Microscopy Image Analysis Research Articles

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

Functionally distinct pericyte subsets differently regulate amyloid-β deposition in patients with Alzheimer's disease.

Although the concept that the blood-brain barrier (BBB) plays an important role in the etiology and pathogenesis of Alzheimer's disease (AD) has become increasingly accepted, little is known yet about how it actually contributes. We and others have recently identified a novel functionally distinct subset of BBB pericytes (PCs). In the present study, we sought to determine whether these PC subsets differentially contribute to AD-associated pathologies by immunohistochemistry and amyloid beta (Aβ) peptidomics. We demonstrated that a disease-associated PC subset (PC2) expanded in AD patients compared to age-matched, cognitively unimpaired controls. Surprisingly, we found that this increase in the percentage of PC2 (%PC2) was correlated negatively with BBB breakdown in AD patients, unlike in natural aging or other reported disease conditions. The higher %PC2 in AD patients was also correlated with a lower Aβ42 plaque load and a lower Aβ42:Aβ40 ratio in the brain as determined by immunohistochemistry. Colocalization analysis of multicolor confocal immunofluorescence microscopy images suggests that AD patient with low %PC2 have higher BBB breakdown due to internalization of Aβ42 by the physiologically normal PC subset (PC1) and their concomitant cell death leading to more vessels without PCs and increased plaque load. On the contrary, it appears that PC2 can secrete cathepsin D to cleave and degrade Aβ built up outside of PC2 into more soluble forms, ultimately contributing to less BBB breakdown and reducing Aβ plaque load. Collectively our data shows functionally distinct mechanisms for PC1 and PC2 in high Aβ conditions, demonstrating the importance of correctly identifying these populations when investigating the contribution of neurovascular dysfunction to AD pathogenesis.

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

BackgroundThe assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches.MethodsHereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. The developed models were fed using two independent datasets after applying a novel augmentation method aimed at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch.ResultsThe average dice similarity coefficient (DSC) scores were obtained as 0.958∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}0.968 for U-net-based deep learning models. The averaged absolute percentage errors (PE) of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had considerable averaged error rates of 22.59% and 33.88%, respectively.ConclusionsComparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models also hold great promise for the prediction of the in vitro wound area, regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.

Investigating correlations between microstructural characteristics and mechanical properties of coral sand mortar with different silica fume contents

The use of coral sand in concrete and mortar for ocean engineering has led to significant reductions in construction costs and improved construction efficiency. However, this approach poses challenges such as low strength, high brittleness, and high porosity. To address these challenges, this study incorporated silica fume to enhance the mechanical properties of the mortar and investigated the effects of different silica fume contents on the hydration process, pore structure, and strength of coral sand mortar. Additionally, this study introduced a novel iterative approach combining low-field nuclear magnetic resonance and scanning electron microscopy image analysis to determine transverse relaxivity (ρ2). During the early hydration process, the intensity spectrum of coral mortar exhibited three peaks at 298, 1841, and 299 au, corresponding to transverse relaxation times (T2) of 0.64, 6.83, and 83.10 ms, respectively. Over a 6 h hydration period, the spectrum of the specimen prepared with white cement exhibited an additional peak that gradually merged with the main peak. As the curing period extended from 7 to 28 days, the porosity of coral sand mortar decreased by 3.77–4.55 %, while the strength increased by 7.43–14.35 MPa across silica fume contents ranging from 6 % to 24 %. With increasing silica fume content, the calcium–silicon ratio of the samples gradually decreased, promoting the formation of more floccus C–S–H gels. Consequently, the sample porosity first decreased slightly. After 28 days of curing, the coral sand mortar containing 12 % silica fume exhibited the highest strength owing to the increased amount of floccus C–S–H gels. These gels gradually filled the pores between the fibrous C–S–H gels, forming a compact spatial reticular structure. This structure mitigated the adverse impact of high porosity on mortar strength.

