High-resolution Image Analysis Research Articles

A modular framework for multi-scale tissue imaging and neuronal segmentation.

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.

Nanofabrication, characterisation and modelling of soft-in-hard FeCo–FePt magnetic nanocomposites

Advanced nanofabrication exploiting e-beam lithography has been used to prepare nanocomposites consisting of periodic arrays of soft magnetic FeCo-based nano-inclusions of variable dimensions, embedded in a hard magnetic FePt matrix. Nanocomposites with non-magnetic (Pt) inclusions and single phase hard magnetic FePt microstructures were prepared as reference samples. The formation of Kirkendall voids in the soft-in-hard nanocomposites, because of annealing-induced diffusion, has been identified through high resolution imaging and chemical analysis. Replication of the μm-scaled nanocomposite and hard magnetic units over mm-scaled surfaces allowed global magnetic characterisation of magnetisation reversal using standard magnetometry. Magnetic force microscopy imaging was used for spatially resolved magnetic characterisation in different remanent magnetic states. Both of these experimental techniques were used to study the influence of the size, volume content and nature of the nano-inclusions on magnetisation reversal. The well-defined geometry and nano-scaled size of the inclusions render these nanocomposites as model systems for micromagnetic simulations and very good agreement was achieved between measured and simulated magnetisation reversal curves. This accordance motivates future in-silico optimisation of such soft-in-hard nanocomposites, which may serve as model systems to guide the design of new high performance bulk magnets which are less dependent on critical elements.

Cancer-associated fibroblast activation predicts progression, metastasis, and prognosis of cutaneous squamous cell carcinoma.

Cutaneous squamous cell carcinoma (cSCC) is the most common metastatic skin cancer and the metastatic disease is associated with poor prognosis. Cancer-associated fibroblasts (CAFs) promote progression of cancer, but their role in cSCC is largely unknown. We examined the potential of CAF markers in the assessment of metastasis risk and prognosis of primary cSCC. We utilized multiplexed fluorescence immunohistochemistry for profiling CAF landscape in metastatic and non-metastatic primary human cSCCs, in metastases, and in premalignant epidermal lesions. Quantitative high-resolution image analysis was performed with two separate panels of antibodies for CAF markers and results were correlated with clinical and histopathological parameters including disease-specific mortality. Increased stromal expression of fibroblast activation protein (FAP), α-smooth muscle actin, and secreted protein acidic and rich in cysteine (SPARC) were associated with progression to invasive cSCC. Elevation of FAP and platelet-derived growth factor receptor-β (PDGFRβ) expression was associated with metastasis risk of primary cSCCs. High expression of PDGFRβ and periostin correlated with poor prognosis. Multimarker combination defined CAF subset, PDGFRα-/PDGFRβ+/FAP+, was associated with invasion and metastasis, and independently predicted poor disease-specific survival. These results identify high PDGFRβ expression alone and multimarker combination PDGFRα-/PDGFRβ+/FAP+ by CAFs as potential biomarkers for risk of metastasis and poor prognosis.

Acto3D: an open-source user-friendly volume rendering software for high-resolution 3D fluorescence imaging in biology.

Advances in fluorescence microscopy and tissue-clearing have revolutionised 3D imaging of fluorescently labelled tissues, organs and embryos. However, the complexity and high cost of existing software and computing solutions limit their widespread adoption, especially by researchers with limited resources. Here, we present Acto3D, an open-source software, designed to streamline the generation and analysis of high-resolution 3D images of targets labelled with multiple fluorescent probes. Acto3D provides an intuitive interface for easy 3D data import and visualisation. Although Acto3D offers straightforward 3D viewing, it performs all computations explicitly, giving users detailed control over the displayed images. Leveraging an integrated graphics processing unit, Acto3D deploys all pixel data to system memory, reducing visualisation latency. This approach facilitates accurate image reconstruction and efficient data processing in 3D, eliminating the need for expensive high-performance computers and dedicated graphics processing units. We have also introduced a method for efficiently extracting lumen structures in 3D. We have validated Acto3D by imaging mouse embryonic structures and by performing 3D reconstruction of pharyngeal arch arteries while preserving fluorescence information. Acto3D is a cost-effective and efficient platform for biological research.

