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Malaria, a significant global health challenge, is caused by Plasmodium parasites. The Plasmodium liver stage plays a pivotal role in the establishment of the infection. This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis. Convolutional neural networks have been recently proposed as a viable option for tasks such as malaria detection, prediction of host-pathogen interactions, or drug discovery. Our research aimed to predict the transition of Plasmodium-infected liver cells to the merozoite stage, a key development phase, 15 hours in advance. We collected and analyzed hourly imaging data over a span of at least 38 hours from 400 sequences, encompassing 502 parasites. Our method was compared to human annotations to validate its efficacy. Performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were evaluated on an independent test dataset. The outcomes revealed an AUC of 0.873, a sensitivity of 84.6%, and a specificity of 83.3%, underscoring the potential of our CNN-based framework to predict liver stage development of P. berghei. These findings not only demonstrate the feasibility of our methodology but also could potentially contribute to the broader understanding of parasite biology.

Digital Pathology is a fast-growing field. It enables assessing disease grading, treatment progression, patient prognosis, etc. These analyses are carried out using whole slide tissue images (WSI). Processing of WSIs is costly due to their high resolutions (may reach over 100,000 × 100,000 pixels). Modern parallel machines offer adequate computing power to enable fast analyses of WSIs. However, WSI partitioning for parallel load-balanced execution can be hard. This occurs because different areas of a WSI can have distinct/irregular processing costs. We address this problem with a novel algorithm called Background Removal Bisection (BRB), which considers heterogeneity in data processing costs. BRB extends our previous cost-aware data partitioning solution (CADP). The contributions of BRB are: i) a strategy to generate irregular-shaped cost-balanced partitions; ii) effective hierarchical fine-grained background pruning; and, iii) a cost cache that reduces partitioning runtime, essential for large-scale executions. We deployed BRB into the Region Templates runtime and evaluated it with a cancer WSI analysis application. Compared to CADP, BRB achieved speedups of up to 2.72 × in hybrid CPU-GPU machines and up to 4.5 × for 32 compute nodes. [ABSTRACT FROM AUTHOR]

Label‐free biological cell imaging relies on rapid multimode phase imaging of biological samples in natural settings. To improve image contrast, phase is encoded into intensity information using the differential interference contrast (DIC) and Zernike phase contrast (ZPC) techniques. To enable multimode contrast‐enhanced observation of unstained specimens, this paper proposes an improved multimode phase imaging method based on the transport of intensity equation (TIE), which combines conventional microscopy with computational imaging. The ZPC imaging module based on adaptive aperture adjustment is applied when the quantitative phase results of biological samples have been obtained by solving the TIE. Simultaneously, a rotationally symmetric shear‐based technique is used that can yield isotropic DIC. In this paper, we describe numerical simulation and optical experiments carried out to validate the accuracy and viability of this technology. The calculated Michelson contrast of the ZPC image in the resolution plate experiment increased from 0.196 to 0.394. [ABSTRACT FROM AUTHOR]

[Display omitted] Cardiovascular diseases are the leading cause of death worldwide and include, among others, critical conditions of the aortic wall. Importantly, such critical conditions require effective diagnosis and treatment, which are not yet accurate enough. However, they could be significantly strengthened with predictive material models of the aortic wall. In particular, such predictive models could support surgical decisions, preoperative planning, and estimation of postoperative tissue remodeling. However, developing a predictive model requires experimental data showing both structural parameters and mechanical behavior. Such experimental data can be obtained using multimodal experiments. This review therefore discusses the current approaches to multimodal experiments. Importantly, the strength of the aortic wall is determined primarily by its passive components, i.e., mainly collagen, elastin, and proteoglycans. Therefore, this review focuses on multimodal experiments that relate the passive mechanical behavior of the human aortic wall to the structure and organization of its passive components. In particular, the multimodal experiments are classified according to the expected results. Multiple examples are provided for each experimental class and summarized with highlighted advantages and disadvantages of the method. Finally, future directions of multimodal experiments are envisioned and evaluated. Multimodal experiments are innovative approaches that have gained interest very quickly, but also recently. This review presents therefore a first clear summary of groundbreaking research in the field of multimodal experiments. The benefits and limitations of various types of multimodal experiments are thoroughly discussed, and a comprehensive overview of possible results is provided. Although this review focuses on multimodal experiments performed on human aortic tissues, the methods used and described are not limited to human aortic tissues but can be extended to other soft materials. [ABSTRACT FROM AUTHOR]

Background This article introduces an innovative classification methodology to identify nanowires within scanning electron microscope images. Methods Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. Results The method’s capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model’s proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Conclusions Such a high level of performance and accuracy of ML methods demonstrate the viability of our technique not only for academic but also for practical commercial implementation and usage.