Development of UV protective and antimicrobial CVC fabric using colloidal zinc oxide solution with carrot and orange peel extract

AbstractChronic exposure to ultraviolet (UV) radiation can cause degenerative changes in the cells, fibrous tissues and blood vessels. Moreover, microbiological attacks pose severe risks to human health and are a major cause of sickness worldwide. The aim of this work is to develop UV protective and antimicrobial materials using zinc oxide (ZnO) colloidal solution with carrot and orange peel extract. Treated samples were subjected to color strength (K/S value) measurement, scanning electron microscope (SEM) image analysis, attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy analysis, ultraviolet protection factor (UPF) test, bacterial reduction rate analysis and bursting strength measurement. The study revealed that the treated fabric exhibited remarkable UV protection capabilities, as evidenced by low transmission percentages across UV wavelengths and an outstanding UPF rating of 50, signifying excellent defense against harmful ultraviolet radiation. Additionally, antibacterial tests revealed the significant effectiveness of the treatment against common bacterial strains, with impressive reduction rates for Staphylococcus aureus (99.99%) and Escherichia coli (99.17%). Furthermore, the fabric's bursting strength, both in dry and wet conditions, remained nearly unchanged after treatment. ATR‐FTIR analysis and SEM imaging provided insights into the phytochemical compounds that give protection against UV composition (44.63–49.91) and surface morphology of the treated fabric, elucidating the mechanisms behind its enhanced properties. Due to the availability of carrot and orange peel in nature and low cost of raw materials, this process can be considered to apply commercially for further research purposes.Highlights Utilization of carrot and orange peel waste. Successful application of zinc oxide for antibacterial and ultraviolet protective materials. Excellent defense against harmful ultraviolet radiation. Significant effectiveness against common bacterial strains. Low cost and commercially viable functional materials.

The development of an automated microscope image tracking and analysis system.

Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.

Alignment assessment of anisotropic liquid crystals through an automated image processing algorithm

Liquid crystals (LCs) are recognised as an emerging type of responsive and functional materials that exhibit diverse technological applications. The spatial arrangement or alignment of LC molecules creates an optical anisotropy, which influences the propagation of light. This study presents an automated image processing algorithm designed to quantitatively assess LC alignment. The algorithm calculates the image structure tensor and computes a score ranging from 0 to 1 to represent the uniformity of LC alignment. The tool was verified on both artificially generated images of lines with pre-defined orientations and then tested on microscope images of LCs. For the latter, test samples of deformed helix ferroelectric LCs were constructed using three different alignment methods. Analysis of microscopic images of samples prepared utilising photoalignment, polymer rubbing, and oblique evaporation alignment techniques, determined average alignment scores (mean ± standard error of the mean) of 0.44 ± 0.03, 0.26 ± 0.1, and 0.17 ± 0.04, respectively, in comparison to commercially sourced control samples with a score of 0.64 ± 0.03 (n=5 each). This study paves the way for automated objective assessment of LC alignment, a critical aspect to ensure the optimal performance, reliability, and efficiency of various devices and sensors relying on LC technology.

The adult plant resistance (APR) genes Yr18, Yr29 and Yr46 in spring wheat showed significant effect against important yellow rust races under North-West European field conditions

Yellow rust caused by Puccinia striiformis f. sp. tritici (Pst) is one of the most important wheat diseases. Adult plant resistance (APR) genes have gained the attention of breeders and scientists because they show higher durability compared to major race-specific genes. Here, we determined the effect of the APR genes Yr18, Yr29 and Yr46 in North-West European field conditions against three currently important Pst races. We used three pairs of sibling wheat lines developed at CIMMYT, which consisted of a line with the functional resistance gene and a sibling with its non-functional allele. All APR genes showed significant effects against the Pst races Warrior and Warrior (–), and a race of the highly aggressive strain PstS2. The effects of Yr18 and Yr46 were especially substantial in slowing down disease progress. This effect was apparent in both Denmark, where susceptible controls reached 100 percent disease severity, and in United Kingdom where disease pressure was lower. We further validated field results by quantifying fungal biomass in leaf samples and by micro-phenotyping of samples collected during early disease development. Microscopic image analyses using deep learning allowed us to quantify separately the APR effects on leaf colonization and pustule formation. Our results show that the three APR genes can be used in breeding yellow rust resistant varieties of spring wheat to be grown in North-West European conditions, and that deep learning image analysis can be an effective method to quantify effects of APR on colonisation and pustule formation.

