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Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks' architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%. [ABSTRACT FROM AUTHOR]

Poly(vinyl chloride) (PVC) films were synthesized by incorporating organic groups, specifically amino groups derived from ethylene di-amine (en). The casting process was employed, with tetrahydrofuran (THF) utilized as the solvent. The incorporation of 4 metal oxide nanoparticles (NPs), namely MgO, ZnO, NiO and TiO2, was carried out to enhance the stability of the films during light exposure. The effect of UV radiation dose was significant. Subsequent exposure of the PVC films to ultraviolet light enabled the evaluation of resulting damage through various analytical and morphological techniques. The utilization of infrared spectroscopy and weight loss study suggested that the films incorporating additives exhibited less harm and little alterations on the surface, as compared to the unaltered film. Among the modified films, the PVC-en/TiO2 NPs film exhibited the highest resistance against the photodegradation process, as evidenced by the data derived from FTIR spectra, weight loss measurements, and surface morphology analyses. Titanium dioxide nanoparticles have been good PVC photostabilizer because of its capacity to block ultraviolet (UV) radiation. The atomic force microscopy (AFM) photographs of the PVC-en/TiO2 NPs film after irradiation revealed a smooth surface, exhibiting a roughness factor (Rq) of 34.3, in contrast to the PVC (blank) which had a roughness factor of 282.2. SEM images for irradiated PVC films reveal the existence of cracks, cavities, protrusions, blemishes and formless, uneven surfaces. Microscopic images revealed that the surface of untreated PVC films exhibited significantly more pronounced damage and anomalies following irradiation compared to modified PVC films incorporating nano-metal oxides. [ABSTRACT FROM AUTHOR]

Background: Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods: Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results: The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions: This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models. [ABSTRACT FROM AUTHOR]

In response to the challenges of high noise, high adhesion, and a low signal-to-noise ratio in microscopic cell images, as well as the difficulty of existing deep learning models such as UNet, ResUNet, and SwinUNet in segmenting images with clear boundaries and high-resolution, this study proposes a CellGAN semantic segmentation method based on a generative adversarial network with a Feature Completion Mechanism. This method incorporates a Transformer to supplement long-range semantic information. In the self-attention module of the Transformer generator, bilinear interpolation for feature completion is introduced, reducing the computational complexity of self-attention to O(n). Additionally, two-dimensional relative positional encoding is employed in the self-attention mechanism to supplement positional information and facilitate position recovery. Experimental results demonstrate that this method outperforms ResUNet and SwinUNet in segmentation performance on rice leaf cell, MuNuSeg, and Nucleus datasets, achieving up to 23.45% and 19.90% improvements in the Intersection over Union and Similarity metrics, respectively. This method provides an automated and efficient analytical tool for cell biology, enabling more accurate segmentation of cell images, and contributing to a deeper understanding of cellular structure and function. [ABSTRACT FROM AUTHOR]

Abstract Stitching of microscopic images is a technique used to combine multiple overlapping images (tiles) from biological samples with a limited field of view and high resolution to create a whole slide image. Image stitching involves two main steps: pairwise registration and global alignment. Most of the computational load and the accuracy of the stitching algorithm depend on the pairwise registration method. Therefore, choosing an efficient, accurate, robust, and fast pairwise registration method is crucial in the whole slide imaging technique. This paper presents a detailed comparative analysis of different pairwise registration techniques in terms of execution time and quality. These techniques included feature-based methods such as Harris, Shi-Thomasi, FAST, ORB, BRISK, SURF, SIFT, KAZE, MSER, and deep learning-based SuperPoint features. Additionally, region-based methods were analyzed, which were based on the normalized cross-correlation (NCC) and the combination of phase correlation and NCC. Investigations have been conducted on microscopy images from different modalities such as bright-field, phase-contrast, and fluorescence. The feature-based methods were highly robust to uneven illumination in tiles. Moreover, some features were found to be more accurate and faster than region-based methods, with the SURF features identified as the most effective technique. This study provides valuable insights into the selection of the most efficient and accurate pairwise registration method for creating whole slide images, which is essential for the advancement of computational pathology and biology.

AbstractLeukemia is a type of cancer that affects the body’s blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals.