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images.

Improving cellular analysis throughput of lens-free holographic microscopy with circular Hough transform and convolutional neural networks

In recent years, lens-free Digital In-Line Holographic Microscopy (DIHM) has become an essential tool for in-vitro experiments. Specifically, it offers many innovative approaches to the characterization of cancer cell assays, which are pioneering studies in the prevention and treatment of cancer, the most common disease of our time. With DIHM, images with very high resolution, high magnification coefficient, and wide FOV can be obtained. However, the success of the DIHM is directly dependent on the successful coordination of the imaging and analysis processes. This study proposes a combined approach of circular Hough transform (CHT) and Convolutional Neural Network (CNN) algorithms to improve the cellular analysis aspect of DIHM. The design, installation, and testing of the DIHM for cellular imaging are presented. Resolution analyses of the microscopy system are presented using a standard USAF 1951 resolution slide. MCF-7 cell lines were imaged with the designed microscope at various concentrations. CHT was used in the wide search space to detect the particles in the cell slides. The proposed method detected all overlapping cells and non-cell particles in the images. CNN was used to classify particles as living, dead, and noncell cells. A GPU-based GUI has been designed to perform imaging and analysis operations. The performance of the proposed method is statistically compared with a reference system using various performance metrics. The accuracy of the model is calculated as %99.92 and also the results obtained with the proposed method show more than R2=0.99 correlation with a reference system. The results of the proposed method have improved the analytical performance of DIHM in in-vitro cell cancer cell line experiments. In addition, the method contributes to the analysis of high-resolution images.

Abstract 4169: A deep learning approach to guide acquisition region selection for imaging mass cytometry

Abstract The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment. This can be aided by high-plex imaging and analysis to obtain phenotypes of specific cells and study their biodistribution and interactions. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and high-plex imaging of tissues, avoiding the complications of autofluorescence and cyclic imaging. IMC has expanded its capabilities with three distinct imaging modes: Preview, Cell, and Tissue. The Preview Mode is a rapid scanning system that captures a comprehensive overview of the stained tissue, mapping out the distribution of over 40 markers and revealing tissue heterogeneity. This enables researchers to make informed decisions about which areas warrant closer examination on the same. Building on this, Cell Mode offers high-resolution imaging for detailed analysis of the Regions of Interest (ROI) identified during Preview, all using the same slide. Tissue Mode complements these by providing a fast acquisition of the entire tissue at a lower resolution, which is optimal for quantitative pixel-based analysis of tissue biology. These modes support automated, continuous imaging of more than 40 large tissue samples (400 mm2) weekly. Following Preview Mode, the selection of ROIs for high-resolution imaging is a critical step, enhanced by automated AI algorithms to ensure it is informed by biomarker expression. Tissue sections of colon adenocarcinoma were stained with a 30-marker IMC panel of structural, tumor, stroma, immune cell, and immune activation markers. Images were acquired on Hyperion XTi™ (Standard BioTools), first in Preview Mode, then in Cell Mode with automatic selection of ROIs using Phenoplex™ (Visiopharm). ROIs were automatically selected based on two criteria: 1) actively proliferating and non-proliferating tumor regions; 2) cold and hot tumor regions as identified by hotspots of lymphoid and myeloid immune markers within stromal or epithelial tumor regions. Single cell analysis of the high-resolution images obtained in Cell Mode, was performed as a multi-step workflow using Phenoplex. Tissue segmentation was used to divide the tissue into epithelial tumor (E-Cadherin/panCK), stromal, and tumor margin regions; cell segmentation was based on Iridium DNA channels; and phenotyping was performed using the guided highplex workflow. These data were used to compare the immune contexture through a series of t-SNE plots partitioned by spatial region and clinical variables. This work demonstrates that the multi-modal features of Hyperion XTi can greatly accelerate the ability of IMC users to gain useful insights from complex biological samples. Phenoplex enables a comprehensive workflow for the analysis of this complex data, providing automated ROI selection, and phenotyping and spatial analyses of high-resolution IMC images for biological assessment. Citation Format: Fabian Schneider, Smriti Kala, Sam Lim, Clinton Hupple, Nina Lane, James Robert Mansfield. A deep learning approach to guide acquisition region selection for imaging mass cytometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4169.