In situ fabrication of atomically adjacent dual-vacancy sites for nearly 100% selective CH4 production

The photocatalytic CO2-to-CH4 conversion involves multiple consecutive proton-electron coupling transfer processes. Achieving high CH4 selectivity with satisfactory conversion efficiency remains challenging since the inefficient proton and electron delivery path results in sluggish proton-electron transfer kinetics. Herein, we propose the fabrication of atomically adjacent anion-cation vacancy as paired redox active sites that could maximally promote the proton- and electron-donating efficiency to simultaneously enhance the oxidation and reduction half-reactions, achieving higher photocatalytic CO2 reduction activity and CH4 selectivity. Taking TiO2 as a photocatalyst prototype, the operando electron paramagnetic resonance spectra, quasi in situ X-ray photoelectron spectroscopy measurements, and high-angle annular dark-field-scanning transmission electron microscopy image analysis prove that the VTi on TiO2 as initial sites can induce electron redistribution and facilitate the escape of the adjacent oxygen atom, thereby triggering the dynamic creation of atomically adjacent dual-vacancy sites during photocatalytic reactions. The dual-vacancy sites not only promote the proton- and electron-donating efficiency for CO2 activation and protonation but also modulate the coordination modes of surface-bound intermediate species, thus converting the endoergic protonation step to an exoergic reaction process and steering the CO2 reduction pathway toward CH4 production. As a result, these in situ created dual active sites enable nearly 100% CH4 selectivity and evolution rate of 19.4 μmol g-1 h-1, about 80 times higher than that of pristine TiO2. Thus, these insights into vacancy dynamics and structure-function relationship are valuable to atomic understanding and catalyst design for achieving highly selective catalysis.

Rice husk-derived cobalt silica as effective peroxymonosulfate activator for degradation of organic pollutants

In this study, mesoporous silica was synthesized from rice husks, and subsequently used to prepare a cobalt silica (Co/SiO2) catalyst via a simple sol–gel approach. The synthesized Co/SiO2 catalyst was thoroughly characterized. The analysis of the scanning electron microscopy (SEM) images revealed silica particles with sizes of nearly 20 nm. Energy-dispersive X-ray analysis showed a good Co dispersion on the catalyst surface with a Co content of 7.7 %. The Co/SiO2 catalyst was then utilized as a peroxymonosulfate (PMS) activator to degrade reactive black 5 (RB5). Furthermore, the impact of various parameters on RB5 degradation was analyzed. The Co/SiO2 catalyst demonstrated excellent performance and completely degraded RB5 within 8 min with a rate constant of 5.5 × 10−1 min−1 at an extremely low dosage of 0.05 g/L. Furthermore, the degradation rate increased with temperature, and the activation energy was 51.5 kJ/mol. The analysis of the quenching and electron paramagnetic resonance experiments showed that SO4• −, HO•, 1O2, and O2• − were produced, with 1O2 being the predominant reactive species. Notably, the catalyst remained effective when tested in actual water environments and could degrade various other dyes and tetracycline. To facilitate catalyst recovery through filtration, Co/SiO2 was embedded in crosslinked chitosan beads. The bead catalyst demonstrated comparable performance to pristine Co/SiO2 and could be reused for five cycles.