Leukemia is one of the deadliest diseases that occur in white blood cells which is identified in microscopic images from blood samples. Researchers have developed various techniques to diagnose leukemia using a machine learning approach by analyzing micro imaging blood samples. During image screening, the Damaged cells be identified based on manually counting on similar structure lymphocytes and monocytes structures, but the variant difference of both cells aren't identified accurately. So the identification failures that occur lead to failed accurate cancer detection because feature variants project equal cell structure. To resolve this problem, we propose an Invariant Structural Cascade Segmentation (ISCS) based on Deep Vectorized Scaling Neural Network (DVSNN) is implemented to detect the Leukemia Cancer automatically from bio-blood samples micro image for early diagnosis. First, the Leukemia Cancer micro-image dataset (LCMID) was collected and preprocessed into a noise-free dataset based on adaptive median filters. Then the segmentation was carried to partition the microcells using Invariant structural cascade segmentation (ISCS) optimized with watershed to identify the features such as texture, pixel color, pixel intensity. For identifying the structural components of cells variation difference using Angular Vector Projection (AVP) was applied to find the structural variance. Then the histogram color equalizer (HCE) was applied to select the damaged cells using K-counts and their feature weight. Then the selected feature weights are trained into Deep vectorized scaling the neural network to classify the risk of the Leukemia cancer cells. This identifies the cancer cells effectively from micro image cells for detecting the risk of the patients. This improves the sensitivity, specificity rate as well in classification accuracy compared to the other methods. [ABSTRACT FROM AUTHOR]

Hair colour is a valuable feature in forensic hair analysis and hair comparisons. Five hundred microscopic images of hair shafts were taken and for each image and colour model (RGB, CIE XYZ and CIE L*a*b*) values for each image were determined. The discriminating power of the three colour model values using statistical and unsupervised machine learning methods was evaluated. Additionally, the discriminating power of the images of the same hair shafts using unsupervised machine learning methods was evaluated. All methods were compared to determine which method had the greatest discriminating power to best distinguished between participants and accurately assigned hair to an individual and assist in forensic hair comparisons. The RGB colour model demonstrated the highest discriminating power of the colour model values while the CIE L*a*b* model achieved complete discrimination with a reduced number of participants. The unsupervised k-means model yielded similar results. Unsupervised PCA/k-means and agglomerative clustering models demonstrated low discrimination power of the images, suggesting the existence of additional features within the data beyond colour. This research highlights the significance of the incorporation of colour values in forensic hair comparisons and for further exploration of the incorporation of other hair features, beyond colour values. [ABSTRACT FROM AUTHOR]

Stitching of microscopic images is a technique used to combine multiple overlapping images (tiles) from biological samples with a limited field of view and high resolution to create a whole slide image. Image stitching involves two main steps: pairwise registration and global alignment. Most of the computational load and the accuracy of the stitching algorithm depend on the pairwise registration method. Therefore, choosing an efficient, accurate, robust, and fast pairwise registration method is crucial in the whole slide imaging technique. This paper presents a detailed comparative analysis of different pairwise registration techniques in terms of execution time and quality. These techniques included feature-based methods such as Harris, Shi-Thomasi, FAST, ORB, BRISK, SURF, SIFT, KAZE, MSER, and deep learning-based SuperPoint features. Additionally, region-based methods were analyzed, which were based on the normalized cross-correlation (NCC) and the combination of phase correlation and NCC. Investigations have been conducted on microscopy images from different modalities such as bright-field, phase-contrast, and fluorescence. The feature-based methods were highly robust to uneven illumination in tiles. Moreover, some features were found to be more accurate and faster than region-based methods, with the SURF features identified as the most effective technique. This study provides valuable insights into the selection of the most efficient and accurate pairwise registration method for creating whole slide images, which is essential for the advancement of computational pathology and biology. [ABSTRACT FROM AUTHOR]

Automated urine sediment analyzers play a crucial role in diagnosing urinary tract infections, offering real-time data analysis and expediting patient diagnosis. This paper introduces a novel hybrid approach combining data-centric and model-centric techniques for automated urine sediment analysis. The proposed methodology addresses challenges such as morphological similarities among particle classes, uneven particle distribution, and intra/inter-class variations. A modified version of convolutional neural network (CNN), specifically the Visual Geometry Group (VGG-19) model, incorporating transfer learning, along with data augmentation is proposed for automated urine sediment classification with 98% accuracy and impressive inference time of 61ms per image. The proposed approach outperforms existing methods, especially in handling diverse sediment categories, demonstrating its potential for practical applications in medical diagnostics. We proposed the integration of a data-centric approach for improved labeling reliability and a model-centric approach for fine-tuning of the deep learning model, showcasing promising results in recognizing 12 distinct urine sediment classes. This study also emphasizes the importance of collaboration with medical professionals in refining the model’s performance and handling challenges related to data acquisition and class imbalance. The proposed approach provides a significant advancement in automating and enhancing urine sediment analysis processes.