Detection of transparent phase specimens especially biological cells with desired contrasts is of great importance in visual display and medical diagnosis. Due to the pure-phase nature, conventional detection approaches may damage samples or require complex operations. Computing liquid crystal (LC) is a thin and flat optical element with excellent capability in optical field modulation, which gives a feasible way to this issue from the perspective of analog optical computing. We here propose and experimentally implement an electrically switched two-dimensional (2D) differential microscopy based on computing LC platforms. The Pancharatnam–Berry phase LC polarization grating induces light’s spin separation to promote the 2D differential operation. Using the electrically tunable LC plate as the system phase retardance provider, the detecting mode can be flexibly switched from bright-field images to edge-enhanced images with desired contrasts. Remarkably, owing to the wavelength-independent feature closely related to the geometric phases, our proposed scheme is demonstrated to be applicable to the multi-wavelength microscopy imaging. These results open avenues to form real-time all-optical image processing and may facilitate multifunctional differential microscopy.

The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium the light passes while travelling from the object to the image plane. The computation of the point spread function (PSF) associated to the optical system is often used to assess the image quality. In a non-ideal optical system, the PSF is affected by aberrations that distort the final image. Moreover, in the presence of turbid media, the scattering phenomena spread the light at wide angular distributions that contribute to reduce contrast and sharpness. If the mathematical degradation operator affecting the recorded image is known, the image can be restored through deconvolution methods. In some scenarios, no (or partial) information on the PSF is available. In those cases, blind deconvolution approaches arise as useful solutions for image restoration. In this work, a new blind deconvolution method is proposed to restore images using spherical aberration (SA) and scatter-based kernel filters. The procedure was evaluated in different microscopy images. The results show the capability of the algorithm to detect both degradation coefficients (i.e., SA and scattering) and to restore images without information on the real PSF. [ABSTRACT FROM AUTHOR]

Conventional microscopy systems have limited depth of field, which often necessitates depth scanning techniques hindered by light scattering. Various techniques have been developed to address this challenge, but they have limited extended depth of field (EDOF) capabilities. To overcome this challenge, this study proposes an end-to-end optimization framework for building a computational EDOF microscope that combines a 4f microscopy optical setup incorporating learned optics at the Fourier plane and a post-processing deblurring neural network. Utilizing the end-to-end differentiable model, we present a systematic design methodology for computational EDOF microscopy based on the specific visualization requirements of the sample under examination. In particular, we demonstrate that the metasurface optics provides key advantages for extreme EDOF imaging conditions, where the extended DOF range is well beyond what is demonstrated in state of the art, achieving superior EDOF performance.

Detecting the wavefront of photons in an efficient and accurate way is crucial to retrieve the information of an optical field. Liquid crystal (LC) possesses strong ability to manipulate the phase and polarization of photons with high diffraction efficiency and therefore offers a promising avenue toward this aim. In this paper, LC performing analog optical processing is explored and a computing LC platform is proposed to implement the wavefront sensing via tuned bias retardation. It is demonstrated that precisely introducing a tiny Pancharatnam–Berry phase by LC can achieve flexible separation of two spin components with high resolution. Modulating the retardation by rotation of an analyzer, the computing LC platform is used to reconstruct wavefronts with phase‐contrast patterns. In particular, combining this scheme with microscopy can precisely retrieve a micro‐scale phase distribution. The result suggests that the computing LC platform can perform as a wavefront sensor with high efficiency and may open up new opportunities in the applications of precision metrology, real‐time analog optical processing, and high‐contrast microscopy. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium the light passes while travelling from the object to the image plane. The computation of the point spread function (PSF) associated to the optical system is often used to assess the image quality. In a non-ideal optical system, the PSF is affected by aberrations that distort the final image. Moreover, in the presence of turbid media, the scattering phenomena spread the light at wide angular distributions that contribute to reduce contrast and sharpness. If the mathematical degradation operator affecting the recorded image is known, the image can be restored through deconvolution methods. In some scenarios, no (or partial) information on the PSF is available. In those cases, blind deconvolution approaches arise as useful solutions for image restoration. In this work, a new blind deconvolution method is proposed to restore images using spherical aberration (SA) and scatter-based kernel filters. The procedure was evaluated in different microscopy images. The results show the capability of the algorithm to detect both degradation coefficients (i.e., SA and scattering) and to restore images without information on the real PSF.