Analysis of microscopic images using image processing to measure homogeneity of metal powder mixture samples

This paper presents automated image analysis frameworks that leverage Python's computer vision libraries, including OpenCV and Matplotlib, to analyze and characterize color-shade distributions in microscopic images of metal powder mixtures. The proposed framework segments the images into multiple regions of interest (ROIs), calculates and visualizes color histograms, and extracts quantitative information about the color distribution within each ROI. The developed image processing framework measures the homogeneity of the powder mixture by surface analysis of the image of samples. This article demonstrates the efficacy of these frameworks on a set of microscopy images, highlighting their potential to measure the homogeneity of the metal powder mixture. Homogeneity is a crucial factor in material science, as it directly impacts the performance and properties of the resulting material. The developed technique is economical compared to available methods to predict the homogeneity of metal powder mixtures, such as Tomographic and Spectroscopic analyses. It is an effective technique to measure the homogeneity of metal powder mixtures exhibiting different colors or shades of color in computer vision. In this work, the homogeneity is analyzed for the powder mixtures consisting of 80% aluminum (Al) and 20% copper (Cu), 90% aluminum (Al) and 10% copper (Cu) and 90% silicon carbide (SiC) and 10% aluminum (Al) using processing of microscopic color images.

Dynamic Adversarial Adaptation Network with selective pseudo-labels for enhanced unsupervised domain adaptation in rock microscopic image analysis

The critical role of lithology classification in reservoir exploration is increasingly germinating interest in intelligent rock image classification applications. Nonetheless, the efficacy of these classification methods predominantly hinges on the premise that the independent and identically distributed principles underlie the training data. Real-world training data, amassed from disparate geographical regions, tend to manifest non-uniform distributions, which is a factor that muddles the accuracy of contemporary classification strategies. In light of the aforementioned scenario and considering the scarcity of labeled data in the target domain, we suggest a selective pseudo-label-based domain adversarial adaptation. This approach earmarks the generation of an adversarial network that aligns both the conditional and marginal distributions of the source and target domains concurrently during adversarial training. By infusing a dynamic factor, we straddle an adaptable balance between distributions and integrate label information from the target domain using selective pseudo-labeling within the adversarial training phase. Notably, our class-wise pseudo-label technology application determines the pseudo-label threshold dynamically via the selection factor. The thorough evaluation of our proposed approach across four distinct geographic areas encompassing 12 tasks demonstrated that our model produced an average accuracy of 72.2%, signaling a 30% improvement over the source-only model. Importantly, relative to six traditional Unsupervised Domain Adaptation (UDA) algorithms, our approach performed remarkably better, recording an average accuracy surge of a minimum of 3%. Sensitivity assessments also portrayed the robustness of our model in responding to parameter alterations, causing a trivial 1% accuracy fluctuation.

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection

Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC.

Effect of ultrasonic and Er,Cr:YSGG laser-activated irrigation protocol on dual-species root canal biofilm removal: An in vitro study.

The aim of the study was to investigate the disinfecting efficacy of a standardized irrigating solution activated by ultrasonics or laser irradiation on mature dual-species biofilms at different root levels in vitro. Conventional access cavity preparations were done on 160 single-rooted mandibular premolar teeth with single canals. Freshly extracted oral microbial strains of Staphylococcus aureus, Streptococcus mutans, Enterococcus faecalis, and Candida albicans after biochemical confirmation were used to generate two discrete dual-species microbial inoculums. The sterilized tooth samples were randomly segregated into two groups (n = 80) and inoculated with a mixed inoculum of S. aureus + E. faecalis strains (Group 1) and S. mutans + C. albicans strains (Group 2), respectively. Following the 21-day incubation period under aerobic conditions, the infected specimens in each group were divided into four subgroups (n = 20) and subjected to experimental treatment protocols. This included a positive control (no treatment of biofilms), syringe irrigation alone with TruNatomy needle, passive ultrasonically activated irrigation with 20# Irrisafe tip, and laser agitation of irrigant with Er,Cr:YSGG laser using RFT 2 laser tip. Root canals of experimental specimens (except the control samples) are instrumented with TruNatomy rotary file system using 1:1 mixture of 3% NaOCl and 18% etidronic acid as irrigants. The quantitative assessment of reduction in viable biofilm microbes after treatment was done using colony-forming unit counts and confocal laser scanning microscopy image analysis. The obtained data were analyzed statistically with a significant level set at 0.05. Laser-assisted irrigation has shown a considerably higher mean percentage reduction of microbes compared to ultrasonic agitation and the syringe irrigation showed the least microbial reduction (P = 0.001). No significant difference was noted between the three root regions of ultrasonic and laser groups (P > 0.05), whereas in the syringe groups, apical portions showed higher microbial counts compared to cervical and mid-root regions (P = 0.001). Erbium laser-assisted irrigation has performed superior to ultrasonic agitation against both the experimental dual-species biofilms, while the syringe irrigation showed the least microbial reduction specifically at apical root portions.