At present, detection methods for rice microbial indicators are usually based on microbial culture or sensory detection methods, which are time‐consuming or require expertise and thus cannot meet the needs of on‐site rice testing when the rice is taken out of storage or traded. In order to develop a fast and non‐destructive method for detecting rice mildew, in this paper, micro‐computer vision technology is used to collect images of mildewed rice samples from 9 image locations. Then, a YOLO‐V5 convolutional neural network model is used to detect moldy areas of rice, and the mold coverage area is estimated. The relationship between the moldy areas and the total number of bacterial colonies in the image is obtained. The results show that the precision and the recall of the established YOLO‐v5 model in identifying the mildewed areas of rice in the validation set were 82.1% and 86.5%, respectively. Based on the mean mildewed area identified by the YOLO‐v5 model, the precision and recall for light mold detection were 100% and 95.3%, respectively. The proposed method based on micro‐computer vision and the YOLO convolutional neural network can be applied to the rapid detection of mildew in rice taken out of storage or traded. [ABSTRACT FROM AUTHOR]

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (x30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model's accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing. [ABSTRACT FROM AUTHOR]

Leukemia is a type of cancer that affects the body's blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals. [ABSTRACT FROM AUTHOR]

The Zingiberaceae, the ginger family, is a family of monocotyledonous herbaceous plants with creeping horizontal or tuberous rhizomes. The plants are aromatic, characterized by the presence of volatile oils and oleoresins. The Zingiberaceae are especially abundant in Southeast Asia and they have been widely used as spices, ornamental, or medicinal plants. The most important commercial spices in the family are ginger (Zingiber officinale), turmeric (Curcuma longa), green cardamom (Elettaria cardamomum) and black cardamom (Amomum subulatum), that also have a great number of health properties. Due to their use in the food and pharmaceutical industries, the microscopic analysis and other parameters of the whole rhizomes/powder of turmeric and ginger provide valuable information in the identification of the plant material, followed by biochemical analysis. In the current study, a microscopic analysis of ginger and turmeric rhizome was carried out to record some of the parameters of taxonomic relevance using a simple method. [ABSTRACT FROM AUTHOR]

The rise in fungal infections, attributed to various factors including medical interventions and compromised immune systems, necessitates rapid and accurate identification methods. While traditional mycological diagnostics are time-consuming, machine learning offers a promising alternative. Nevertheless, the scarcity of well-curated datasets is a significant obstacle. To address this, a novel approach for identifying fungi in microscopic images using Residual Neural Networks and a subimage retrieval mechanism is proposed, with the final step involving the implementation of majority voting. The new method, applied to the Digital Images of Fungus Species database, surpassed the original patch-based classification using Convolutional Neural Networks, obtaining an overall classification accuracy of 94.7% compared to 82.4% with AlexNet FV SVM. The observed MCC metric exceeds 0.9, while AUC is near to one. This improvement is attributed to the optimization of hyperparameters and top layer architecture, as well as the effectiveness of the Mish activation function in ResNet-based architectures. Noteworthy, the proposed method achieved 100% accurate classification for images from 8 out of 9 classes after majority voting and is high resistant to overfitting, highlighting its potential for rapid and accurate fungal species identification in medical diagnostics and research. [ABSTRACT FROM AUTHOR]

In response to the challenges of high noise, high adhesion, and a low signal-to-noise ratio in microscopic cell images, as well as the difficulty of existing deep learning models such as UNet, ResUNet, and SwinUNet in segmenting images with clear boundaries and high-resolution, this study proposes a CellGAN semantic segmentation method based on a generative adversarial network with a Feature Completion Mechanism. This method incorporates a Transformer to supplement long-range semantic information. In the self-attention module of the Transformer generator, bilinear interpolation for feature completion is introduced, reducing the computational complexity of self-attention to O(n). Additionally, two-dimensional relative positional encoding is employed in the self-attention mechanism to supplement positional information and facilitate position recovery. Experimental results demonstrate that this method outperforms ResUNet and SwinUNet in segmentation performance on rice leaf cell, MuNuSeg, and Nucleus datasets, achieving up to 23.45% and 19.90% improvements in the Intersection over Union and Similarity metrics, respectively. This method provides an automated and efficient analytical tool for cell biology, enabling more accurate segmentation of cell images, and contributing to a deeper understanding of cellular structure and function.

