Structural diversity of tick-borne encephalitis virus particles in the inactivated vaccine based on strain Sofjin

The main approach to preventing tick-borne encephalitis (TBE) is vaccination. Formaldehyde-inactivated TBE vaccines have a proven record of safety and efficiency but have never been characterized structurally with atomic resolution. We report a cryoelectron microscopy (cryo-EM) structure of the formaldehyde-inactivated TBE virus (TBEV) of Sofjin-Chumakov strain representing the Far-Eastern subtype. A 3.8 Å resolution reconstruction reveals the structural integrity of the envelope E proteins, specifically the E protein ectodomains. The comparative study shows a high structural similarity to the previously published structures of the TBEV European subtype strains Hypr and Kuutsalo-14. A fraction of inactivated virions exhibits asymmetric features including the deformations of the membrane profile. We propose that the heterogeneity is caused by inactivation and perform a local variability analysis on the small parts of the envelope protein shell to reveal membrane curvature features possibly induced by the inactivation. The results of this study will have implications for the design of novel vaccines against diseases caused by flaviviruses.

Cements and concretes materials characterisation using machine-learning-based reconstruction and 3D quantitative mineralogy via X-ray microscopy

3D imaging via X-ray microscopy (XRM), a form of tomography, is revolutionising materials characterisation. Nondestructive imaging to classify grains, particles, interfaces and pores at various scales is imperative for our understanding of the composition, structure, and failure of building materials. Various workflows now exist to maximise data collection and to push the boundaries of what has been achieved before, either from singular instruments, software or combinations through multimodal correlative microscopy. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, density (atomic number) and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI resolutions for this problem, which address artefact detection during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased throughput, with the goal to enhance segmentation and visualisation during postprocessing leading to better characterisation of samples. Here, we apply three AI and machine-learning-based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of 'thick' cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to spatially characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data, resolve features not previously accessible, and streamline scanning and reconstruction processes for greater throughput.

A critical catalogue of SEM-EDS multispectral maps analysis methods and their application to hydrated cementitious materials

Many methods have been proposed to analyse SEM-EDS hypermaps of hydrated cementitious materials but none can fit all purposes. In this presentation, we review existing methods for phase identification, stoichiometry quantification, and microstructure quantification in cementitious materials and related materials. We first discuss the unique contribution of SEM-EDS with respect to the outstanding scientific challenges towards sustainable construction materials. We then compare the SEM-EDS and image analysis techniques which contribute to answering these challenges. Convergence and divergence in current methods and workflows, and knowledge gaps are highlighted in terms of the specificities of the material (phase assemblage complexity, grain sizes, mixtures, etc.). The discussion is weighted by the required expert knowledge for the sample preparation, microscope operation, and data analysis. We conclude by discussing how microscopy and image analysis integrate into the overall experimental toolkit to investigate and improve cementitious materials.

Atomic force microscopy 3D structural reconstruction of individual particles in the study of amyloid protein assemblies

Recent developments in atomic force microscopy (AFM) image analysis have made three-dimensional (3D) structural reconstruction of individual particles observed on 2D AFM height images a reality. Here, we review the emerging contact point reconstruction AFM (CPR-AFM) methodology and its application in 3D reconstruction of individual helical amyloid filaments in the context of the challenges presented by the structural analysis of highly polymorphous and heterogeneous amyloid protein structures. How individual particle-level structural analysis can contribute to resolving the amyloid polymorph structure-function relationships, the environmental triggers leading to protein misfolding and aggregation into amyloid species, the influences by the conditions or minor fluctuations in the initial monomeric protein structure on the speed of amyloid fibril formation, and the extent of the different types of amyloid species that can be formed, are discussed. Future perspectives in the capabilities of AFM-based 3D structural reconstruction methodology exploiting synergies with other recent AFM technology advances are also discussed to highlight the potential of AFM as an emergent general, accessible and multimodal structural biology tool for the analysis of individual biomolecules.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

Micro-patterned culture of iPSC-derived alveolar and airway cells distinguishes SARS-CoV-2 variants

The emergence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) variants necessitated a rapid evaluation system for their pathogenesis. Lung epithelial cells are their entry points; however, in addition to their limited source, the culture of human alveolar epithelial cells is especially complicated. Induced pluripotent stem cells (iPSCs) are an alternative source of human primary stem cells. Here, we report a model for distinguishing SARS-CoV-2 variants at high resolution, using separately induced iPSC-derived alveolar and airway cells in micro-patterned culture plates. The position-specific signals induced the apical-out alveolar type 2 and multiciliated airway cells at the periphery and center of the colonies, respectively. The infection studies in each lineage enabled profiling of the pathogenesis of SARS-CoV-2 variants: infection efficiency, tropism to alveolar and airway lineages, and their responses. These results indicate that this culture system is suitable for predicting the pathogenesis of emergent SARS-CoV-2 variants.

Measuring cell proliferation in bioprinting research

Tissue-like constructs, intended for application in tissue engineering and regenerative medicine, can be produced by three-dimensional (3D) bioprinting of cells in hydrogels. It is essential that the viability and proliferation of the encapsulated cells can be reliably determined. Methods currently used to evaluate cell proliferation, such as quantification of DNA and measurement of metabolic activity, have been developed for application in 2D cultures and might not be suitable for bioinks. In this study, human fibroblasts were either cast or printed in gelatin methacryloyl (GelMA) or sodium alginate hydrogels and cell proliferation was assessed by AlamarBlue, PicoGreen and visual cell counts. Comparison of data extrapolated from standard curves generated from 2D cultures and 3D hydrogels showed potential inaccuracies. Moreover, there were pronounced discrepancies in cell numbers obtained from these assays; the different bioinks strongly influenced the outcomes. Overall, the results indicate that more than one method should be applied for better assessment of cell proliferation in bioinks.