Key message: ClearSee alpha and FAST9 were optimized for imaging Arabidopsis seeds up to the torpedo stages. The methods preserve the fluorescence of reporter proteins and seed shape, allowing phenotyping embryos in intact seeds. Tissue clearing methods eliminate the need for sectioning, thereby helping better understand the 3D organization of tissues and organs. In the past fifteen years, clearing methods have been developed to preserve endogenous fluorescent protein tags. Some of these methods (ClearSee, TDE, PEA-Clarity, etc.) were adapted to clear various plant species, with the focus on roots, leaves, shoot apical meristems, and floral parts. However, these methods have not been used in developing seeds beyond the early globular stage. Tissue clearing is problematic in post-globular seeds due to various apoplastic barriers and secondary metabolites. In this study, we compared six methods for their efficiency in clearing Arabidopsis thaliana seeds at post-globular embryonic stages. Three methods (TDE, ClearSee, and ClearSee alpha) have already been reported in plants, whereas the others (fsDISCO, FAST9, and CHAPS clear) are used in this context for the first time. These methods were assessed for seed morphological changes, clearing capacity, removal of tannins, and spectral properties. We tested each method in seeds from globular to mature stages. The pros and cons of each method are listed herein. ClearSee alpha appears to be the method of choice as it preserves seed morphology and prevents tannin oxidation. However, FAST9 with 60% iohexol as a mounting medium is faster, clears better, and appears suitable for embryonic shape imaging. Our results may guide plant researchers to choose a suitable method for imaging fluorescent protein-labeled embryos in intact Arabidopsis seeds. [ABSTRACT FROM AUTHOR]

In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from all disease classes for offline training. Class incremental learning offers a promising solution by adapting a deep network trained on specific disease classes to handle new diseases. However, catastrophic forgetting occurs, decreasing the performance of earlier classes when adapting the model to new data. Prior proposed methodologies to overcome this require perpetual storage of previous samples, posing potential practical concerns regarding privacy and storage regulations in healthcare. To this end, we propose a novel data-free class incremental learning framework that utilizes data synthesis on learned classes instead of data storage from previous classes. Our key contributions include acquiring synthetic data known as Continual Class-Specific Impression (CCSI) for previously inaccessible trained classes and presenting a methodology to effectively utilize this data for updating networks when introducing new classes. We obtain CCSI by employing data inversion over gradients of the trained classification model on previous classes starting from the mean image of each class inspired by common landmarks shared among medical images and utilizing continual normalization layers statistics as a regularizer in this pixel-wise optimization process. Subsequently, we update the network by combining the synthesized data with new class data and incorporate several losses, including an intra-domain contrastive loss to generalize the deep network trained on the synthesized data to real data, a margin loss to increase separation among previous classes and new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse effects of imbalanced distributions in training data. Extensive experiments show that the proposed framework achieves state-of-the-art performance on four of the public MedMNIST datasets and in-house echocardiography cine series, with an improvement in classification accuracy of up to 51% compared to baseline data-free methods. Our code is available at https://github.com/ubc-tea/Continual-Impression-CCSI. [Display omitted] • Propose a two-step pipeline for data-free class-incremental learning over medical images. • Investigate the effect of normalization layers in both synthesizing and training steps. • Synthesize high-quality and fidelity data as Continual Class-Specific Impression (CCSI) instead of inaccessible original data. • Propose three novel losses to increase the utility of synthesized data in the training step. • Perform qualitative and quantitative evaluation of our pipeline over five various medical imaging classification datasets. [ABSTRACT FROM AUTHOR]

Computing metasurfaces are two-dimensional artificial nanostructures capable of performing mathematical operations on the input electromagnetic field, including its amplitude, phase, polarization, and frequency distributions. Rapid progress in the development of computing metasurfaces provide exceptional abilities for all-optical image processing, including the edge-enhanced imaging, which opens a broad range of novel and superior applications for real-time pattern recognition. In this paper, we review recent progress in the emerging field of computing metasurfaces for all-optical image processing, focusing on innovative and promising applications in optical analog operations, image processing, microscopy imaging, and quantum imaging.

