Image Analysis Research Articles

Correlation of dynamic contrast-enhanced ultrasonography and the Ki-67 labelling index in pancreatic ductal adenocarcinoma

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant and aggressive tumor, and high Ki-67 expression indicates poor histological differentiation and prognosis. Therefore, one of the challenges in diagnosing preoperatively patients with PDAC is predicting the degree of malignancy. Dynamic contrast-enhanced ultrasonography (DCE-US) plays a crucial role in abdominal tumor diagnosis, and can adequately show the microvascular composition within the tumors. However, the relationship between DCE-US and the Ki-67 labelling index remains unclear at the present time. AIM To predict the correlation between Ki-67 expression and the parameters of DCE-US. METHODS Patients with PDAC who underwent DCE-US were retrospectively analyzed. Patients who had received any treatment (radiotherapy or chemotherapy) prior to DCE-US; had incomplete clinical, imaging, or pathologic information; and had poor-quality image analysis were excluded. Correlations between Ki-67 expression and the parameters of DCE-US in patients with PDAC were assessed using Spearman’s rank correlation analysis. The diagnostic performances of these parameters in high Ki-67 expression group were evaluated according to receiver operating characteristic curve. RESULTS Based on the Ki-67 labelling index, 30 patients were divided into two groups, i.e. , the high expression group and the low expression group. Among the relative quantitative parameters between the two groups, relative half-decrease time (rHDT), relative peak enhancement, relative wash-in perfusion index and relative wash-in rate were significantly different between two groups (P = 0.018, P = 0.025, P = 0.028, P = 0.035, respectively). The DCE-US parameter rHDT was moderately correlated with Ki-67 expression, and rHDT ≥ 1.07 was more helpful in accurately diagnosing high Ki-67 expression, exhibiting a sensitivity and specificity of 53.8% and 94.1%, respectively. CONCLUSION One parameter of DCE-US, rHDT, correlates with high Ki-67 expression. It demonstrates that parameters obtained noninvasively by DCE-US could better predict Ki-67 expression in PDAC preoperatively.

Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study

Purpose: Elastography increased the diagnostic accuracy of liver fibrosis. However, several challenges persist, including the widespread utilization of equipment, difficulties in measuring certain cases, and the influence of viscosity factors. A rough surface and a blunted hepatic margin have long been acknowledged as valuable characteristics indicative of hepatic fibrosis. The objective of this study was to conduct an image analysis and quantitative assessment of the contour of the sagittal section of the left lobe of the liver. Methods: Between February and October 2020, 486 consecutive outpatients underwent ultrasound examinations at our hospital. A total of 214 images were manually annotated by delineating the liver contour to create annotation images. U-Net was employed for liver segmentation, with the dataset divided into training (n = 128), testing (n = 42), and validation (n = 44) subsets. Additionally, 43 Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) cases with pathology data from between 2015 and 2020 were included. Segmentation was performed using the program developed in the first step. Subsequently, shape analysis was conducted using ImageJ. Results: Liver segmentation exhibited high accuracy, as indicated by Dice loss of 0.044, Intersection over Union of 0.935, and an F score of 0.966. The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. Image analysis showed MinFeret and Minor correlated with liver fibrosis stage (p = 0.046, 0.036, respectively). Sensitivity, specificity, and AUROC of Minor for ≥F3 were 0.571, 0.862, and 0.722, respectively, and F4 were 1, 0.600, and 0.825, respectively. Conclusion: Deep learning segmentation of the sagittal cross-sectional contour of the left lobe of the liver demonstrated commendable accuracy. The roughness of the liver surface was correctly judged by artificial intelligence. Image analysis showed the thickness of the left lobe inversely correlated with liver fibrosis stage.