Smartphone-based digital phenotyping for genome-wide association study of intramuscular fat traits in longissimus dorsi muscle of pigs

Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time-consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image-based IMF traits through embedded deep-learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White × Tongcheng pigs. Genome-wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user-friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Distinct regulation of two flagella by calcium during chemotaxis of male gametes in the brown alga Mutimo cylindricus (Cutleriaceae, Tilopteridales)

Brown algal male gametes show chemotaxis to the sex pheromone that is released from female gametes. The chemotactic behavior of the male gametes is controlled by the changes in the beating of two flagella known as the anterior and posterior flagellum. Our previous study using Mutimo cylindricus showed that the sex pheromone induced an increment in both the deflection angle of the anterior flagellum and sustained unilateral bend of the posterior flagellum, but the mechanisms regulating these two flagellar waveforms were not fully revealed. In this study, we analyzed the changes in swimming path and flagellar waveforms with a high-speed recording system under different calcium conditions. The extracellular Ca2+ concentration at 10-3 M caused an increment in the deflection angle of the anterior flagellum only when ionomycin was absent. No sustained unilateral bend of the posterior flagellum was induced either in the absence or presence of ionomycin in extracellular Ca2+ concentrations below 10-2 M. Real-time Ca2+ imaging revealed that there is a spot near the basal part of anterior flagellum showing higher Ca2+ than in the other parts of the cell. The intensity of the spot slightly decreased when male gametes were treated with the sex pheromone. These results suggest that Ca2+-dependent changes in the anterior and posterior flagellum are regulated by distinct mechanisms and that the increase in the anterior flagellar deflection angle and sustained unilateral bend of the posterior flagellum may not be primarily induced by the Ca2+ concentration.

Automatic Classification of Slit-Lamp Photographs by Imaging Illumination

PURPOSE

The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique.

METHODS

SLPs were collected from patients with corneal ulcer at Kellogg Eye Center, Bascom Palmer Eye Institute, and Aravind Eye Care Systems. Illumination techniques were slit beam, diffuse white light, diffuse blue light with fluorescein, and sclerotic scatter (ScS). Images were manually labeled for illumination and randomly split into training, validation, and testing data sets (70%:15%:15%). Classification algorithms including MobileNetV2, ResNet50, LeNet, AlexNet, multilayer perceptron, and k-nearest neighborhood were trained to distinguish 4 type of illumination techniques. The algorithm performances on the test data set were evaluated with 95% confidence intervals (CIs) for accuracy, F1 score, and area under the receiver operator characteristics curve (AUC-ROC), overall and by class (one-vs-rest).

RESULTS

A total of 12,132 images from 409 patients were analyzed, including 41.8% (n = 5069) slit-beam photographs, 21.2% (2571) diffuse white light, 19.5% (2364) diffuse blue light, and 17.5% (2128) ScS. MobileNetV2 achieved the highest overall F1 score of 97.95% (CI, 97.94%-97.97%), AUC-ROC of 99.83% (99.72%-99.9%), and accuracy of 98.98% (98.97%-98.98%). The F1 scores for slit beam, diffuse white light, diffuse blue light, and ScS were 97.82% (97.80%-97.84%), 96.62% (96.58%-96.66%), 99.88% (99.87%-99.89%), and 97.59% (97.55%-97.62%), respectively. Slit beam and ScS were the 2 most frequently misclassified illumination.

CONCLUSIONS

MobileNetV2 accurately labeled illumination of SLPs using a large data set of corneal images. Effective, automatic classification of SLPs is key to integrating deep learning systems for clinical decision support into practice workflows.

The Soft Nanodots as Fluorescent Probes for Cell Imaging: Analysis of Cell and Spheroid Penetration Behavior of Single Chain Polymer Dots

This study describes the formation, size control, and penetration behavior of polymer nanodots (Pdots) consisting of single or few chain polythiophene-based conjugated polyelectrolytes (CPEs) via nanophase separation between good solvent and poor solvent of CPE. Though the chain singularity may be associated with dilution nanophase separation suggests that molecules of a good solvent create a thermodynamically driven solvation layer surrounding the CPEs and thereby separating the single chains even in their poor solvents. This statement is therefore corroborated with emission intensity/lifetime, particle size, and scattering intensity of polyelectrolyte in good and poor solvents. Regarding the augmented features, Pdots are implemented into cell imaging studies to understand the nuclear penetration and to differentiate the invasive characteristics of breast cancer cells. The python based red, green, blue (RGB) color analysis   depicts that Pdots have more nuclear penetration ability in triple negative breast cancer cells due to the different nuclear morphology in shape and composition and Pdots have penetrated cell membrane as well as extracellular matrix in spheroid models. The current Pdot protocol and its utilization in cancer cell imaging are holding great promise for gene/drug delivery to target cancer cells by explicitly achieving the very first priority of nuclear intake.

Feasibility of comprehensive genomic profiling using endoscopic ultrasound-guided tissue acquisition with a 22-gauge Franseen needle

Aim

Comprehensive genomic profiling (CGP) test for solid tumors is now increasingly utilized in clinical practice, especially in pancreatobiliary cancer, and specimens obtained by endoscopic ultrasound-guided tissue acquisition (EUS-TA) are often submitted for tissue-based CGP test. In this study, we evaluated the feasibility of EUS-TA using a 22-gauge Franseen needle for the CGP test.

Methods

Consecutive patients with solid tumors who underwent EUS-TA using a 22-gauge Franseen needle, and whose tissue samples were pre-checked for suitability for CGP test, were included in this single-center, retrospective analysis. The success rates of appropriate sample collection for CGP evaluated by pathologists (1st quality control) and CGP test (2nd quality control) were evaluated. In addition, The EUS-TA slides were evaluated for the tissue area and tumor area content, using the image software.