The carrier is one of the key components used in wastewater treatment, which can enrich microorganisms at the surface to improve the amount of biomass in the reactor. Monitoring and adjusting the number of carriers is a key component for the processing efficiency of the ecosystem, which directly impacts wastewater treatment effectiveness. Therefore, carrier detection in wastewater microscopic images is important in the urban domestic wastewater treatment process. The current process to detect carriers is operator-dependent, which is time-consuming and expensive. Though a number of general object or cell detection approaches are used for this task, their effectiveness is limited because the carrier and background are similar and there are defective carriers as background noise. In this paper, we propose CarrDet, the first deep learning-based carrier detection framework for wastewater treatment. CarrDet uses a carrier feature block attention module and a symmetry-based defective carrier detection module to detect carriers with shallow edges and reduce false positives caused by defective carriers, respectively. To evaluate CarrDet, we propose a carrier dataset of 600 wastewater microscopic images, manually annotated by experts. Compared with state-of-the-art object detection methods, CarrDet shows superior performance in terms of both accuracy and speed, achieving a mAP of 94.32 and an IPS of 4.93. We employed CarrDet to confirm the detection results of 621 wastewater microscopic images, which were detected by inexperienced engineers who are new to the field. CarrDet added 398 unrecognized carriers with shallow edges and corrected 273 incorrect manual annotations in 5 min, which emphasizes the efficiency and practicality of CarrDet for practical business scenarios.

Dietary nucleic acids (dietNAs) are being increasingly recognized as important food components with nutritional value. However, the precise dietary recommendations for dietNAs are limited, because established methods for determining the quantity and nutritional role of dietNAs are still lacking. One of the tools to narrow this gap could be microscopic imaging, as a convenient approach to visualize the abundance and distribution of dietNAs in food products. With the aid of appropriate bioinformatic elaboration, such images may in future enable the direct semiquantitative estimation of these macromolecules in food products. In the presented study, two methods of preparing microscopic sections and staining them with DNA-specific fluorochromes were used for microscopic imaging of dietNAs in food products of plant and animal origin. Procedures for preparing formalin-fixed paraffin-embedded sections and cryosections were compared in terms of their usefulness for routine food analysis. Both methods turned out equally suitable for visualizing dietNA distribution in animal and plant products. However, the use of cryosections allowed a significantly shorter analysis time and reduced the consumption of organic solvents. Both of these advantages make the cryosection method preferable while establishing a dedicated methodology for routine assessment of dietNAs in the food industry. [ABSTRACT FROM AUTHOR]

Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE)method. Identifying different patterns inmicroscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developedHVE algorithm can increase the system's performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%. [ABSTRACT FROM AUTHOR]

Chenodeoxycholic acid (CA) is a naturally occurring bile acid that is produced in the liver from cholesterol. Three CA complexes using Zn(II), Mg(II), and Ca(II) ions were synthesized to examine the chelation tendencies of CA towards these metal ions. The complexation reaction of CA with the metal ions under investigation was conducted with a 1:1 molar ratio (CA to metal) at 60–70 °C in neutralized media, which consisted of a binary solvent of MeOH and H2O (1:1). The resulting CA complexes were characterized using elemental data (metal, H, C, and Cl analysis) and spectral data (UV–visible, FT-IR, and 1H NMR). The results suggested that CA in anion form utilized oxygen atoms of the carboxylate group (-COO−) to capture Zn(II), Mg(II), and Ca(II) ions. This produced complexes with the general compositions of [Zn(CA)(H2O)Cl], [Mg2(CA)2(H2O)4Cl2], and [Ca2(CA)2(H2O)4Cl2]·2H2O, respectively. The Kirby–Bauer disc diffusion assay was then used to explore the bioactivity of the CA complexes toward three fungal species (Aspergillus niger, Candida albicans, and Penicillium sp.), three Gram-positive bacteria (Staphylococcus aureus, Streptococcus pneumoniae, and Bacillus subtilis), and two Gram-negative bacteria (Pseudomonas aeruginosa and Escherichia coli). The Ca(II) and Mg(II) complexes exhibited marked inhibitory effects on the cell growth of the fungal species Aspergillus niger with potency equal to 127 and 116% of the activity of the positive control, respectively. The Zn(II) and Ca(II) complexes strongly inhibited the growth of Penicillium sp., while the Zn(II) and Mg(II) complexes showed strong growth inhibition towards the Gram-negative species Pseudomonas aeruginosa. [ABSTRACT FROM AUTHOR]