Using GeoMx DSP Spatial Proteomics to Investigate Immune Infiltration of NOD Mouse Islet and Exocrine Compartments

Abstract Purpose Type 1 Diabetes (T1D) pathogenesis involves immune cells infiltrating pancreatic Islets of Langerhans, leading to T cell activation, beta cell destruction, and impaired insulin production. However, infiltration has a heterogenic nature that isn’t described in detail, as not all islets are infiltrated. The aim of this study was to investigate if the observed heterogeneity is coupled to differences in immune and/or dysfunctional status of islets or exocrine cells, and if specific markers could elucidate mechanistic details of T1D pathogenesis. Procedures The GeoMx platform was used to spatially quantify protein levels in pancreatic islets and exocrine tissue in Non-Obese Diabetic (NOD) mice. The protein panel included 17 immune activity markers and nine dysfunction markers. Immunohistochemical (IHC) staining and digital image analysis was used to analyze select marker proteins. Results Use of the GeoMx platform to investigate T1D was shown to be possible, as Granzyme B protein levels were found to be lower in distal islet areas when compared to proximal areas. Smooth Muscle Actin protein levels were higher in exocrine areas proximal to immune-infiltrated islets, when compared to distally located exocrine areas. Findings from GeoMx were however not observed in IHC-stained sections. Conclusions This study demonstrates that investigating T1D is possible with spatial proteomics, as the assays revealed presence of heterogenic islet areas in NOD mice, which may play a role in T1D progression and escape from immune recognition. This study highlights the potential of spatial technologies for elucidating T1D pathogenesis and future treatment strategies.

SEM and TEM Image Analysis for Morphology and Phase Transition of CZTS, Sb2Se3 and Perovskite Thin Films under Thermal Stress

SEM and TEM Image Analysis for Morphology and Phase Transition of CZTS, Sb2Se3 and Perovskite Thin Films under Thermal Stress

Optimizing Basil Seed Vigor Evaluations: An Automatic Approach Using Computer Vision-Based Technique

The short cultivation cycle and high essential oil content of basil plants render them a valuable raw material source for diverse industries. However, large-scale production is hindered by the lack of specific protocols to assess seed vigor; thus, a consistent supply of high-quality seeds that meet consumer demands cannot be ensured. This study investigated the effectiveness of an automated system for seedling analysis as a tool for evaluating basil seed vigor and compared it to traditional tests. For this purpose, seeds from eight commercial lots were evaluated in two separate trials spaced six months apart using the following tests: germination, first germination count, saturated salt accelerated aging, primary root emergence, mean germination time, seedling emergence, seedling emergence speed index, and computerized seedling image analysis. The parameters provided by the system allowed us to clearly and objectively classify the basil seed lots based on vigor, and the results were strongly and significantly correlated with the findings of traditional vigor tests, particularly between the vigor index and seedling length. Digital analysis of four-day-old seedlings proved to be a fast and efficient technique for evaluating basil seed vigor and has the potential for use in automating the data collection and analysis process.

A foundation model unlocks unified biomedical image analysis

A foundation model unlocks unified biomedical image analysis

Assessment of podocyte detachment as a pivotal step in the development of focal segmental glomerulosclerosis

Abstract Background Podocytopenia refers to a decrease in the number of podocytes. When podocytes are injured, they may detach leading to podocytopenia, which represents a critical step in the development of podocytopathy and subsequently deterioration of renal functions. Pathological assessment of podocytopenia plays a crucial role in diagnosing underlying kidney diseases. Aim To assess detached podocytes and evaluate their diagnostic role in the development of focal segmental glomerulosclerosis. Materials and methods This is a retrospective study, conducted on 67 archival renal biopsies with the clinical diagnosis of steroid-resistant or steroid-dependent nephrotic syndrome (SRNS) and diagnosed as focal segmental glomerulosclerosis (FSGS) and podocytopathy with detached podocytes by electron microscopy (EM). Colloidal iron stain and Desmin immunohistochemical stain were performed. Assessment of the mean percent of stained pixels in relation to the surface tuft area of the glomerulus, i.e., mean percent of stained area (PSA) was done using image analysis system (ImageJ 1.52a) software. Results Podocytopathy with detached podocytes was diagnosed in 35 (52.24%) cases, while FSGS was diagnosed in 32 (47.76%) cases. Regarding detached podocytes, 27 (49.3%) cases showed no detached podocytes by light microscopy (LM), while only 4 (6%) showed severe podocyte detachment. There was a statistically significant difference between control cases and both podocytopathy with detached podocytes and FSGS regarding mean PSA (p ≤ 0.001). Conclusion Standardized reporting of detached podocyte cells is becoming mandatory as they have a high positive predictive value for the expected EM picture.