Results

A total of 50 cases, with 78% of pancreatic cancer, were included in the analysis. A median of 3 passes of EUS-TA were performed with an adverse event rate of 4%. The success rates for 1st and 2nd quality control for CGP tests were 86% and 76%, respectively. The image analyses suggested EUS-TA specimen did not always fulfill CGP test criteria, with 18% of tissue area ≥16 mm2 and 38% of tumor area content ≥20%, even in cases with successful CGP tests. The suction method yielded a significantly larger amount of DNA but without a significant difference in the multivariate analysis.

Conclusions

The present study demonstrated the feasibility of EUS-TA using a 22-gauge Franseen needle for CGP test.

Atomic resolution imaging using a novel, compact and stiff scanning tunnelling microscope in cryogen-free superconducting magnet

We present the design and performance of a novel scanning tunnelling microscope (STM) operating in a cryogen-free superconducting magnet. Our home-built STM head is compact (51.5 mm long and 20 mm in diameter) and has a single arm that provides complete openness in the scanning area between the tip and sample. The STM head consists of two piezoelectric tubes (PTs), a piezoelectric scanning tube (PST) mounted on a well-polished zirconia shaft, and a large PT housed in a sapphire tube called the motor tube. The main body of the STM head is made of tantalum. In this design, we fixed the sapphire tube to the frame with screws so that the tube's position can be changed quickly. To analyse the stiffness of the STM head unit, we identified the lowest eigenfrequencies with 3 and 4 kHz in the bending modes, 8 kHz in a torsional mode, and 9 kHz in a longitudinal mode by finite element analysis, and also measured the low drift rates in the X-Y plane and in the Z direction. The high performance of the home-built STM was demonstrated by images of the hexagonal graphite lattice at 300 K and in a sweeping magnetic field from 0 T to 9 T. Our results confirm the high stability, vibration resistance, insensitivity to high magnetic fields and the application potential of our newly developed STM for the investigation of low-frequency systems with high static support stiffness in physics, chemistry, material and biological sciences.

Creating and troubleshooting microscopy analysis workflows: Common challenges and common solutions

As microscopy diversifies and becomes ever more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they have generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by keeping analysis in mind, optimizing data quality, understanding tools and tradeoffs, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.

Evaluation of the Quality Changes in Three Commercial Pastourma Samples during Refrigerated Storage Using Physicochemical, Microbiological, and Image Analyses Combined with Chemometrics

Despite the inherent stability of dried and cured products, such as pastourma, appropriate refrigeration remains essential for preserving their optimal characteristics. This study explored quality and safety characteristics in lamb, beef, and buffalo pastourma during 16-day refrigeration storage after package opening. The comprehensive approach employed Attenuated Total Reflection-Fourier-Transform Infrared (ATR-FTIR) spectroscopy, colorimetry, and image analysis, alongside physicochemical and microbiological analyses, to shed light on these alterations. The findings reveal a reduction in textural uniformity and color vibrancy (fading reds and yellows) across all samples during storage, with lamb pastourma exhibiting the most pronounced effects. Notably, image analysis emerged as a powerful tool, enabling the accurate classification of samples based on storage duration. Additionally, significant variations were observed in moisture content, hue angle, firmness, and TBARS levels, highlighting their influence on pastourma quality. The study documented a gradual decrease in lactic acid bacteria and aerobic plate count populations over time. ATR-FTIR spectra's interpretation revealed the presence of lipids, proteins, carbohydrates, and water. Protein secondary structures, demonstrably influenced by the meat type used, exhibited significant changes during storage, potentially impacting the functional and textural properties of pastourma. Overall, the findings contribute to a deeper understanding of pastourma spoilage during storage, paving the way for the development of improved preservation and storage strategies.

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

Microscopic Factors Affecting the Performance of Pervious Concrete

The impacts of various aggregate particle sizes and cement contents on the internal structure of pervious concrete were investigated. Accordingly, test blocks with different aggregate particle sizes and cement contents were dissected and photographed. Subsequently, the captured images were processed using the ImageJ software (1.53i) to analyze the profiles of the test blocks and identify the internal mesoscopic parameters of the pervious concrete. This study discusses the relationship between microscopic parameters and macroscopic factors based on experimental results. It also fits functional equations linking the permeability coefficient with pore parameters, matrix parameters, and compressive strength. The results indicated that, as the aggregate size increased, the internal pore diameter of the pervious concrete increased, whereas the total area and width of the cement matrix decreased. This resulted in a low permeability coefficient and high compressive strength of the test block. Increasing the cement content in pervious concrete reduced the porosity and increased the width and area of the internal matrix. Consequently, the permeability coefficient decreased, and the compressive strength of the test block increased.

Research Note: Prospects for early detection of breast muscle myopathies by automated image analysis

White Striping (WS), Wooden Breast (WB), and Spaghetti Meat (SM) are documented breast muscle myopathies (BMM) affecting broiler chickens' product quality, profitability and welfare. This study evaluated the efficacy of our newly developed deep learning-based automated image analysis tool for early detection of morphometric parameters related to BMM in broiler chickens. Male chicks were utilized, and muscle samples were collected on d 14 of rearing. Histological procedures, including microscopic scoring, blood vessel count, and collagen quantification, were conducted. A previous study demonstrated our automated image analysis as a reliable tool for evaluating myofiber size, conforming with manual histological measurements. A threshold for BMM detection was established by normalizing and consolidating myofiber diameter and area into a unified metric based on automated measurements, also termed as "relative myofiber size value." Results show that severe myopathy broilers consistently exhibited lower relative myofiber size values, effectively detecting myopathy severity. Our study, aimed as proof of concept, underscores the potential of our automated image analysis tool as an early detection method for BMM.

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI)_and the ASL MRI challenge

PURPOSE

Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis.

METHODS

Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches.

RESULTS

Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results.

CONCLUSIONS

Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.