The robust regulation of the cell cycle is critical for the survival and proliferation of bacteria. To gain a comprehensive understanding of the mechanisms regulating the bacterial cell cycle, it is essential to accurately quantify cell-cycle-related parameters and to uncover quantitative relationships. In this paper, we demonstrate that the quantification of cell size parameters using microscopic images can be influenced by software and by the parameter settings used. Remarkably, even if the consistent use of a particular software and specific parameter settings is maintained throughout a study, the type of software and the parameter settings can significantly impact the validation of quantitative relationships, such as the constant-initiation-mass hypothesis. Given these inherent characteristics of microscopic image-based quantification methods, it is recommended that conclusions be cross-validated using independent methods, especially when the conclusions are associated with cell size parameters that were obtained under different conditions. To this end, we presented a flexible workflow for simultaneously quantifying multiple bacterial cell-cycle-related parameters using microscope-independent methods. [ABSTRACT FROM AUTHOR]

Microscopic images in material science documents have increased in number due to the growth and common use of electron microscopy instruments. Through the use of data mining techniques, they are easily accessible and can be obtained from documents published online. As data-driven approaches are becoming increasingly common in the material science field, massively acquired experimental images through microscopy play important roles in terms of developing an artificial intelligence (AI) model for the purposes of automatically diagnosing crucial material structures. However, irrelevant objects (e.g., letters, scale bars, and arrows) that are often present inside original microscopic photos should be removed for the purposes of improving the AI models. To avoid the issue above, we applied four image inpainting algorithms (i.e., shift-net, global and local, contextual attention, and gated convolution) to a learning approach, with the aim of recovering microscopic images in journal papers. We estimated the structural similarity index measure (SSIM) and ℓ 1 / ℓ 2 errors, which are often used as measures of image quality. Lastly, we observed that gated convolution possessed the best performance for inpainting the microscopic images. [ABSTRACT FROM AUTHOR]

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields. [ABSTRACT FROM AUTHOR]

Embryo assessment and selection are usually based on the visual morphological analysis by expert embryologists. Although the embryologist assessment has been routinely used in clinical practice, it is highly dependent on the embryologist's experience and is very time‐consuming. Therefore, objective and efficient methods for automated embryo evaluation are in high demand. We proposed a framework of cascaded networks to hierarchically extract and integrate the microscopic image features for embryo classification. The cascaded networks consisted of a coarse network and a refined network. The coarse network produced a classification activation mapping (CAM) with the highest classification probability, which indicated the most discriminative regions of embryo classification. The refined network extracted and integrated the image features again by using both the CAMs and the corresponding original images. In addition, the residual external‐attention block (ResEA) was used in the refined network to better capture long‐range dependencies. Our cascaded networks were trained on a dataset of 7728 microscopic images of day 3 embryos from 1800 couples and evaluated on an independent testing dataset of 734 microscopic images. The accuracy, sensitivity, specificity, precision, and F1‐score were employed to evaluate the performance of our cascaded networks. Compared with the coarse network and the refined network, respectively, the cascaded networks without the ResEA improved the classification results of embryos. The ResEA block helped the cascaded networks to further improve all five metrics for better embryo classification. Our proposed cascaded networks also achieved better classification results than a junior embryologist did. The cascaded networks hierarchically make full use of image features for more effective learning, and the ResEA further improves the performance of embryo classification. [ABSTRACT FROM AUTHOR]