The analysis of high resolution whole slide tissue images is a computationally expensive task, which adversely impacts effective use of pathology imaging data in research. We propose runtime solutions to enable efficient execution of pathology image analysis applications on modern distributed memory hybrid platforms equipped with both CPUs and GPUs. Hybrid systems offer significant computation capacity, but taking advantage of this computing power is complex. An application developer may have to implement multiple versions of data processing codes targeted for different computing devices. The developer also has to tackle the challenges of efficiently distributing computational load among the nodes of a distributed memory machine and among computing devices on a node. This is particularly difficult in analysis of high resolution images because of irregular computing costs of processing different image regions. In order to address these problems, we have leveraged a high-level image processing language (Halide) and integrated it into our runtime system called Region Templates (RT). The language simplifies the application development while generating code for multiple devices, such as CPU and GPU. The integration with RT allows for efficient multiple node hybrid execution. We also developed a novel cost-aware data partitioning (CADP) strategy that considers the workload irregularity to minimize load imbalance. Our experimental evaluation shows significant performance improvements on hybrid CPU-GPU machines, as compared with using a single processor (CPU or GPU), as well as on multi-GPU systems. CADP resulted in 1.7× better performance than other workload partitioning approaches (e.g., KD-Trees) on a hybrid machine and was up to 2.24× faster in multi-node settings. • Pathology image analysis is computed intensive due to the high-resolution images. • We evaluate the use of Halide with Region Templates (RT) to speedup these analyses. • We support execution on machines with CPU-GPU and unified programing interface. • We propose a novel cost-aware data partitioning strategy targeting hybrid machines. • Our solutions attained better performance than competitor partitioning strategies. [ABSTRACT FROM AUTHOR]

The physical process underlying microscopy imaging suffers from several issues: some of them include the blurring effect due to the Point Spread Function, the presence of Gaussian or Poisson noise, or even a mixture of these two types of perturbation. Among them, auto–fluorescence presents other artifacts in the registered image, and such fluorescence may be an important obstacle in correctly recognizing objects and organisms in the image. For example, particle tracking may suffer from the presence of this kind of perturbation. The objective of this work is to employ Deep Learning techniques, in the form of U-Nets like architectures, for background emission removal. Such fluorescence is modeled by Perlin noise, which reveals to be a suitable candidate for simulating such a phenomenon. The proposed architecture succeeds in removing the fluorescence, and at the same time, it acts as a denoiser for both Gaussian and Poisson noise. The performance of this approach is furthermore assessed on actual microscopy images and by employing the restored images for particle recognition. [ABSTRACT FROM AUTHOR]

The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system's interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive. [ABSTRACT FROM AUTHOR]

Optical microscopy has become an invaluable tool for investigating complex samples. Over the years, many advances to optical microscopes have been made that have allowed us to uncover new insights into the samples studied. Dynamic changes in biological and chemical systems are of utmost importance to study. To probe these samples, multidimensional approaches have been developed to acquire a fuller understanding of the system of interest. These dimensions include the spatial information, such as the three-dimensional coordinates and orientation of the optical probes, and additional chemical and physical properties through combining microscopy with various spectroscopic techniques. In this review, we survey the field of multidimensional microscopy and provide an outlook on the field and challenges that may arise. [ABSTRACT FROM AUTHOR]

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

Background: This article introduces an innovative classification methodology to identify nanowires within scanning electron microscope images., Methods: Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays., Results: The method's capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model's proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall., Conclusions: Such a high level of performance and accuracy of ML methods demonstrate the viability of our technique not only for academic but also for practical commercial implementation and usage., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Brugnolotto E et al.)

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks. [ABSTRACT FROM AUTHOR]

The physical process underlying microscopy imaging suffers from several issues: some of them include the blurring effect due to the Point Spread Function, the presence of Gaussian or Poisson noise, or even a mixture of these two types of perturbation. Among them, auto–fluorescence presents other artifacts in the registered image, and such fluorescence may be an important obstacle in correctly recognizing objects and organisms in the image. For example, particle tracking may suffer from the presence of this kind of perturbation. The objective of this work is to employ Deep Learning techniques, in the form of U-Nets like architectures, for background emission removal. Such fluorescence is modeled by Perlin noise, which reveals to be a suitable candidate for simulating such a phenomenon. The proposed architecture succeeds in removing the fluorescence, and at the same time, it acts as a denoiser for both Gaussian and Poisson noise. The performance of this approach is furthermore assessed on actual microscopy images and by employing the restored images for particle recognition.

The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system’s interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive.