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

Abstract Senescence is a highly ordered biological process involving resource redistribution away from aging tissues that affects yield and quality in annuals and perennials. Images from 14 unmanned / unoccupied / uncrewed aerial system (UAS, UAV, drone) flights captured the senescence window across two experiments while functional principal component analysis (FPCA) effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: the red chromatic coordinate index (RCC) and transformed normalized difference green and red index (TNDGR). Convolutional neural networks (CNNs) trained on temporally concatenated, or “sandwiched,” UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis (SPA). The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1-M6). Model performance was strongest for FPC1 scores from VSRs (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to-moderate for RCC (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA). This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.

Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin

AbstractAssessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.

QuPath Edu and OpenMicroanatomy: Open‐source virtual microscopy tools for medical education

AbstractVirtual microscopy is becoming increasingly common in both medical education and routine clinical practice. Virtual microscopy software is typically designed either for (1) training students in anatomy, histology, and histopathology, or (2) quantitative analysis—but not both simultaneously. QuPath is one of the most widely used software applications for histopathology image analysis in research and provides a comprehensive set of computational tools to evaluate histology slides. We have enhanced QuPath by developing a new extension, QuPath Edu, which adapts the software to function as an intuitive microanatomy learning environment. Additionally, we have created an entirely new, complementary software platform called OpenMicroanatomy, which provides an alternative way to access QuPath Edu teaching content through a web interface. These tools have been used in teaching of first year medical and dentistry students at the University of Oulu Medical Faculty, and we conducted a user survey for the Class of 2023 to assess the usability and student experience. In general, the introduced annotation and quiz features were appreciated by the students and the system usability of OpenMicroanatomy was considered excellent (SUS score 84.8). Together, these freely available tools enable teachers to develop and deploy innovative training material for anatomy, histopathology, quantitative analysis, and artificial intelligence in a wide range of contexts. This unique combination can provide the next generation of students with essential multidisciplinary skills.

Abstract B029: Mapping the vascular walls of colorectal cancer

Abstract Introduction: Properties of tumor angiogenesis impact on tumor growth and composition of the tumor microenvironment. Tumor vessels are composed of pericytes and other mural cells. Detailed characterization of the cellular composition of the microvasculature can suggest functional vessel and case properties relevant for outcome, response to treatment and immune surveillance. Results: Based on single cell RNA-seq from three human stage II/III colon cancers and pilot in-situ multi-antibody staining of six tumors two main clusters of perivascular cells were identified; B1 (MCAM+, MYH11-) and B2 (MCAM+, MYH11+) cells. B1 cells displayed pericyte features including expression of RGS5, whereas B2 cells showed high expression of smooth muscle cell markers like ACTG2 and Desmin. Desmin-high B2 cells were predominantly located in muscular layers, whereas Desmin-low/MYH11-high B2 cells showed perivascular enrichment. An extended novel analyses workflow of digital image analysis was used to profile peri-endothelial composition in 19 intervals; 11 1-µm intervals from 0 to 10 µm and 8 5-µm intervals from 10 to 50 µm from endothelial areas. Each of these expansion areas was quantitated regarding density of PDGFRB+/- cells including B1/B2 cells, and a MYH11+/MCAM- cells. Density of macrophage subsets (CD68-positive subsets defined by CD163 and CD11c) were also quantitated. In general, B1 cells showed stronger spatial association with endothelial cells than B2 cells, and this spatial association was stronger for the PDGFRB+ B1 subset. Survival analysis indicated that prognostic relevance was strongest when PDGFRB+ (favorable prognosis) cells were closest to endothelial cells. Regarding perivascular macrophage density, M0 and transit M1/M2 macrophages were predominantly enriched in the peri-endothelial regions, whereas density of M1 and M2 increased gradually as distance from vessels increased. M2 density was overall associated with poor prognosis, whereas high density of other macrophage subsets overall was associated with good prognosis. Ongoing studies are using a clustering-based approach to identify vessel subsets, and prognosis associations are being consolidated in additional cohorts. Summary: This study provides a novel approach for high resolution profiling of perivascular status in CRC, and possibly other tumor types, that could be of specific importance to identify tumor features associated with response to angiogenesis-directed therapies. Citation Format: Linglong( 凌 龙 ) Huang( 黄 ), Mercedes Herrera, Jonas Sjölund, Vladimir Chocoloff, Simon Joost, Rasul M. Tabiev, Lina Wik Leiss, Carina Strell, Luis Nunes, Artur Mezheyeuski, David Edler, Anna Martling, Fredrik Pontén, Bengt Glimelius, Tobias Sjöblom, Maria Kasper, Kristian Pietras, Arne Östman. Mapping the vascular walls of colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B029.