Surface pretreatment of scheelite and cassiterite by gallic acid to achieve high efficiency flotation separation

Scheelite and cassiterite are common tungsten and tin oxide minerals that often coexist in nature. However, the similar oxidation degrees and solution and surface chemical properties of scheelite and cassiterite pose challenges to their flotation separation. Moreover, research in this area is currently limited. This study investigated the selective depression mechanism of gallic acid (GA) in the flotation separation of scheelite and cassiterite and successfully achieved the effective separation of the two minerals. Single mineral and artificially mixed mineral experiments were performed to analyze the flotation separation behavior of scheelite and cassiterite. The results revealed that in the absence of a depressant, scheelite and cassiterite exhibited recovery of 93.43 % and 89.72 %, respectively. After the addition of 5 × 10−4 mol/L GA to the mineral slurry, the grades of WO3 and Sn in the flotation concentrates were 48.93 % and 18.13 %, with recovery of 82.50 % and 31.62 %, respectively. Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, zeta potential, adsorption amount, contact angle, and atomic force microscopy imaging analyses were conducted to examine the selective depression mechanism of GA in the flotation separation of scheelite and cassiterite. In the slurry, the cassiterite surface provided active Sn sites for the chemical adsorption of GA. Moreover, the phenolate O- donor atoms of the deprotonated polar groups of GA will undergo coordination with metal ions and then adsorb on the mineral surface. GA forms metal gallate through adsorption of its –COOH and –OH groups on mineral surface and produces textured oxide layer rich in OH groups, thus reducing the surface hydrophobicity of cassiterite and then depressing cassiterite. The selective adsorption of GA on the cassiterite surface hindered the further adsorption of NaOL, thereby reducing its surface hydrophobicity. Therefore, GA finally realized the flotation separation of the two minerals.

Studies of indium nanostructures growth models on A3B6 layered templates

This article explores the surface architecture formation on layered A3B6 semiconductors, such as In4Se3, InSe, and InTe, focusing on kinetics of surface roughness parameters, such as RMS, skewness and kurtosis, and power spectral density (PSD) under indium deposition studied through large-scale 1 × 1 µm2 and 150 × 150 nm2 scanning tunneling microscopy (STM) images analysis. Our findings reveal common kinetics of surface parameters across different crystals, suggesting fundamental similarities in their interface kinetics. Particularly, PSD analysis elucidates the role of low-frequency spatial structures, underscoring their significance in surface roughness kinetics to some extent independent of the specific layered crystal surface structure.

The Impact of Calcium Depletion on Proliferation of Chlorella sorokiniana Strain DSCG150.

This study analyzed the effects of Ca2+ metal ions among culture medium components on the Chlorella sorokiniana strain DSCG150 strain cell growth. The C. sorokiniana strain DSCG150 grew based on a multiple fission cell cycle and growth became stagnant in the absence of metal ions in the medium, particularly Ca2+. Flow cytometry and confocal microscopic image analysis results showed that in the absence of Ca2+, cell growth became stagnant as the cells accumulated into four autospores and could not transform into daughter cells. Genetic analysis showed that the absence of Ca2+ caused upregulation of calmodulin (calA) and cell division control protein 2 (CDC2_1) genes, and downregulation of origin of replication complex subunit 6 (ORC6) and dual specificity protein phosphatase CDC14A (CDC14A) genes. Analysis of gene expression patterns by qRT-PCR showed that the absence of Ca2+ did not affect cell cycle progression up to 4n autospore, but it inhibited Chlorella cell fission (liberation of autospores). The addition of Ca2+ to cells cultivated in the absence of Ca2+ resulted in an increase in n cell population, leading to the resumption of C. sorokiniana growth. These findings suggest that Ca2+ plays a crucial role in the fission process in Chlorella.