Automated segmentation of cell organelles in volume electron microscopy using deep learning

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed-up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep-learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography. RESEARCH HIGHLIGHTS: Introducing a rapid, multimodal machine-learning workflow for the automatic segmentation of 3D cell organelles. Successfully applied to a variety of volume electron microscopy datasets and cell lines. Outperforming manual segmentation methods in time and accuracy. Enabling high-throughput quantitative cell biology.

Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies

Cassava brown streak disease (CBSD) poses a substantial threat to food security. To address this challenge, we used PlantCV to extract CBSD root necrosis image traits from 320 clones, with an aim of identifying genomic regions through genome-wide association studies (GWAS) and candidate genes. Results revealed strong correlations among certain root necrosis image traits, such as necrotic area fraction and necrotic width fraction, as well as between the convex hull area of root necrosis and the percentage of necrosis. Low correlations were observed between CBSD scores obtained from the 1-5 scoring method and all root necrosis traits. Broad-sense heritability estimates of root necrosis image traits ranged from low to moderate, with the highest estimate of 0.42 observed for the percentage of necrosis, while narrow-sense heritability consistently remained low, ranging from 0.03 to 0.22. Leveraging data from 30,750 SNPs obtained through DArT genotyping, eight SNPs on chromosomes 1, 7, and 11 were identified and associated with both the ellipse eccentricity of root necrosis and the percentage of necrosis through GWAS. Candidate gene analysis in the 172.2kb region on the chromosome 1 revealed 24 potential genes with diverse functions, including ubiquitin-protein ligase, DNA-binding transcription factors, and RNA metabolism protein, among others. Despite our initial expectation that image analysis objectivity would yield better heritability estimates and stronger genomic associations than the 1-5 scoring method, the results were unexpectedly lower. Further research is needed to comprehensively understand the genetic basis of these traits and their relevance to cassava breeding and disease management.

Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Digital Images

The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson's correlation or Manders' co-occurrence intensity-based colocalization paradigms. Though conceptually and computationally simple, these approaches are critically flawed due to their reliance on signal overlap, sensitivity to cursory signal qualities, and inability to differentiate true and incidental colocalization. Point pattern analysis provides a framework for quantitative characterization of spatial relationships between spatial patterns using the distances between observations rather than their overlap, thus overcoming these issues. Here we introduce an image analysis tool called Spatial Pattern Analysis using Closest Events (SPACE) that leverages nearest neighbor-based point pattern analysis to characterize the spatial relationship of fluorescence microscopy signals from image data. The utility of SPACE is demonstrated by assessing the spatial association between mRNA and cell nuclei from confocal images of cardiac myocytes. Additionally, we use synthetic and empirical images to characterize the sensitivity of SPACE to image segmentation parameters and cursory image qualities such as signal abundance and image resolution. Ultimately, SPACE delivers performance superior to traditional colocalization methods and offers a valuable addition to the microscopist's toolbox.

A semi-automated spectral approach to analyzing cyclical growth patterns using fish scales

We introduce a new semi-automated approach to analyzing growth patterns recorded on fish scales. After manually specifying the center of the scale, the algorithm radially unwraps the scale patterns along a series of transects from the center to the edge of the scale. A sliding window Fourier transform is used to produce a spectrogram for each sampled transect of the scale image. The maximum frequency over all sampled transects of the average spectrogram yields a well-discriminated peak frequency trace that can then serve as a growth template for that fish. The spectrogram patterns of individual fish scales can be adjusted to a common period accounting for differences in date of return or size of fish at return without biasing the growth profile of the scale. We apply the method to 147 Atlantic salmon scale images sampled from 3 years and contrast the information derived with this automated approach to what is obtained using classical human operator measurements. The spectrogram analysis quantifies growth patterns using the entire scale image rather than just a single transect and provides the possibility of more robustly analyzing individual scale growth patterns. This semi-automated approach that removes essentially all the human operator interventions provides an opportunity to process large datasets of fish scale images and combined with advanced analyses such as deep learning methods could lead to a greater understanding of salmon marine migration patterns and responses to variations in ecosystem conditions.

Unveiling the Inner Structure of Micrometric Hollow Polymeric Fibers Using Synchrotron X-Ray Nanotomography

In this study, a novel application of synchrotron X-ray nanotomography based on high-resolution full-field transmission X-ray microscopy for characterizing the structure and morphology of micrometric hollow polymeric fibers is presented. By employing postimage analysis using an open-source software such as Tomviz and ImageJ, various key parameters in fiber morphology, including diameter, wall thickness, wall thickness distribution, pore size, porosity, and surface roughness, were assessed. Electrospun polycaprolactone fibers with micrometric diameters and submicrometric features with induced porosity via gas dissolution foaming were used to this aim. The acquired synchrotron X-ray nanotomography data were analyzed using two approaches: 3D tomographic reconstruction and 2D radiographic projection-based analysis. The results of the combination of both approaches demonstrate unique capabilities of this technique, not achievable by other available techniques, allowing for a full characterization of the internal and external morphology and structure of the fibers as well as to obtain valuable qualitative insights into the overall fiber structure.

The rise of data-driven microscopy powered by machine learning

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.

Creating and troubleshooting microscopy analysis workflows: common challenges and common solutions

As microscopy diversifies and becomes ever-more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certainchallenges in turning microscopy images into answers, independent of their scientific question and the images they've generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by understanding tools and tradeoffs, optimizing data quality, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.