Real-time analysis of liquid jet sample delivery stability for an X-ray free-electron laser using machine vision

Automated evaluation of optical microscopy images of liquid jets, commonly used for sample delivery at X-ray free-electron lasers (XFELs), enables real-time tracking of the jet position and liquid jet hit rates, defined here as the proportion of XFEL pulses intersecting with the liquid jet. This method utilizes machine vision for preprocessing, feature extraction, segmentation and jet detection as well as tracking to extract key physical characteristics (such as the jet angle) from optical microscopy images captured during experiments. To determine the effectiveness of these tools in monitoring jet stability and enhancing sample delivery efficiency, we conducted XFEL experiments with various sample compositions (pure water, buffer and buffer with crystals), nozzle designs and jetting conditions. We integrated our real-time analysis algorithm into the Karabo control system at the European XFEL. The results indicate that the algorithm performs well in monitoring the jet angle and provides a quantitative characterization of liquid jet stability through optical image analysis conducted during experiments.

The early dodder gets the host: decoding the coiling patterns of Cuscuta campestris with automated image processing.

We developed an in-house Python-based image analysis pipeline to investigate the movement patterns of Cuscuta. Our analysis unveiled that the coiling and circumnutation movements of Cuscuta are regulated by its intrinsic circadian rhythm. Cuscuta spp., commonly known as dodders, are rootless and leafless stem parasitic plants. Upon germination, Cuscuta starts rotating immediately in a counterclockwise direction (circumnutation) to locate a host plant, creating a seamless vascular connection to steal water and nutrients from its host. In this study, our aim was to elucidate the dynamics of the coiling patterns of Cuscuta, which is an essential step for successful parasitism. Using time-lapse photography, we recorded the circumnutation and coiling movements of C. campestris at different inoculation times on non-living hosts. Subsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between. The study revealed that the coiling efficacy of C. campestris varied with the inoculation time of day, showing higher success and faster initiation in morning than in evening. These observations suggest that Cuscuta, despite lacking leaves and a developed chloroplast, can discern photoperiod changes, significantly determining its parasitic efficiency. The automated image analysis results confirmed the reliability of our Python pipeline by aligning closely with manual annotations. This study provides significant insights into the parasitic strategies of C. campestris and demonstrates the potential of integrating computational image analysis in plant biology for exploring complex plant behaviors. Furthermore, this method provides an efficient tool for investigating plant movement dynamics, laying the foundation for future studies on mitigating the economic impacts of parasitic plants.

Advanced single-cell and spatial analysis with high-multiplex characterization of circulating tumor cells and tumor tissue in prostate cancer: Unveiling resistance mechanisms with the CoDuCo in situ assay.