Thermomechanical and physicochemical evolution of meat‐analogue based fried foods

SummaryThis study aimed to get mechanistic insights on the evolution of major quality attributes of meat‐analogue (MA) based batter‐coated fried products. Wheat and rice flour‐based batter systems were used to coat a MA. The products were deep‐fried at 180°C for 4 min in canola oil and their post‐fry quality changes at room environment (RE, 25°C) and under IR‐heating (65 °C) were investigated. Results showed that in addition to moisture‐fat profile, batter coating substantially (P < 0.05) influenced the development of thermal, textural, colour and microstructural traits of MA. Post‐fry colour changes (ΔE value: 3.1 to 10.4) in MA‐based coated product continued under IR‐heating (65 °C). Evolution of the thermal, textural and colour attributes of MA‐based coated products were significantly (P < 0.05) influenced by the formulation of outer batter coatings. Moisture migration occurred from high moisture (ranged from 1.07 to 1.25 g g−1 dry matter) containing core to dry (moisture ranged from 0.15 to 0.59 g g−1 dry matter) crust region of MA‐based coated fried product, where the moisture migration was interlaced with batter formulations. Holding environment and duration substantially (P < 0.05) impacted the textural properties (hardness, brittleness and crispiness) of MA‐based coated fried product. Glass transition temperature (Tg) of coated fried products were ranged between −20.4 to −23.0 °C, that explains quality changes at RE and under IR‐heating. Scanning electron microscopic image analysis (surface openings: 5.7 to 27.31%; fractal dimension: 2.555 to 2.702) revealed the impact of surface microstructure and mass‐transfer modulated textural development in MA‐based coated products. FTIR spectroscopy revealed surface chemical profile in relation to colour evolution of MA‐based fried products. Principal component analysis outcome (PC1: 52.9.% and PC2: 25.8%) revealed varying extent of correlation between studied thermomechanical and physicochemical attributes.

Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer

Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms and tailoring the production processes. Analysis of microscopy images that reveal defects typically requires image analysis tasks such as segmentation and object detection. With the permanently increasing amount of data from experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining techniques to create a robust and accurate, automated image analysis pipeline for extracting the type and position of all defects in a microscopy image of a KOH-etched 4H-SiC wafer.Graphical abstract

Performance of solid-oxide fuel cells operating with different sustainable fuel reformates

Solid oxide fuel cells (SOFCs) are recognized for their outstanding fuel flexibility and high efficiency in converting chemical energy into electrical energy. This research presents experiments on SOFCs fed with humidified hydrogen and hydrogen-rich gaseous reformates derived from methanol and ammonia: methanol steam reforming (MSR), methanol decomposition (MD), and ammonia decomposition (AD), at 700–850 °C. Results of SOFCs operating with MD reformate on different types of SOFCs, which to the best of our knowledge aren't available in the literature, are presented and compared with other reformate types for the first time. MD-reformate yielded the highest cell performance, with the least disparity in power density from humidified hydrogen, followed by AD-, and MSR-reformates. The study compares the direct internal reforming method, and the innovative Combined Electro-Thermo-Chemical cycle's potential. Introducing the concept of critical power density, the research evaluates fuel utilization across SOFC types. Scanning Electron Microscopy imaging and Energy-Dispersive X-ray Spectroscopy analyses confirm the viability of CO as a fuel, with no carbon deposits on the anodes when using MD-reformate. The findings demonstrate the suitability of using methanol- and ammonia-decomposition products in SOFCs and their compatibility with hybrid power generation cycles.