Since the advent of microscopy imaging and flow cytometry, there has been an explosion in the number of probes, consisting of a component binding to an analyte and a detectable tag, to mark areas of interest in or on cells and tissue. Probe tags have been created to detect and/or visualize probes. Over time, these probe tags have increased in number. The expansion has resulted in arbitrarily created synonyms of probe tags used in publications and software. The synonyms are problematic for readability of publications, accuracy of text/data mining, and bridging data from multiple platforms, protocols, and databases for Big Data analysis. Development and implementation of a universal language for probe tags will ensure equivalent quality and level of data being reported or extracted for clinical/scientific evaluation as well as help connect data from many platforms. The International Society for Advancement of Cytometry Data Standards Task Force composed of academic scientists and industry hardware/software/reagent manufactures have developed recommendations for a standardized nomenclature for probe tags used in cytometry and microscopy imaging. These recommendations are shared in this technical note in the form of a Probe Tag Dictionary. © 2020 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Malaria, a significant global health challenge, is caused by Plasmodium parasites. The Plasmodium liver stage plays a pivotal role in the establishment of the infection. This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis. Convolutional neural networks have been recently proposed as a viable option for tasks such as malaria detection, prediction of host-pathogen interactions, or drug discovery. Our research aimed to predict the transition of Plasmodium-infected liver cells to the merozoite stage, a key development phase, 15 hours in advance. We collected and analyzed hourly imaging data over a span of at least 38 hours from 400 sequences, encompassing 502 parasites. Our method was compared to human annotations to validate its efficacy. Performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were evaluated on an independent test dataset. The outcomes revealed an AUC of 0.873, a sensitivity of 84.6%, and a specificity of 83.3%, underscoring the potential of our CNN-based framework to predict liver stage development of P. berghei . These findings not only demonstrate the feasibility of our methodology but also could potentially contribute to the broader understanding of parasite biology., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Main conclusion: A histological study of Rafflesia patma revealed the simplicity of a flower’s vascular tissue and epidermal features of flower organs, including their structures and pigmentation. Rafflesia is an endophytic holoparasitic plant that infects Tetrastigma. In a previous study, we characterized the shape of the strands of an endophyte (Rafflesia patma Blume) and hypothesized their distribution. In this study, we deepened our analysis by assessing parts of flower tissue sampled during anthesis, performed surface casting of the abaxial and adaxial sides of the perigone lobe to profile their surface features, and histologically characterized the perigone lobe, perigone tube, and central column base, including the anther and cupula region. The objective of these observations was to compare tissues from different organs and the distribution of cells staining positive for tannin, suberin, and lignin. Observable features in this study were vascular and epidermal tissue. We also observed reduced vascular tissue with xylem and vascular parenchyma in multiple organs. The adaxial epidermis found in the perigone lobes and tube had papillate cells, and their function might be to assist with the emission of odor through chemical evaporation. The abaxial epidermis, also found in perigone lobes and tube, had flattened cells. These, combined with the nearby flattened parenchyma cells, especially in the outermost, early perigone lobe, might provide a tougher (stiffer) outer protective barrier for the flower. The accumulation of tannin in perigone lobes might offer protection to the flower from herbivores prior to anthesis. Although a previous observation indicated the possibility of stomata on the surface of Rafflesia flowers, no stomata were found in this study. [ABSTRACT FROM AUTHOR]

Viewing a particular region of an object with high magnification is essentially significant in both fundamental research and practical applications, however, imaging the sample from multi-direction is still very challenging for current microscopes. Herein, a centered miniature rotation robot under microscopy is presented to achieve multi-directional imaging of small objects. First, the miniature rotatable robot is constructed by three nano motors, including one rotational and two linear-motors. Then, a new centering strategy of neighborhood-window difference is proposed to center the micro/nano sample onto the rotation axis of the robot (error less than 20 pixels). In experiments, the sample is tested to be imaged from multi-direction (360°) by rotating the robot continuously. To highlight the versatility of this system, we demonstrate the multi-directional imaging under both optical microscope (OM) and scanning electron microscope (SEM). Compared with current techniques, this system is able to provide richer morphology information at small scale benefiting from the multi-directional imaging, which is expected to have long-term impacts on microscopy observation, micro defect inspection, nanomaterial characterization and other related fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose and experimentally demonstrate a three-dimensional (3D) microscopic system that reconstructs a 3D image based on structured light illumination. The spatial pattern of the structured light changes according to the profile of the object, and by measuring the change, a 3D image of the object is reconstructed. The structured light is generated with a digital micro-mirror device (DMD), which controls the structured light pattern to change in a kHz rate and enables the system to record the 3D information in real time. The working distance of the imaging system is 9 cm at a resolution of 20 μm. The resolution, working distance, and real-time 3D imaging enable the system to be applied in bridge and road crack examinations, and structure fault detection of transportation infrastructures.