Metastatic prostate cancer is a highly heterogeneous and dynamic disease and practicable tools for patient stratification and resistance monitoring are urgently needed. Liquid biopsy analysis of circulating tumor cells (CTCs) and circulating tumor DNA are promising, however, comprehensive testing is essential due to diverse mechanisms of resistance. Previously, we demonstrated the utility of mRNA-based in situ padlock probe hybridization for characterizing CTCs. We have developed a novel combinatorial dual-color (CoDuCo) assay for in situ mRNA detection, with enhanced multiplexing capacity, enabling the simultaneous analysis of up to 15 distinct markers. This approach was applied to CTCs, corresponding tumor tissue, cancer cell lines, and peripheral blood mononuclear cells for single-cell and spatial gene expression analysis. Using supervised machine learning, we trained a random forest classifier to identify CTCs. Image analysis and visualization of results was performed using open-source Python libraries, CellProfiler, and TissUUmaps. Our study presents data from multiple prostate cancer patients, demonstrating the CoDuCo assay's ability to visualize diverse resistance mechanisms, such as neuroendocrine differentiation markers (SYP, CHGA, NCAM1) and AR-V7 expression. In addition, druggable targets and predictive markers (PSMA, DLL3, SLFN11) were detected in CTCs and formalin-fixed, paraffin-embedded tissue. The machine learning-based CTC classification achieved high performance, with a recall of 0.76 and a specificity of 0.99. The combination of high multiplex capacity and microscopy-based single-cell analysis is a unique and powerful feature of the CoDuCo in situ assay. This synergy enables the simultaneous identification and characterization of CTCs with epithelial, epithelial-mesenchymal, and neuroendocrine phenotypes, the detection of CTC clusters, the visualization of CTC heterogeneity, as well as the spatial investigation of tumor tissue. This assay holds significant potential as a tool for monitoring dynamic molecular changes associated with drug response and resistance in prostate cancer.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.

Salicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate. A trial and validation set of 96 and 24 plants from 2 populations confirmedthe efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding. The CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

Identification of Rusts Formed on Steels by Image Analysis with HSV Color Space

Identification of Rusts Formed on Steels by Image Analysis with HSV Color Space

Multiwavelength photobiomodulation in atrophic age-related macular degeneration.

To evaluate photobiomodulation (PBM) safety and efficacy in patient with atrophic AMD and to explore tissue effects using Spectrally Resolved Autofluorescence (SrAF) to identify a potential biomarker indicative of mitochondrial activity, the primary PBM target. This retrospective, non-comparative case series involved six eyes of five patients with atrophic AMD, conducted at the Medical Retina and Imaging Unit, IRCCS San Raffaele Hospital in Milan, Italy. At baseline and follow-ups (after one and three months) a complete ophthalmological assessment and multimodal imaging, including spectrally resolved autofluorescence (SrAF), were performed. PBM treatment (Valeda Light Delivery System - LumiThera) was administered in nine sessions over three weeks. The SrAF images were analyzed to evaluate the effect of the treatment. Six eyes of five patients with atrophic AMD received PBM at baseline. Best corrected visual acuity (BCVA), mean central macular thickness (CMT), mean retinal sensitivity on microperimetry and the atrophic area have remained stable. No eyes developed foveal atrophy after the treatment. The image analysis in SrAF showed an increase in the highlighted area of 4.89% after one month and 17.11% after three months. No adverse systemic or local side effects were reported. PBM is a safe treatment for the human retina. The evaluation of the retinal area within the green light spectrum (575 nm) in SrAF is a potential indirect biomarker of mitochondrial activity and could be used to assess treatment efficacy in clinical studies.

C1-inhibitor to prevent intracerebral hemorrhage-related secondary brain injury.