Determining individual glomerular proteinuria and periglomerular infiltration in a cleared murine kidney by a 3-dimensional fast marching algorithm

Three-dimensional (3D) imaging has advanced basic research and clinical medicine. However, limited resolution and imperfections of real-world 3D image material often preclude algorithmic image analysis. Here, we present a methodologic framework for such imaging and analysis for functional and spatial relations in experimental nephritis. First, optical tissue-clearing protocols were optimized to preserve fluorescence signals for light sheet fluorescence microscopy and compensated attenuation effects using adjustable 3D correction fields. Next, we adapted the fast marching algorithm to conduct backtracking in 3D environments and developed a tool to determine local concentrations of extractable objects. As a proof-of-concept application, we used this framework to determine in a glomerulonephritis model the individual proteinuria and periglomerular immune cell infiltration for all glomeruli of half a mouse kidney. A correlation between these parameters surprisingly did not support the intuitional assumption that the most inflamed glomeruli are the most proteinuric. Instead, the spatial density of adjacent glomeruli positively correlated with the proteinuria of a given glomerulus. Because proteinuric glomeruli appear clustered, this suggests that the exact location of a kidney biopsy may affect the observed severity of glomerular damage. Thus, our algorithmic pipeline described here allows analysis of various parameters of various organs composed of functional subunits, such as the kidney, and can theoretically be adapted to processing other image modalities.

Diagnostic value of synthetic diffusion-weighted imaging on breast magnetic resonance imaging assessment: comparison with conventional diffusion-weighted imaging

PURPOSE

To compare images generated by synthetic diffusion-weighted imaging (sDWI) with those from conventional DWI in terms of their diagnostic performance in detecting breast lesions when performing breast magnetic resonance imaging (MRI).

METHODS

A total of 128 consecutive patients with 135 enhanced lesions who underwent dynamic MRI between 2018 and 2021 were included. The sDWI and DWI signals were compared by three radiologists with at least 10 years of experience in breast radiology.

RESULTS

Of the 82 malignant lesions, 91.5% were hyperintense on sDWI and 73.2% were hyperintense on DWI. Of the 53 benign lesions, 71.7% were isointense on sDWI and 37.7% were isointense on DWI. sDWI provides accurate signal intensity data with statistical significance compared with DWI (P < 0.05). The diagnostic performance of DWI and sDWI to differentiate malignant breast masses from benign masses was as follows: sensitivity 73.1% [95% confidence interval (CI): 62-82], specificity 37.7% (95% CI: 24-52); sensitivity 91.5% (95% CI: 83-96), specificity 71.7% (95% CI: 57-83), respectively. The diagnostic accuracy of DWI and sDWI was 59.2% and 83.7%, respectively. However, when the DWI images were evaluated with apparent diffusion coefficient mapping and compared with the sDWI images, the sensitivity was 92.68% (95% CI: 84-97) and the specificity was 79.25% (95% CI: 65-89) with no statistically significant difference. The inter-reader agreement was almost perfect (P < 0.001).

CONCLUSION

Synthetic DWI is superior to DWI for lesion visibility with no additional acquisition time and should be taken into consideration when conducting breast MRI scans. The evaluation of sDWI in routine MRI reporting will increase diagnostic accuracy.

A novel workflow for unbiased quantification of autophagosomes in 3D in Arabidopsis thaliana roots

Macroautophagy is often quantified by live imaging of autophagosomes labeled with fluorescently tagged ATG8 protein (FP-ATG8) in Arabidopsis thaliana. The labeled particles are then counted in single focal planes. This approach may lead to inaccurate results as the actual 3D distribution of autophagosomes is not taken into account and appropriate sampling in the Z-direction is not performed. To overcome this issue, we developed a workflow consisting of immunolabeling of autophagosomes with an anti-ATG8 antibody followed by stereological image analysis using the optical disector and the Cavalieri principle. Our protocol specifically recognized autophagosomes in epidermal cells of Arabidopsis root. Since the anti-ATG8 antibody recognizes multiple AtATG8 isoforms, we were able to detect a higher number of immunolabeled autophagosomes than with the FP-AtATG8e marker, that most likely does not recognize all autophagosomes in a cell. The number of autophagosomes per tissue volume correlated with the intensity of autophagy induction. Compared to the quantification of autophagosomes in maximum intensity projections, stereological methods were able to detect the autophagosomes present in a given volume with higher accuracy. Our novel workflow provides a powerful toolkit for unbiased and reproducible quantification of autophagosomes and offers a convenient alternative to the standard of live imaging with FP-ATG8 markers.

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.

Pseudoimmunofluorescent immunohistochemistry image analysis of phosphorylated signaling proteins in human esophageal squamous cell carcinoma tissue

Immunohistochemistry is primarily employed to visualize the localization of specific molecules in tissue samples. However, there is an increasing need for software-assisted quantitative assessment. In the present study, we performed inverted blue channel-based pseudoimmunofluorescence image analysis using original immunohistochemistry images. In human esophageal squamous cell carcinoma tissues, various humoral factors promote the phosphorylation of signaling proteins, including protein kinase B (Akt) and/or extracellular signal-regulated kinase 1/2 (ERK1/2), leading to tumor progression. Our method demonstrated applicability in the analysis of localized signaling proteins in histological sections. Relatively high phosphorylated Akt (p-Akt) intensity was observed in the cancer-stroma adjacent (Adj) and noncancerous regions of the superficial layer (SL). Furthermore, localized phosphorylated ERK1/2 (Thr202/Tyr204) was observed in the Adj of the SL and invasive front, distinct from the pattern of p-Akt (Ser473) and p-Akt (Thr308). In conclusion, pseudoimmunofluorescent immunohistochemistry image analysis is useful for the quantitative assessment and objective interpretation of localized signaling proteins in esophageal squamous cell carcinoma. The method can also be applied to analyze various immunohistochemistry images from diverse tissues.