Cellular barriers, such as the skin, the lung epithelium or the intestinal epithelium, constitute one of the first obstacles facing nanomedicines or other nanoparticles entering organisms. It is thus important to assess the capacity of nanoparticles to enter and transport across such barriers. In this work, Caco-2 intestinal epithelial cells were used as a well-established model for the intestinal barrier, and the uptake, trafficking and translocation of model silica nanoparticles of different sizes were investigated using a combination of imaging, flow cytometry and transport studies. Compared to typical observations in standard cell lines commonly used for in vitro studies, silica nanoparticle uptake into well-developed Caco-2 cellular barriers was found to be very low. Instead, nanoparticle association to the apical outer membrane was substantial and these particles could easily be misinterpreted as internalised in the absence of imaging. Passage of nanoparticles through the barrier was very limited, suggesting that the low amount of internalised nanoparticles was due to reduced uptake into cells, rather than a considerable transport through them.

Bioimage classification is increasingly becoming more important in many biological studies including those that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new General Purpose (GenP) bioimage classification method that can be applied to a large range of classification problems. The GenP system we propose is an ensemble that combines multiple texture features (both handcrafted and learned descriptors) for superior and generalizable discriminative power. Our ensemble obtains a boosting of performance by combining local features, dense sampling features, and deep learning features. Each descriptor is used to train a different Support Vector Machine that is then combined by sum rule. We evaluate our method on a diverse set of bioimage classification tasks each represented by a benchmark database, including some of those available in the IICBU 2008 database. Each bioimage classification task represents a typical subcellular, cellular, and tissue level classification problem. Our evaluation on these datasets demonstrates that the proposed GenP bioimage ensemble obtains state-of-the-art performance without any ad-hoc dataset tuning of the parameters (thereby avoiding any risk of overfitting/overtraining). To reproduce the experiments reported in this paper, the MATLAB code of all the descriptors is available at https://github.com/LorisNanni and https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0. [ABSTRACT FROM AUTHOR]

Two novel D-π-A-π-D structural 2,2 ′ -bipyridine derivatives based on different electron donors, namely aza-crown ether and triphenylamine groups were synthesized and characterized. One of which was characterized by single crystal X-ray diffraction determination. Their linear photophysical properties were systematically investigated in various solvents. The two-photon excited fluorescence were investigated. The two dyes both exhibited excellent one- and two-photon excited fluorescence as well as high quantum yield and large two-photon absorption cross-section ( δ ). Fluorescence microscopy imaging of them in NIH/3T3 cell line was studied. The result indicated that both of them exhibit excellent biological compatibility. [ABSTRACT FROM AUTHOR]

The flagellar swimming of euglenids, which are propelled by a single anterior flagellum, is characterized by a generalized helical motion. The 3D nature of this swimming motion, which lacks some of the symmetries enjoyed by more common model systems, and the complex flagellar beating shapes that power it make its quantitative description challenging. In this work, we provide a quantitative, 3D, highly resolved reconstruction of the swimming trajectories and flagellar shapes of specimens of Euglena gracilis. We achieved this task by using high-speed 2D image recordings taken with a conventional inverted microscope combined with a precise characterization of the helical motion of the cell body to lift the 2D data to 3D trajectories. The propulsion mechanism is discussed. Our results constitute a basis for future biophysical research on a relatively unexplored type of eukaryotic flagellar movement. [ABSTRACT FROM AUTHOR]

This paper presents a multi-classifier approach for classifying microfilariae in 2-D images. A shape descriptor based on the quench function is described. This descriptor is represented as a feature vector that encodes the shape information. The color feature vector is calculated as a histogram. Two classifiers were used to train both color and shape feature vectors, one for each vector. The posterior probabilities calculated from the scores of each classifier are then used to calculate the final classification decision. The experimental results show that, although the proposed approach is simple, it is efficient when compared to various approaches.