Abstract 6224: Drop-like deformation of nuclei in cancer patient tissue

Abstract In this study, we investigated nuclear deformations in cancer tissues and their clinical relevance to diagnostics. Introduction: Cancers are graded by pathologists examining biopsy slides stained with hematoxylin and eosin for factors including nuclear atypia, which can include features like lobules, invaginations, large nuclear size, and prominent nucleoli. While the importance of nuclear atypia has been known for centuries, its cause remains unclear. Nuclear shaping has been studied extensively in vitro, leading to the liquid drop model of nuclear shaping, which states that nuclear shape changes can occur due to excess area in the form of wrinkles or folds in the lamina. When the lamina becomes fully taut, the nucleus reaches a limiting steady shape. Wrinkles allow the nuclear shape to mirror the cell shape such as in invading cancer cells. The applicability of this model to nuclei in cancer tissues from human patients is untested. Here, tissues were stained for nuclear lamina proteins to test for the presence of excess area in the lamina. Diagnostic relevance of the drop model was assessed by quantitative Fourier analysis of high-resolution images of lamin-stained nuclei. Methods: Deidentified patient tissues from breast, colon, head and neck, and skin were immunostained for lamin B1 and imaged at high resolution (60X, 1.5 NA). Nuclei were then roughly segmented by the deep learning program Cellpose, followed by segmentation with a custom approach that sensitively detects micron-scale variations in the laminar contour. An Elliptical Fourier Analysis was performed to quantify nuclear irregularity by describing nuclear shape with a series of elliptical harmonics. More irregular nuclei are described by larger Fourier coefficients at higher frequencies. Results: Folds and wrinkles in the nuclear lamina were observed in healthy tissue from all four tissue types studied, indicating the presence of excess area in the lamina. The presence of irregular nuclear shape including wrinkling/folds in the lamina was quantified with the Elliptical Fourier Coefficient (EFC) ratio. Posterior probability analysis related EFC ratio to tumor grade to assess the clinical value of the excess area found in the nuclear lamina. This revealed cancer-type dependent trends between EFC ratio and tumor grade. Conclusions: Observations of wrinkling/folds in the nuclear lamina in healthy and cancerous tissue are consistent with the liquid drop model of nuclear deformation. Quantification of nuclear lamina wrinkling with the EFC ratio, along with other nuclear shape parameters, can be a powerful approach for computer-based, explainable, clinical grading. Our findings reveal a fundamental principle of nuclear deformation in cancer tissues and suggest a new approach for improving the accuracy in cancer grading. Citation Format: Christina R. Dubell, Ting-Ching Wang, Ishita Singh, Daniel G. Rosen, Saptarshi Chakraborty, Tanmay P. Lele. Drop-like deformation of nuclei in cancer patient tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6224.

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images.