Most in situ tool wear monitoring methods during micro end milling rely on signals captured from the machining process to evaluate tool wear behavior; accurate positioning in the tool wear region and direct measurement of the level of wear are difficult to achieve. In this paper, an in situ monitoring system based on machine vision is designed and established to monitor tool wear behavior in micro end milling of titanium alloy Ti6Al4V. Meanwhile, types of tool wear zones during micro end milling are discussed and analyzed to obtain indicators for evaluating wear behavior. Aiming to measure such indicators, this study proposes image processing algorithms. Furthermore, the accuracy and reliability of these algorithms are verified by processing the template image of tool wear gathered during the experiment. Finally, a micro end milling experiment is performed with the verified micro end milling tool and the main wear type of the tool is understood via in-situ tool wear detection. Analyzing the measurement results of evaluation indicators of wear behavior shows the relationship between the level of wear and varying cutting time; it also gives the main influencing reasons that cause the change in each wear evaluation indicator. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of superficial fungal infections is still mostly based on direct microscopic examination with potassium hydroxide solution. However, this method can be time consuming, and its diagnostic accuracy rates vary widely depending on the clinician's experience. Objectives: This study presents a deep neural network structure that enables the rapid solutions for these problems and can perform automatic fungi detection in grayscale images without dyes. Methods: One hundred sixty microscopic full field photographs containing the fungal element, obtained from patients with onychomycosis, and 297 microscopic full field photographs containing dissolved keratin obtained from normal nails were collected. Smaller patches containing fungi (n = 1835) and keratin (n = 5238) were extracted from these full field images. In order to detect fungus and keratin, VGG16 and InceptionV3 models were developed by the use of these patches. The diagnostic performance of models was compared with 16 dermatologists by using 200 test patches. Results: For the VGG16 model, the InceptionV3 model and 16 dermatologists, mean accuracy rates were 88.10 ± 0.8%, 88.78 ± 0.35% and 74.53 ± 8.57%, respectively; mean sensitivity rates were 75.04 ± 2.73%, 74.93 ± 4.52% and 74.81 ± 19.51%, respectively; and mean specificity rates were 92.67 ± 1.17%, 93.78 ± 1.74% and 74.25 ± 18.03%, respectively. The models were statistically superior to dermatologists according to rates of accuracy and specificity but not to sensitivity (p <.0001, p <.005 and p >.05, respectively). Area under curve values of the VGG16 and InceptionV3 models were 0.9339 and 0.9292, respectively. Conclusion: Our research demonstrates that it is possible to build an automated system capable of detecting fungi present in microscopic images employing the proposed deep learning models. It has great potential for fungal detection applications based on AI. [ABSTRACT FROM AUTHOR]

Background and Aims: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and timeconsuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives: This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology: A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results: By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion: This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning. [ABSTRACT FROM AUTHOR]

The grain shape and orientation distribution of metal sheets at mesoscales are usually irregular, which has an impact on the elastic properties of metal materials. A grain shape function (GSF) is constructed to represent the shape of grains. The expansion coefficient of GSF on the basis of the Wigner D function is called the shape coefficient. In this paper, we study the influence of average grain shape on the elastic constitutive relation of orthogonal polycrystalline materials, and obtain a new expression of the elastic constitutive relation of polycrystalline materials containing grain shape effects. The seven string method is proposed to fit the shape of irregular grains. Experiments show that the GSF can better describe the shape of irregular grains. Using the microscopic images of the grains, we carried out the experimental measurement of micro and macrostrain at grain scale. The experimental results show that the grain shape parameter (slenderness ratio) is consistent with the theoretical results of the material macroscopic mechanical properties. [ABSTRACT FROM AUTHOR]

Dietary nucleic acids (dietNAs) are being increasingly recognized as important food components with nutritional value. However, the precise dietary recommendations for dietNAs are limited, because established methods for determining the quantity and nutritional role of dietNAs are still lacking. One of the tools to narrow this gap could be microscopic imaging, as a convenient approach to visualize the abundance and distribution of dietNAs in food products. With the aid of appropriate bioinformatic elaboration, such images may in future enable the direct semiquantitative estimation of these macromolecules in food products. In the presented study, two methods of preparing microscopic sections and staining them with DNA-specific fluorochromes were used for microscopic imaging of dietNAs in food products of plant and animal origin. Procedures for preparing formalin-fixed paraffin-embedded sections and cryosections were compared in terms of their usefulness for routine food analysis. Both methods turned out equally suitable for visualizing dietNA distribution in animal and plant products. However, the use of cryosections allowed a significantly shorter analysis time and reduced the consumption of organic solvents. Both of these advantages make the cryosection method preferable while establishing a dedicated methodology for routine assessment of dietNAs in the food industry.