Research Note: A validation of an image-based method to estimate chicken comb size

Chicken combs carry important information about the individual, especially the size has been related to sexual reproduction, health, and social signaling. Comb size is usually estimated by weighing removed combs or by calculating the product of the comb's longest and highest dimensions (LHA) to approximate comb area based on measures of a ruler or caliper. These methods have several shortcomings including invasiveness or imprecision. As a result, more recent efforts have employed pixel-based approximations of comb area (PBA) from images. However, the validity of PBA to estimate comb area and how the approximation compares to previous approximation methods, such as LHA, is unknown. Therefore, we developed an apparatus for taking standardized images of the head position of the hens and then applied PBA using the software ImageJ. The hens were each photographed 3 times by 2 different handlers. We first tested the accuracy of the pixel-based area approximation on 3 geometric shapes of known area. Second, we tested the precision of PBA of 15 hens (Dekalb White), evaluated as within-image and within-individual hen precision. Furthermore, we compared the PBA with the LHA based on measures of a caliper. The PBA was both accurate and precise, whereas the LHA overestimated comb area with increasing overestimation for larger combs. Due to the greater accuracy of the PBA, as well as future possibilities of automation and inclusion of further measures, we suggest PBA as a more reliable approach to estimate comb area than LHA. Additionally, our results demonstrate that the outcomes of LHA should be evaluated on an ordinal scale level only.

Disordered spatial pattern of redness signal on facial skin and visual perception of health, stress, and hidden aging

BACKGROUND

Well-being is commonly communicated across industries; however, experimental understanding how human perceive skin health and skin stresses are not sufficient.

MATERIALS AND METHODS

Image analysis algorithm, a* gradient, was developed to evaluate spatial pattern and shape of red signal on skin. Human perception for skin health and stresses were compared with technical measurements in two visual perception studies.

RESULTS

a* gradient correlated with perceived Inflamed Skin (R = 0.73, p < 0.01), Stressed Skin (R = 0.79, p < 0.01), Sensitive Skin (R = 0.75, p < 0.01), Healthy Skin (R = -0.83, p < 0.01), and Start Aging (R = 0.75, p < 0.01).

CONCLUSIONS

Disordered spatial pattern of redness signal drives human perception of skin health, stress, and aging. This new skin index of redness signal shows higher correlation with those human perception than basal a* mean, unevenness of a*, and other conventional skin color attributes.

Analysing biological colour patterns from digital images: An introduction to the current toolbox

Understanding the numerous roles that colouration serves in the natural world has remained a central focus in many evolutionary and ecological studies. However, to accurately characterise and then compare colours or patterns among individuals or species has been historically challenging. In recent years, there have been a myriad of new resources developed that allow researchers to characterise biological colours and patterns, specifically from digital imagery. However, each resource has its own strengths and weaknesses, answers a specific question and requires a detailed understanding of how it functions to be used properly. These nuances can make navigating this emerging field rather difficult. Herein, we evaluate several new techniques for analysing biological colouration, with a specific focus on digital images. First, we introduce fundamental background knowledge about light and perception to be considered when designing and implementing a study of colouration. We then show how numerous modifications can be made to images to ensure consistent formatting prior to analysis. After, we describe many of the new image analysis approaches and their respective functions, highlighting the type of research questions that they can address. We demonstrate how these various techniques can be brought together to examine novel research questions and test specific hypotheses. Finally, we outline potential future directions in colour pattern studies. Our goal is to provide a starting point and pathway for researchers wanting to study biological colour patterns from digital imagery.

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image's gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

Texture Analysis as a Discriminating Tool: Unmasking Rodlet Cell Degranulation in Response to a Contaminant of Emerging Concern

BACKGROUND

Contaminants of emerging concern (CECs) have garnered significant attention due to their potential impacts on ecology, wildlife, and human health. The interest in these contaminants arises from their inadequate regulation or lack of routine monitoring in natural environments. Among them, per- and polyfluoroalkyl substances (PFAS) are of particular concern due to their notable propensity to accumulate within the kidney, significantly influencing the excretion of these pollutants. Rodlet cells (RCs) have emerged as promising indicators of immunotoxicity in response to chemical stressors. A prior comprehensive study extensively detailed the effects of sub-chronic exposure to perfluorooctanoic acid (PFOA), a well-known PFAS compound, on RCs located in the hematopoietic tissue of the common carp kidney. Even at concentrations commonly found in the environment, PFOA exhibited a significant impact on the distribution patterns of RCs, concurrently enhancing exocytosis activity.

METHODS

The assessment of PFOA-induced RC degranulation employed texture analysis combined with linear discriminant analysis (LDA) to differentiate between various experimental exposure groups. The investigation encompassed three fish groups: an unexposed group, a group exposed to an environmentally relevant PFOA concentration (200 ng L-1), and a group exposed to a higher PFOA concentration (2 mg L-1). Texture analysis was conducted on high-resolution color (RGB) images obtained from light microscopy of ultrathin sections from five fish per experimental group, stained with toluidine blue.

RESULTS

This analysis facilitated the quantification of potential cytoplasmic alterations associated with degranulation, encompassing all three RGB channels. The data subjected to LDA enabled the identification of the most distinctive texture characteristics, providing a reliable, objective, and reproducible method to differentiate between experimental groups. Remarkably, 98.0% of both the original and cross-validated cases were correctly classified. However, only one unexposed case was misclassified as a fish exposed to a 200 ng L-1 PFOA concentration, constituting the single false positive in the analysis.

CONCLUSIONS

Utilizing texture analysis and LDA to quantify RC degranulation offers a dependable approach for assessing immunotoxicity within experimental models of toxicological and environmental pathology. This underscores the scientific significance of employing a morphological approach in such investigations.

Genotype-specific spinal cord damage in spinocerebellar ataxias: an ENIGMA-Ataxia study

BACKGROUND

Spinal cord damage is a feature of many spinocerebellar ataxias (SCAs), but well-powered in vivo studies are lacking and links with disease severity and progression remain unclear. Here we characterise cervical spinal cord morphometric abnormalities in SCA1, SCA2, SCA3 and SCA6 using a large multisite MRI dataset.

METHODS

Upper spinal cord (vertebrae C1-C4) cross-sectional area (CSA) and eccentricity (flattening) were assessed using MRI data from nine sites within the ENIGMA-Ataxia consortium, including 364 people with ataxic SCA, 56 individuals with preataxic SCA and 394 nonataxic controls. Correlations and subgroup analyses within the SCA cohorts were undertaken based on disease duration and ataxia severity.