Esophageal cancer (EC) remains a significant health challenge globally, with increasing incidence and high mortality rates. Despite advances in treatment, there remains a need for improved diagnostic methods and understanding of disease progression. This study addresses the significant challenges in the automatic classification of EC, particularly in distinguishing its primary subtypes: adenocarcinoma and squamous cell carcinoma, using histopathology images. Traditional histopathological diagnosis, while being the gold standard, is subject to subjectivity and human error and imposes a substantial burden on pathologists. This study proposes a binary class classification system for detecting EC subtypes in response to these challenges. The system leverages deep learning techniques and tissue-level labels for enhanced accuracy. We utilized 59 high-resolution histopathological images from The Cancer Genome Atlas (TCGA) Esophageal Carcinoma dataset (TCGA-ESCA). These images were preprocessed, segmented into patches, and analyzed using a pre-trained ResNet101 model for feature extraction. For classification, we employed five machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression (LR), Decision Tree (DT), AdaBoost (AD), Random Forest (RF), and a Feed-Forward Neural Network (FFNN). The classifiers were evaluated based on their prediction accuracy on the test dataset, yielding results of 0.88 (SVC and LR), 0.64 (DT and AD), 0.82 (RF), and 0.94 (FFNN). Notably, the FFNN classifier achieved the highest Area Under the Curve (AUC) score of 0.92, indicating its superior performance, followed closely by SVC and LR, with a score of 0.87. This suggested approach holds promising potential as a decision-support tool for pathologists, particularly in regions with limited resources and expertise. The timely and precise detection of EC subtypes through this system can substantially enhance the likelihood of successful treatment, ultimately leading to reduced mortality rates in patients with this aggressive cancer.

Deep Learning Promotes Profiling of Multiple miRNAs in Single Extracellular Vesicles for Cancer Diagnosis.

Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profiling single-EV multi-miRNA signatures by combining total internal reflection fluorescence (TIRF) imaging with a deep learning (DL) algorithm for the first time. This innovative technique allows for the precise characterization of EV miRNAs at the single-vesicle level, overcoming the challenges posed by EV heterogeneity. TIRF with high resolution and a signal-to-noise ratio can simultaneously detect multi-miRNAs in situ in individual EVs. DL algorithm avoids complicated and inaccurate artificial feature extraction, achieving automated high-resolution image analysis. Using this approach, we reveal that the main variation of EVs from 5 cancer cells and normal plasma is the triple-positive EV subpopulation, and the classification accuracy of single triple-positive EVs from 6 sources can reach above 95%. In the clinical cohort, 20 patients (5 lung cancer, 5 breast cancer, 5 cervical cancer, and 5 colon cancer) and 5 healthy controls are predicted with an overall accuracy of 100%. This single-EV strategy provides new opportunities for exploring more specific EV biomarkers to achieve cancer diagnosis and classification.

Convolutional neural network-based techniques and error level analysis for image tamper detection

<span>Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.</span>

Effect of Crown Ether Additives on the Enhanced Performance of Metallic Bipolar Plates for Proton Exchange Membrane Fuel Cells.

One of the key components of the fuel cell stack is a metallic bipolar plate (MBP) that plays multiple roles, such as current collector, fuel and oxidant distributor, and mechanical support. However, corrosion and consequent metal elution are major drawbacks of the MBP because they diminish the efficiency and power performance of membrane-electrode assemblies (MEAs). Herein, we show that the crown ether (CE) additive can simultaneously inhibit surface corrosion of the MBP and act as a scavenger for eluted metal ions to alleviate contamination of other components. From the electrochemical measurement, high-resolution imaging, and elemental analysis, we have found that the CE undergoes electrolytic decomposition and makes an efficient protective layer in an in situ manner. This layer prevents direct contact between the MBP and electrolyte as well as the dissolution of metal ions into the electrolyte. In addition, we demonstrate that the CE can improve the recovery protocol of the MEA owing to the formation of host-guest complexes between the CE and metal cations. These results provide key insights into the design of high-performance MBPs for proton-exchange membrane fuel cells.

High-Resolution Large-Area Image Analysis Deciphers the Distribution of Salmonella Cells and ECM Components in Biofilms Formed on Charged PEDOT:PSS Surfaces.