Preclinical studies indicate that the systemic application of C1-inhibitor, clinically used to treat hereditary angioedema, reduces secondary brain injury after ischemic stroke. This study assessed the effect of C1-inhibitor on secondary brain injury after hemorrhagic stroke. We used an established striatal whole-blood injection mouse model to mimic intracerebral hemorrhage-related secondary brain injury. Based on the spatiotemporal dynamics in our model, we calculated the necessary sample size (n = 24) and determined the most sensitive time point to detect potential group differences (48h) prior to the experiments. The experimental setup, tissue processing and image analysis adhered to our published protocol. We randomized mice into three groups: C1-inhibitor treatment, placebo, and sham. Histology was standardized by taking eight anatomically predefined slices across the entire lesion. Lesion size, vascular leakage, and inflammatory responses were assessed using automated thresholding and dextran/ICAM1/CD45 intensity mapping. Investigators were blinded to group allocation during the experiment, tissue processing, and image analysis. Whole blood injection resulted in significantly larger lesion size and more pronounced vascular leakage and cellular inflammation compared to the sham group. However, there was no difference in lesion size or inflammatory markers between the C1-inhibitor and placebo groups. In addition, there was no difference in the inflammatory response of the choroid plexus, which has been identified as a central organ orchestrating inflammation after intracerebral hemorrhage. The protective effect of C1-inhibitor might be isolated to pathophysiological processes with a predominant thromboinflammatory component, as in ischemia-reperfusion, but less so in permanent ischemia or intracerebral hemorrhage.

Accurately Identifying Sound vs. Rotten Cranberries Using Convolutional Neural Network

Cranberries, native to North America, are known for their nutritional value and human health benefits. One hurdle to commercial production is losses due to fruit rot. Cranberry fruit rot results from a complex of more than ten filamentous fungi, challenging breeding for resistance. Nonetheless, our collaborative breeding program has fruit rot resistance as a significant target. This program currently relies heavily on manual sorting of sound vs. rotten cranberries. This process is labor-intensive and time-consuming, prompting the need for an automated classification (sound vs. rotten) system. Although many studies have focused on classifying different fruits and vegetables, no such approach has been developed for cranberries yet, partly because datasets are lacking for conducting the necessary image analyses. This research addresses this gap by introducing a novel image dataset comprising sound and rotten cranberries to facilitate computational analysis. In addition, we developed CARP (Cranberry Assessment for Rot Prediction), a convolutional neural network (CNN)-based model to distinguish sound cranberries from rotten ones. With an accuracy of 97.4%, a sensitivity of 97.2%, and a specificity of 97.2% on the training dataset and 94.8%, 95.4%, and 92.7% on the independent dataset, respectively, our proposed CNN model shows its effectiveness in accurately differentiating between sound and rotten cranberries.

An introduction for non-experts on using X-ray micro computed tomography as a tool for pore scale digital subsurface characterisation of siliciclastic materials

This paper presents an overview of using X-ray micro computed tomography (μCT) as a valuable tool for micro scale investigation of siliciclastic materials. When processed using digital image analysis (DIA), valuable quantitative data can be extracted from μCT 3D images. Subsurface reservoirs are of great importance to society as fluid-bearing formations, but also as storage reservoirs for carbon dioxide. μCT imaging has the capability to perform preliminary, highly detailed investigations of potential reservoirs. This approach has a range of benefits when compared to traditional 2D techniques, such as optical and scanning electron microscopy (SEM). Key advantages include the technique being non-destructive and capable of 3D and 4D visualisation. This facilitates rapid repeated digital measurements and experiments on microstructures. Digital samples can also be readily shared within the scientific community to replicate results and quickly launch new investigations. However, limitations still exist, posing challenges to the wider application of such a methodology. Such limitations include the identification of a representative elementary volume (REV), computational cost, and suitable processing of the output image data. Here, we highlight the value of using μCT and DIA, from our first experiences, to facilitate pore scale siliciclastic reservoir characterisation, but also highlight our perceived limitations and barriers to its much wider application. This paper introduces the key processing stages, opportunities and limitations of these techniques.