT) is an emerging technology in maxillofacial imaging that offers several advantages in evaluating MRONJ. This review aims to summarise the radiological features of MRONJ as observed via CBCT and highlight its advantages over two-dimensional plain films in assessing MRONJ. CBCT has the capability to detect early MRONJ lesions, characterise the extent and nature of lesions, distinguish MRONJ from other osseous pathologies, and assist in treatment planning. By leveraging the advantages of CBCT, clinicians can enhance their understanding of MRONJ, improve decision making, and ultimately optimize patient care.

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105–106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Clinical and Radiological Outcomes at ≥10-Year Follow-up After Matrix-induced Autologous Chondrocyte Implantation in the Patellofemoral Joint.

Matrix-induced autologous chondrocyte implantation (MACI) has demonstrated encouraging outcomes in the treatment of knee cartilage defects, although limited research is available on its longer term (≥10 years) sustainability in the patellofemoral joint. To report the clinical and radiological outcomes at ≥10 years in a prospectively recruited cohort of patients undergoing MACI in the patellofemoral joint and compare outcomes in patients undergoing MACI on the patella versus the trochlea. Case series; Level of evidence, 4. The current study prospectively enrolled 95 patients who underwent patellofemoral MACI, of whom 29 (13 patella, 16 trochlea) underwent concomitant tibial tubercle osteotomy. Patients were assessed preoperatively and at 2, 5, and ≥10 years using a range of patient-reported outcome measures (PROMs) including the Knee injury and Osteoarthritis Outcome Score, the 36-item Short Form Health Survey, and the frequency and severity of knee pain as well as patient satisfaction, full active knee flexion and extension, and peak isokinetic knee extensor and flexor torques. High-resolution magnetic resonance imaging (MRI) was performed to assess pertinent graft parameters, as well as determine an overall MRI composite score, per the Magnetic Resonance Observation of Cartilage Repair Tissue scoring system. Results were analyzed according to the graft location (patella or trochlea). Of the 95 patients recruited, 82 patients (41 patella, 41 trochlea) were available for a clinical review at ≥10 years after surgery (mean follow-up, 11.9 years [range, 10-15 years]). For the whole patellofemoral MACI cohort, all PROMs significantly improved over time (P < .05), with no significant changes (P > .05) observed in any MRI-based score from 2 to ≥10 years after surgery. At ≥10 years, 90.2% (n = 74) were satisfied with MACI in relieving their knee pain, and 85.4% (n = 70) were satisfied with the improvement in their ability to participate in sports. No differences (P > .05) were observed in PROMs between those undergoing patellar MACI and those undergoing trochlear MACI, although a significant group effect was observed for limb symmetry indices of knee extensor (P = .009) and flexor (P = .041) strength, which were greater in those undergoing patellar (vs trochlear) MACI. No statistically significant differences (P > .05) were observed between patellar and trochlear grafts on any MRI-based measure. In the cohort assessed at ≥10 years after surgery, 4 patients (2 patella, 2 trochlea) demonstrated graft failure on MRI scans, although a further 3 patients (all trochlea) were omitted from the ≥10-year review for having already progressed to total knee arthroplasty. Good clinical scores, high levels of patient satisfaction, and adequate graft survivorship were observed at ≥10 years after MACI on the patella and trochlea.

Waveguide-based microscope slide for label-free high-resolution imaging

Waveguide-based total internal reflection fluorescence microscopy has been widely adopted due to its excellent signal-to-noise ratio over a large field of view. However, with the increasing demand for label-free imaging, waveguide-based evanescent light scattering microscopy (ESM) has also garnered significant attention. Here, we present a low-cost waveguide-based microscope slide that offers easier integration with conventional optical microscopy. This microscope slide uses an incoherent light source coupled to a lithium tantalate (LT) planar waveguide to generate an evanescent light that illuminates samples located within a few hundred nanometers of the waveguide surface. We perform its application for imaging chromium nanoholes and polystyrene nanospheres, demonstrating its label-free, high-resolution, high-contrast imaging performance. LT waveguide microscope slides provide a simple and effective solution for ESM.