Biofilms, comprised of cells embedded in extracellular matrix (ECM), enable bacterial surface colonization and contribute to pathogenesis and biofouling. Yet, antibacterial surfaces are mainly evaluated for their effect on bacterial cells rather than the ECM. Here, a method is presented to separately quantify amounts and distribution of cells and ECM in Salmonella biofilms grown on electroactive poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS). Within a custom-designed biofilm reactor, biofilm forms on PEDOT:PSS surfaces electrically addressed with a bias potential and simultaneous recording of the resulting current. The amount and distribution of cells and ECM in biofilms are analyzed using a fluorescence-based spectroscopic mapping technique and fluorescence confocal microscopy combined with advanced image processing. The study shows that surface charge leads to upregulated ECM production, leaving the cell counts largely unaffected. An altered texture is also observed, with biofilms forming small foci or more continuous structures. Supported by mutants lacking ECM production, ECM is identified as an important target when developing antibacterial strategies. Also, a central role for biofilm distribution is highlighted that likely influences antimicrobial susceptibility in biofilms. This work provides yet a link between conductive polymer materials and bacterial metabolism and reveals for the first time a specific effect of electrochemical addressing on bacterial ECM formation.

Computational Approaches and Observer Variation in the 3D Musculoskeletal Modeling of the Heads of Anolis.

High-resolution imaging, 3D modeling, and quantitative analyses are equipping evolutionary biologists with new approaches to understanding the variation and evolution of the musculoskeletal system. However, challenges with interpreting DiceCT data and higher order use of modeled muscles have not yet been fully explored, and the error in and accuracy of some digital methods remain unclear. West Indian Anolis lizards are a model clade for exploring patterns in functional adaptation, ecomorphology, and sexual size dimorphism in vertebrates. These lizards possess numerous jaw muscles with potentially different anatomies that sculpt the adductor chamber of the skull. Here we test approaches to quantifying the musculoskeletal shape of the heads of two species of Anolis: A. pulchellus and A. sagrei. We employ comparative approaches such as DiceCT segmentation of jaw muscles, 3D surface attachment mapping, and 3D landmarking with the aim of exploring muscle volumes, 3D muscle fiber architecture, and sexual dimorphism of the skull. We then compare sources of measurement error in these 3D analyses while also presenting new 3D musculoskeletal data from the Anolis feeding apparatus. These findings demonstrate the accessibility and repeatability of these emerging techniques as well as provide details regarding the musculoskeletal anatomy of the heads of A. pulchellus and A. sagrei which show potential for further research of comparative biomechanics and evolution in the clade.

Preparation, characterization and mechanical properties analysis of SAC305-SnBi-Co hybrid solder joints for package-on-package technology

The development of hybrid solder for chip packing is a major 3D packaging research topic. The incorporation of with low melting points into solder materials has been used in low-temperature packaging. Hybrid solder struggles to enhance low-temperature solder efficiency and mechanical properties. This work examined the effects of Co elements on microstructure and intermetallic compound (IMC) morphology of SAC305-SnBi-Co hybrid solder generated by reflow welding on electroless nickel immersion gold (ENIG) substrate at various temperatures. Moreover, mechanical characteristics and failure behavior of joints were extensively explored. The results show that the addition of 0.05% Co reduced the melting range of the solder and improved the wettability of SnBi solder. According to transmission electron microscopes (TEM) and electron diffraction spots (EDS), the crystal structure of the grains is (Cu, Co)6Sn5 basic-centered monoclinic crystal. Fourier analysis of high-resolution images yields deformed lattice fringes, indicating that Co atoms fill Cu6Sn5's lattice gap in IMC. The shear strength of SAC305/SnBi hybrid solder joints with Co rises and then decreases with temperature. The highest shear strength of solder joints with 0.05% Co (40 nm) is 73.2 MPa at 220 °C, whereas joints with 0.1%Co are 67.5 MPa. Combined with the fracture morphology, the proportion of IMC fracture mode decreased after adding Co element.

Application of Texture and Volume Model Analysis to Dedicated Axillary High-resolution 3D T2-weighted MR Imaging: A Novel Method for Diagnosing Lymph Node Metastasis in Patients with Clinically Node-negative Breast Cancer.