RESULTS

Individuals in the ataxic stage of SCA1, SCA2 and SCA3, relative to non-ataxic controls, had significantly reduced CSA and increased eccentricity at all examined levels. CSA showed large effect sizes (d>2.0) and correlated with ataxia severity (r<-0.43) and disease duration (r<-0.21). Eccentricity correlated only with ataxia severity in SCA2 (r=0.28). No significant spinal cord differences were evident in SCA6. In preataxic individuals, CSA was significantly reduced in SCA2 (d=1.6) and SCA3 (d=1.7), and the SCA2 group also showed increased eccentricity (d=1.1) relative to nonataxic controls. Subgroup analyses confirmed that CSA and eccentricity are abnormal in early disease stages in SCA1, SCA2 and SCA3. CSA declined with disease progression in all, whereas eccentricity progressed only in SCA2.

CONCLUSIONS

Spinal cord abnormalities are an early and progressive feature of SCA1, SCA2 and SCA3, but not SCA6, which can be captured using quantitative MRI.

High-throughput characterization of the effect of sodium chloride and potassium chloride on 31 lactic acid bacteria and their co-cultures

Salt (NaCl) is associated with a risk of hypertension and the development of coronary heart disease, so its consumption should be limited. However, salt plays a key role in the quality and safety of food by controlling undesirable microorganisms. Since studies have focused primarily on the effect of salts on the overall counts of the lactic acid bacteria (LAB) group, we have not yet understood how salt stress individually affects the strains and the interactions between them. In this study, we characterized the effect of sodium chloride (NaCl) and potassium chloride (KCl) on the growth and acidification of 31 LAB strains. In addition, we evaluated the effect of salts on a total of 93 random pairwise strain combinations. Strains and co-cultures were tested at 3% NaCl, 5% NaCl, and 3% KCl on solid medium using an automated approach and image analysis. The results showed that the growth of LAB was significantly reduced by up to 68% at 5% NaCl (p < 0.0001). For the co-cultures, a reduction of up to 57% was observed at 5% NaCl (p < 0.0001). However, acidification was less affected by salt stress, whether for monocultures or co-cultures. Furthermore, KCl had a lesser impact on both growth and acidification compared to NaCl. Indeed, some strains showed a significant increase in growth at 3% KCl, such as Lactococcus lactis subsp. lactis 74310 (23%, p = 0.01). More importantly, co-cultures appeared to be more resilient and had more varied responses to salt stress than the monocultures, as several cases of suppression of the significant effect of salts on acidification and growth were detected. Our results highlight that while salts can modulate microbial interactions, these latter can also attenuate the effect of salts on LAB.

Extended correlation functions for spatial analysis of multiplex imaging data

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

Three-Dimensional-Printed Instrument for Isothermal Nucleic Acid Amplification with Real-Time Colorimetric Imaging

Isothermal amplification methods have become popular in research due to the simplicity of the technology needed to run the reactions. Specifically, loop-mediated isothermal amplification (LAMP) has been widely used for various applications since first reported in 2000. LAMP reactions are commonly monitored with the use of colorimetry. Although color changes associated with positive amplification are apparent to the naked eye, this detection method is subjective due to inherent differences in visual perception from person to person. The objectivity of the colorimetric detection method may be improved by programmed image capture over time with simultaneous heating. As such, the development of a novel, one-step, automated, and integrated analysis system capable of performing these tasks in parallel is detailed herein. The device is adaptable to multiple colorimetric dyes, cost-effective, 3D-printed for single-temperature convective heating, and features an easy-to-use LabVIEW software program developed for automated image analysis. The device was optimized and subsequently validated using four messenger-RNA targets and mock forensic samples. The performance of our device was determined to be comparable to that of a conventional thermal cycler and smartphone image analysis, respectively. Moreover, the outlined system is capable of objective colorimetric analysis, with exceptional throughput of up to 96 samples at once.

Preventing E. coli Biofilm Formation with Antimicrobial Peptide-Functionalized Surface Coatings: Recognizing the Dependence on the Bacterial Binding Mode Using Live-Cell Microscopy

Antimicrobial peptides (AMPs) can kill bacteria by destabilizing their membranes, yet translating these molecules' properties into a covalently attached antibacterial coating is challenging. Rational design efforts are obstructed by the fact that standard microbiology methods are ill-designed for the evaluation of coatings, disclosing few details about why grafted AMPs function or do not function. It is particularly difficult to distinguish the influence of the AMP's molecular structure from other factors controlling the total exposure, including which type of bonds are formed between bacteria and the coating and how persistent these contacts are. Here, we combine label-free live-cell microscopy, microfluidics, and automated image analysis to study the response of surface-bound Escherichia coli challenged by the same small AMP either in solution or grafted to the surface through click chemistry. Initially after binding, the grafted AMPs inhibited bacterial growth more efficiently than did AMPs in solution. Yet, after 1 h, E. coli on the coated surfaces increased their expression of type-1 fimbriae, leading to a change in their binding mode, which diminished the coating's impact. The wealth of information obtained from continuously monitoring the growth, shape, and movements of single bacterial cells allowed us to elucidate and quantify the different factors determining the antibacterial efficacy of the grafted AMPs. We expect this approach to aid the design of elaborate antibacterial material coatings working by specific and selective actions, not limited to contact-killing. This technology is needed to support health care and food production in the postantibiotic era.

Usefulness Assessment of Automated Strabismus Angle Measurements Using Innovative Strabiscan Device

BACKGROUND

The variability of the obtained results of manual tests assessing the angle of strabismus depends on the experience, skills, and training of the examiner. The authors hope that this new measuring device will provide a more sensitive and repeatable method for detecting small strabismus angles compared to the gold standard-PCT. The purpose of this article is to present an innovative strabismus angle demonstration device, called Strabiscan, to provide automated measurements of eye deviation and to compare the obtained results of these measurements to the traditional manual method.