HRDLNet: a semantic segmentation network with high resolution representation for urban street view images

AbstractSemantic segmentation of urban street scenes has attracted much attention in the field of autonomous driving, which not only helps vehicles perceive the environment in real time, but also significantly improves the decision-making ability of autonomous driving systems. However, most of the current methods based on Convolutional Neural Network (CNN) mainly use coding the input image to a low resolution and then try to recover the high resolution, which leads to problems such as loss of spatial information, accumulation of errors, and difficulty in dealing with large-scale changes. To address these problems, in this paper, we propose a new semantic segmentation network (HRDLNet) for urban street scene images with high-resolution representation, which improves the accuracy of segmentation by always maintaining a high-resolution representation of the image. Specifically, we propose a feature extraction module (FHR) with high-resolution representation, which efficiently handles multi-scale targets and high-resolution image information by efficiently fusing high-resolution information and multi-scale features. Secondly, we design a multi-scale feature extraction enhancement (MFE) module, which significantly expands the sensory field of the network, thus enhancing the ability to capture correlations between image details and global contextual information. In addition, we introduce a dual-attention mechanism module (CSD), which dynamically adjusts the network to more accurately capture subtle features and rich semantic information in images. We trained and evaluated HRDLNet on the Cityscapes Dataset and the PASCAL VOC 2012 Augmented Dataset, and verified the model’s excellent performance in the field of urban streetscape image segmentation. The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-the-art methods.

Fluorescent Pdots Facilitate High-Resolution Mapping of the Intact Meningeal Vascular Network and Eye-Brain Connections.

Meningeal vascular network is significant in neurology and neurosurgery. However, high-resolution imaging of intact meningeal vascular network is lacking. In this work, we develop a practical experimental method to ensure that the intact meninges are morphologically unfolded and fixed in an agarose gel. With the help of high-brightness polymer dots (Pdots) as probe, macroscopic and detailed imaging of the vascular network on the intact dorsal meninges can be performed. Meningeal vessels are symmetrically distributed along the superior sagittal sinus, and the distribution of meningeal vessels had a certain degree of hierarchy. The meninges are thicker blood vessels and capillary networks from the outside to the inside. Moreover, the diameter of the capillaries is 3.96 ± 0.89 μm. Interestingly, meningeal primo vessels in the central nervous system of mice is imaged with the diameter of 4.18 ± 1.18 μm, which has not been reported previously. It is worth mentioning that we found that orthotopic xenografts of brain tumors caused the appearance of corneal neovascularization and morphological changes in optic nerve microvessels. In conclusion, our work provides an effective Pdots-based imaging method for follow-up research on meningeal vascular-related diseases, and illustrates that the eye can serve as a window for the prevention and diagnosis of brain diseases.

Recent Advances in Photoacoustic Imaging: Current Status and Future Perspectives

Photoacoustic imaging (PAI) is an emerging hybrid imaging modality that combines high-contrast optical imaging with high-spatial-resolution ultrasound imaging. PAI can provide a high spatial resolution and significant imaging depth by utilizing the distinctive spectroscopic characteristics of tissue, which gives it a wide variety of applications in biomedicine and preclinical research. In addition, it is non-ionizing and non-invasive, and photoacoustic (PA) signals are generated by a short-pulse laser under thermal expansion. In this study, we describe the basic principles of PAI, recent advances in research in human and animal tissues, and future perspectives.

Visualizing DNA/RNA, Proteins, and Small Molecule Metabolites within Live Cells.

Live cell imaging is essential for obtaining spatial and temporal insights into dynamic molecular events within heterogeneous individual cells, in situ intracellular networks, and in vivo organisms. Molecular tracking in live cells is also a critical and general requirement for studying dynamic physiological processes in cell biology, cancer, developmental biology, and neuroscience. Alongside this context, this review provides a comprehensive overview of recent research progress in live-cell imaging of RNAs, DNAs, proteins, and small-molecule metabolites, as well as their applications in molecular diagnosis, immunodiagnosis, and biochemical diagnosis. A series of advanced live-cell imaging techniques have been introduced and summarized, including high-precision live-cell imaging, high-resolution imaging, low-abundance imaging, multidimensional imaging, multipath imaging, rapid imaging, and computationally driven live-cell imaging methods, all of which offer valuable insights for disease prevention, diagnosis, and treatment. This review article also addresses the current challenges, potential solutions, and future development prospects in this field.

Toward Automated Structural Design for Controlled Vibration Characteristics Using Topology Optimization and Computer Vision in Space Missions

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing custom-developed photogrammetry software, high-resolution images are processed to generate detailed 3D models, which are subsequently converted to STL files with precision. Adaptive meshing in COMSOL 5.3 Multiphysics, controlled through a MATLAB 2023 API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL’s advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining the desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency.

Small vessel disease burden and prognosis of recent subcortical ischaemic stroke differ by parent artery atherosclerosis.