The present work aimed to explain the differences in oral performance in fasted humans who were categorized into groups based on the three different drug product formulations of dexketoprofen trometamol (DKT) salt—Using a combination of in vitro techniques and pharmacokinetic analysis. The non-bioequivalence (non-BE) tablet group achieved higher plasma Cmax and area under the curve (AUC) than the reference and BE tablets groups, with only one difference in tablet composition, which was the presence of calcium monohydrogen phosphate, an alkalinizing excipient, in the tablet core of the non-BE formulation. Concentration profiles determined using a gastrointestinal simulator (GIS) apparatus designed with 0.01 N hydrochloric acid and 34 mM sodium chloride as the gastric medium and fasted state simulated intestinal fluids (FaSSIF-v1) as the intestinal medium showed a faster rate and a higher extent of dissolution of the non-BE product compared to the BE and reference products. These in vitro profiles mirrored the fraction doses absorbed in vivo obtained from deconvoluted plasma concentration–time profiles. However, when sodium chloride was not included in the gastric medium and phosphate buffer without bile salts and phospholipids were used as the intestinal medium, the three products exhibited nearly identical concentration profiles. Microscopic examination of DKT salt dissolution in the gastric medium containing sodium chloride identified that when calcium phosphate was present, the DKT dissolved without conversion to the less soluble free acid, which was consistent with the higher drug exposure of the non-BE formulation. In the absence of calcium phosphate, however, dexketoprofen trometamol salt dissolution began with a nano-phase formation that grew to a liquid–liquid phase separation (LLPS) and formed the less soluble free acid crystals. This phenomenon was dependent on the salt/excipient concentrations and the presence of free acid crystals in the salt phase. This work demonstrated the importance of excipients and purity of salt phase on the evolution and rate of salt disproportionation pathways. Moreover, the presented data clearly showed the usefulness of the GIS apparatus as a discriminating tool that could highlight the differences in formulation behavior when utilizing physiologically-relevant media and experimental conditions in combination with microscopy imaging.

Silica sand is a major mineral commodity for foundries, specifically for sand casting. Suppliers of foundry sand located near consumers would keep costs low production. Gray cast iron in Mexico demands large amounts of silica sand for the manufacture of core molds, currently supplied by the USA. This sand must meet industrial specifications for core sand to reach adequate strength, such as grain shape, grain size distribution, pH and ADV. In the last years, a high demand for silica sand for fracking in the USA has obligated Mexican foundries to search for new sources of this commodity. In this research, seven samples of silica sand from different areas Northeast and Southeast in Mexico were analyzed by microscopy imaging using metallography microscopy and SEM, to compare with core commercial sands. Also, semi-qualitative chemical composition by energy dispersive X-ray spectroscopy was analyzed. Surprisingly, results showed some silica sands from Mexico have textural surface and chemical composition similar to samples from the USA. A source of silica sand located in Mexico could be developed to meet local demand. [ABSTRACT FROM AUTHOR]

• Microscopic spectroscopy imaging is explored for needle reflectance measurement. • This method measures bidirectional reflectance and is easy to be implemented. • Needle azimuth and specular reflection affect correlations to chlorophyll content. • The method can eliminate specular reflection from reflectance of conifer needles. Leaf reflectance is widely used to retrieve leaf chlorophyll content (Cab) and parameterize canopy radiative transfer models. Measurements of broadleaf reflectance are typically made by using integrating sphere devices, but the approach is generally limited in conifer needle measurements due to the narrow needle coverage relative to the sample port of the integrating sphere. In this study, we proposed a method to measure the bidirectional reflectance factor (BRF) of needles by integrating a hyperspectral imaging spectrometer and an optical microscope. Pure needle pixels can be easily extracted from hyperspectral images after microscope magnification. The method was first validated by the narrow strips of broad leaves as proxies of needles, resulting in the difference between the measured BRF of strips and the BRF of whole broad leaves in the visible bands (400–700 nm) and in the near-infrared bands (700–1000 nm) being 0.0043 and 0.019, respectively. The method was also indirectly verified by tracking the variation in the natural needle BRF with its Cab, resulting in the change in BRF being consistent with the change in Cab. In addition, linear relationships between the needle Cab and the classic vegetation indices (i.e. , the red-edge simple ratio and normalized difference vegetation index) calculated from the BRF were found. Apart from the above, we found that (1) the BRF of the abaxial side has a discrepancy with that of the adaxial side of needles, (2) the BRF of a needle varies with its azimuth, hence affecting the correlation between vegetation indices and Cab, and (3) filtering the specular reflection on the microsurfaces of the needle could enhance the correlation between the vegetation indices and needle Cab. The method is expected to accurately measure the BRF of needles and hence to improve the applications of needle optical properties in plant sciences, ecology, and remote sensing fields. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference. Results: We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis. Conclusions: We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks. [ABSTRACT FROM AUTHOR]

We present a hyper-spectral microscopic imaging system capable of obtaining both transmission- and fluorescence-spectral images for biological measurements. For obtaining high quality images of samples with low self-fluorescence, large aperture optics and low vibration elements were implemented in the system. This study focused on obtaining different types of spectral information, i.e., transmission and fluorescence, to study the structure of the objects and observe the similarities and differences in composition at different positions. By employing this system to investigate bee pollen, we observed a special thin-film structure, which cannot be detected using conventional microscopic systems. [ABSTRACT FROM AUTHOR]