METHODS

For patients with manifest strabismic disease (n = 30) and a group of healthy subjects (n = 30), a detailed history was taken and routine ophthalmologic examinations were performed, including best-corrected distance visual acuity, assessment of refractive error using an autorefractometer after cycloplegia, biomicroscopic evaluation of the anterior segment of the eye and evaluation of the eye fundus by indirect ophthalmoscopy. Subsequently, each patient and healthy subject was subjected to a prismatic cover-uncover test using a manual method, after which the presence of strabismus was detected and its angle assessed using a Strabiscan demonstration device.

RESULTS

In the control group using the Strabiscan demonstration device, small-angle latent strabismus ≤ 3DP was diagnosed in 83% of patients, while >3DP was found in 13%. In contrast, using the prismatic cover-uncover test, latent strabismus ≤ 3DP was diagnosed in only 13% of patients, and latent strabismus with an angle > 3DP was found in 13% of patients. No statistically significant differences were noted in the measurements of strabismus angles made by the different methods.

CONCLUSIONS

The Strabiscan demonstration device allows quick and accurate assessment of the strabismus angle. Compared to the prismatic cover-uncover test, it has a higher sensitivity for detecting low-angle latent strabismus. Measurements with the Strabiscan do not require the presence of additional assistants for the test.

MARTin-an open-source platform for microarray analysis

Background: Microarray technology has brought significant advancements to high-throughput analysis, particularly in the comprehensive study of biomolecular interactions involving proteins, peptides, and antibodies, as well as in the fields of gene expression and genotyping. With the ever-increasing volume and intricacy of microarray data, an accurate, reliable and reproducible analysis is essential. Furthermore, there is a high level of variation in the format of microarrays. This not only holds true between different sample types but is also due to differences in the hardware used during the production of the arrays, as well as the personal preferences of the individual users. Therefore, there is a need for transparent, broadly applicable and user-friendly image quantification techniques to extract meaningful information from these complex datasets, while also addressing the challenges posed by specific microarray and imager formats, which can flaw analysis and interpretation. Results: Here we introduce MicroArray Rastering Tool (MARTin), as a versatile tool developed primarily for the analysis of protein and peptide microarrays. Our software provides state-of-the-art methodologies, offering researchers a comprehensive tool for microarray image quantification. MARTin is independent of the microarray platform used and supports various configurations including high-density formats and printed arrays with significant x and y offsets. This is made possible by granting the user the ability to freely customize parts of the application to their specific microarray format. Thanks to built-in features like adaptive filtering and autofit, measurements can be done very efficiently and are highly reproducible. Furthermore, our tool integrates metadata management and integrity check features, providing a straightforward quality control method, along with a ready-to-use interface for in-depth data analysis. This not only promotes good scientific practice in the field of microarray analysis but also enhances the ability to explore and examine the generated data. Conclusion: MARTin has been developed to empower its users with a reliable, efficient, and intuitive tool for peptidomic and proteomic array analysis, thereby facilitating data-driven discovery across disciplines. Our software is an open-source project freely available via the GNU Affero General Public License licence on GitHub.

A perspective on FAIR quality control in multiplexed imaging data processing

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

An Integrated Framework for Image Acquisition, Processing, and Analysis Procedures for Automated Damage Evaluation of Concrete Surfaces

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82.

Comparison of Measured and Calculated Porosity Parameters of Woven Fabrics to Results Obtained with Image Analysis

Porosity, the measure of the open spaces within a fabric structure, is a decisive factor in the performance of textiles. It influences breathability, permeability to liquids or gases, and suitability for various industries such as apparel, medical, and technical textiles. This study compares classical porosity calculation methods with non-destructive image analysis for 24 woven fabric samples that differ in density and weave pattern. Factors such as fabric density, weave pattern, illumination conditions, magnification, and the influence of the Otsu and Yen threshold algorithms were considered. The multifactor ANOVA statistical analysis shows that fabric density and weave pattern significantly influence porosity, with illumination playing an important role, while the threshold algorithm has a minor influence. A strong correlation is found between the actual fabric porosity and the results of the image analysis, except for double-sided illumination (reflective and transmissive), where the correlation is weakest. This comprehensive investigation provides valuable insights into the reliability of different porosity assessment approaches, which is essential for applications in various textile industries.

RPE Curvature Can Screen for Early and Intermediate AMD

Purpose

Diagnosing AMD early optimizes clinical management. However, current diagnostic accuracy is limited by the subjectivity of qualitative diagnostic measures used in clinical practice. This study tests if RPE curvature could be an accurate, quantitative measure for AMD diagnosis.

Methods

Consecutive patients without AMD or normal aging changes (n = 111), with normal aging changes (n = 107), early AMD (n = 102) and intermediate AMD (n = 114) were recruited. RPE curvature was calculated based on the sinuosity method of measuring river curvature in environmental science. RPE and Bruch's membrane were manually segmented from optical coherence tomography B-scans and then their lengths automatically extracted using customized MATLAB code. RPE sinuosity was calculated as a ratio of RPE to Bruch's membrane length. Diagnostic accuracy was determined from area under the receiver operator characteristic curve (aROC).

Results

RPE sinuosity of foveal B-scans could distinguish any eyes with AMD (early or intermediate) from those without AMD (non-AMD or eyes with normal aging changes) with acceptable diagnostic accuracy (aROC = 0.775). Similarly, RPE sinuosity could identify intermediate AMD from all other groups (aROC = 0.871) and distinguish between early and intermediate AMD (aROC = 0.737). RPE sinuosity was significantly associated with known AMD lesions: reticular pseudodrusen (P < 0.0001) and drusen volume (P < 0.0001), but not physiological variables such as age, sex, and ethnicity.

Conclusions

RPE sinuosity is a simple, robust, quantitative biomarker that is amenable to automation and could enhance screening of AMD.