Parent artery atherosclerosis is an important aetiology of recent subcortical ischaemic stroke (RSIS). However, comparisons of RSIS with different degrees of parent artery atherosclerosis are lacking. Prospectively collected data from our multicentre cohort (all were tertiary centres) of the Stroke Imaging Package Study between 2015 and 2017 were retrospectively reviewed. The patients with RSIS defined as a single clinically relevant diffusion-weighted imaging positive lesion in the territory of lenticulostriate arteries were categorized into three subgroups: (1) normal middle cerebral artery (MCA) on magnetic resonance angiography and high-resolution magnetic resonance imaging (HR-MRI); (2) low-grade MCA atherosclerosis (normal or <50% stenosis on magnetic resonance angiography and with MCA plaques on HR-MRI); (3) steno-occlusive MCA atherosclerosis (stenosis ≥50% or occlusion). The primary outcome was 90-day functional dependence (modified Rankin Scale score >2). The clinical and imaging findings were compared between subgroups. A total of 239 patients (median age 60.0 [52.0-67.0] years, 72% male) were enrolled, including 140 with normal MCA, 64 with low-grade MCA atherosclerosis and 35 with steno-occlusive MCA atherosclerosis. Patients with steno-occlusive MCA atherosclerosis had the largest infarct volume. Low-grade MCA atherosclerosis was independently associated with cerebral microbleeding, more severe perivascular spaces in basal ganglia and higher total cerebral small vessel disease burden. Low-grade MCA atherosclerosis was an independent determinant of 90-day functional dependence (odds ratio 3.897; 95% confidence interval 1.309-11.604). Our study suggested RSIS with varying severity of parent artery atherosclerosis exhibits distinctive clinical and neuroimaging characteristics, with low-grade MCA atherosclerosis associating with higher cerebral small vessel disease burden and worse prognosis.

A Global Dataset of Potential Chloride Deposits on Mars as Identified by TGO CaSSIS

Chloride deposits are markers for early Mars’ aqueous past, with important implications for our understanding of the martian climate and habitability. The Colour and Stereo Surface Imaging System (CaSSIS) onboard ESA’s Trace Gas Orbiter provides high-resolution color-infrared images, enabling a planet-wide search for (small) potentially chloride-bearing deposits. Here, we use a neural network to map potentially chloride-bearing deposits in CaSSIS images over a significant fraction of the planet. We identify 965 chloride deposit candidates with diameters ranging from <300 to >3000 m, including previously unknown deposits, 136 (~14%) of which are located in the highlands north of the equator, up to ~36°N. Northern chloride candidates tend to be smaller than in the south and are predominantly located in small-scale topographic depressions in low-albedo Noachian and Hesperian highland terranes. Our new dataset augments existing chloride deposit maps, informs current and future imaging campaigns, and enables future modelling work towards a better understanding of the distribution of near-surface water in Mars’ distant past.

Histology Atlas of the Developing Mouse Respiratory System From Prenatal Day 9.0 Through Postnatal Day 30.

Respiratory diseases are one of the leading causes of death and disability around the world. Mice are commonly used as models of human respiratory disease. Phenotypic analysis of mice with spontaneous, congenital, inherited, or treatment-related respiratory tract abnormalities requires investigators to discriminate normal anatomic features of the respiratory system from those that have been altered by disease. Many publications describe individual aspects of normal respiratory tract development, primarily focusing on morphogenesis of the trachea and lung. However, a single reference providing detailed low- and high-magnification, high-resolution images of routine hematoxylin and eosin (H&E)-stained sections depicting all major structures of the entire developing murine respiratory system does not exist. The purpose of this atlas is to correct this deficiency by establishing one concise reference of high-resolution color photomicrographs from whole-slide scans of H&E-stained tissue sections. The atlas has detailed descriptions and well-annotated images of the developing mouse upper and lower respiratory tracts emphasizing embryonic days (E) 9.0 to 18.5 and major early postnatal events. The selected images illustrate the main structures and events at key developmental stages and thus should help investigators both confirm the chronological age of mouse embryos and distinguish normal morphology as well as structural (cellular and organ) abnormalities.

FusionEXNet: an interpretable fused deep learning model for skin cancer detection

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics.

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging.

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2. Advancing in sensitivity thereby prompted us to pursue higher mass spectrometry imaging (MSI) spatial resolution. A dedicated T-shaped ion guide was designed to allow perpendicular incidence of ablation laser in reflection geometry MALDI. Although 8-10 μm pixel was used in MALDI imaging due to the limited precision of the motorized stage, the laser spot diameter could be down to 2.5 μm for potentially higher spatial resolution. In addition, this ion source enabled real-time and high-quality microscope imaging from backward of the sample plate. Beneficially, we were able to monitor the actual laser spot condition in real time as well as obtain high-resolution microscopic sample images that inherently register with MSI images. All of these benefits have been demonstrated by analyzing standard samples and imaging of cells. We believe that the enhancement in sensitivity, spatial resolution, and microscope capacity of our design could facilitate spatial omics studies.