To evaluate the effectiveness of the texture analysis of axillary high-resolution 3D T2-weighted imaging (T2WI) in distinguishing positive and negative lymph node (LN) metastasis in patients with clinically node-negative breast cancer. Between December 2017 and May 2021, 242 consecutive patients underwent high-resolution 3D T2WI and were classified into the training (n = 160) and validation cohorts (n = 82). We performed manual 3D segmentation of all visible LNs in axillary level I to extract the texture features. As the additional parameters, the number of the LNs and the total volume of all LNs for each case were calculated. The least absolute shrinkage and selection operator algorithm and Random Forest were used to construct the models. We constructed the texture model using the features from the LN with the largest least axis length in the training cohort. Furthermore, we constructed the 3 models combining the selected texture features of the LN with the largest least axis length, the number of LNs, and the total volume of all LNs: texture-number model, texture-volume model, and texture-number-volume model. As a conventional method, we manually measured the largest cortical diameter. Moreover, we performed the receiver operating curve analysis in the validation cohort and compared area under the curves (AUCs) of the models. The AUCs of the texture model, texture-number model, texture-volume model, texture-number-volume model, and conventional method in the validation cohort were 0.7677, 0.7403, 0.8129, 0.7448, and 0.6851, respectively. The AUC of the texture-volume model was higher than those of other models and conventional method. The sensitivity, specificity, positive predictive value, and negative predictive value of the texture-volume model were 90%, 69%, 49%, and 96%, respectively. The texture-volume model of high-resolution 3D T2WI effectively distinguished positive and negative LN metastasis for patients with clinically node-negative breast cancer.

Lateral prefrontal cortex thickness is associated with stress but not cognitive fatigue in exhaustion disorder.

Impaired executive functioning, including cognitive fatigue, is a core feature of the long-term stress-related condition exhaustion disorder (ED). Recent research suggests that a key area for executive control, the lateral prefrontal cortex (LPFC), may be mechanistically linked to cognitive fatigue due to stress. Here, we therefore asked if and how stress, the LPFC and cognitive fatigue may be related in ED. We used a multimodal cross-sectional study design with high-resolution structural magnetic resonance imaging (MRI), self-reported measures, and path analysis modeling in 300 participants with ED. We found positive associations between stress and cognitive fatigue, and stress and LPFC thickness,but no association between LPFC thickness and cognitive fatigue. Furthermore, LPFC thickness did not mediate or moderate the association between stress and cognitive fatigue. These findings suggest that LPFC brain morphology is related to perceived stress levels but not cognitive fatigue, expanding previous research on the role of the LPFC in executive functioning. Moreover, the results support the notion that the LPFC may be mechanistically involved in stress-related executive function impairment but prompt further research into if and how this may be related to cognitive symptoms in ED.

Expansion microscopy for visualization of protein clusters in cultured cells and brain tissues

Expansion microscopy for visualization of protein clusters in cultured cells and brain tissues

Clast shape-fabric analysis: A comprehensive and efficient methodology to measure particle-orientation data in solid and loose volcaniclastic deposits

Fabric analysis is essential for understanding the evolution of volcaniclastic deposits. Here we present a comprehensive and efficient methodology, called “Clast shape-fabric analysis,” which is part of the Quantitative Textural Analysis (QTA). This methodology combines high-resolution image analysis techniques with geospatial data processing tools.The fabric of a deposit refers to the three-dimensional orientation of the particles with respect to space, where the degree of iso-orientation of the major axes of the particles is taken into account. The process begins with the collection of oriented samples in the field. Then, in the laboratory, the samples are processed to obtain high-resolution images. The final stage involves the analysis of these images using the FabricS program, which combines image processing techniques and circular statistics.An application of the method was made at the Joya Honda Maar in Mexico, where shape-fabric analysis was used to identify the emission centers of pyroclastic materials.In summary, the “Clast shape-fabric analysis” is a reliable, low-cost and high-potential methodology that can be applied in several geoscientific disciplines and other areas of scientific research.•New Methodology for shape-fabric analysis is presented.•The methodology involves field work, laboratory work and image analysis.•Identification of particle orientations in volcaniclastic deposits.

Multi-Temporal and Multiscale Satellite Remote Sensing Imagery Analysis for Detecting Pasture Area Changes after Grazing Cessation Due to the Fukushima Daiichi Nuclear Disaster

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8’s classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making.