Leishmaniasis is a vector-borne disease for which only limited therapeutic options are available. The disease is ranked among the six most important tropical infectious diseases and represents the second-largest parasitic killer in the world. The development of new therapies has been hampered by the lack of technologies and methodologies that can be integrated into the complex physiological environment of a cell or organism and adapted to suitable in vitro and in vivo Leishmania models. Recent advances in microscopy imaging offer the possibility to assess the efficacy of potential drug candidates against Leishmania within host cells. This technology allows the simultaneous visualization of relevant phenotypes in parasite and host cells and the quantification of a variety of cellular events. In this review, we present the powerful cellular imaging methodologies that have been developed for drug screening in a biologically relevant context, addressing both high-content and high-throughput needs. Furthermore, we discuss the potential of intra-vital microscopy imaging in the context of the anti-leishmanial drug discovery process. [ABSTRACT FROM AUTHOR]

Background: Ultrasound is considered a reliable, widely available, non-invasive, and inexpensive imaging technique for assessing and detecting the development phases of cancer; both in vivo and ex vivo, and for understanding the effects on cell cycle and viability after ultrasound treatment. Methods: Based on the topological continuity characteristics, and that adjacent points or areas represent similar features, we propose a topological penalized convex objective function of sparse coding, to recognize similar cell phases. Results: This method introduces new features using a deep learning method of sparse coding with topological continuity characteristics. Large-scale comparison tests demonstrate that the RAW can outperform SIFT GIST and HoG as the input features with this method, achieving higher sensitivity, specificity, F1 score, and accuracy. Conclusions: Experimental results show that the proposed topological sparse coding technique is valid and effective for extracting new features, and the proposed system was effective for cell recognition of microscopy images of the MDA-MB-231 cell line. This method allows features from sparse coding learning methods to have topological continuity characteristics, and the RAW features are more applicable for the deep learning of the topological sparse coding method than SIFT GIST and HoG. [ABSTRACT FROM AUTHOR]

Mitochondrial fission is important for organelle transport, quality control and apoptosis. Changes to the fission process can result in a wide variety of neurological diseases. In mammals, mitochondrial fission is executed by the GTPase dynamin-related protein 1 (Drp1; encoded by DNM1L), which oligomerizes around mitochondria and constricts the organelle. The mitochondrial outer membrane proteins Mff, MiD49 (encoded by MIEF2) and MiD51 (encoded by MIEF1) are involved in mitochondrial fission by recruiting Drp1 from the cytosol to the organelle surface. In addition, endoplasmic reticulum (ER) tubules have been shown to wrap around and constrict mitochondria before a fission event. Up to now, the presence of MiD49 and MiD51 at ER-mitochondrial division foci has not been established. Here, we combine confocal live-cell imaging with correlative cryogenic fluorescence microscopy and soft x-ray tomography to link MiD49 and MiD51 to the involvement of the ER in mitochondrial fission. We gain further insight into this complex process and characterize the 3D structure of ER-mitochondria contact sites. [ABSTRACT FROM AUTHOR]

We introduce a region template abstraction and framework for the efficient storage, management and processing of common data types in analysis of large datasets of high resolution images on clusters of hybrid computing nodes. The region template abstraction provides a generic container template for common data structures, such as points, arrays, regions, and object sets, within a spatial and temporal bounding box. It allows for different data management strategies and I/O implementations, while providing a homogeneous, unified interface to applications for data storage and retrieval. A region template application is represented as a hierarchical dataflow in which each computing stage may be represented as another dataflow of finer-grain tasks. The execution of the application is coordinated by a runtime system that implements optimizations for hybrid machines, including performance-aware scheduling for maximizing the utilization of computing devices and techniques to reduce the impact of data transfers between CPUs and GPUs. An experimental evaluation on a state-of-the-art hybrid cluster using a microscopy imaging application shows that the abstraction adds negligible overhead (about 3%) and achieves good scalability and high data transfer rates. Optimizations in a high speed disk based storage implementation of the abstraction to support asynchronous data transfers and computation result in an application performance gain of about 1.13 × . Finally, a processing rate of 11,730 4K × 4K tiles per minute was achieved for the microscopy imaging application on a cluster with 100 nodes (300 GPUs and 1200 CPU cores). This computation rate enables studies with very large datasets. [ABSTRACT FROM AUTHOR]

Tissue optical clearing technique provides a prospective solution for the application of advanced optical methods in life sciences. This paper gives a review of recent developments in tissue optical clearing techniques. The physical, molecular and physiological mechanisms of tissue optical clearing are overviewed and discussed. Various methods for enhancing penetration of optical-clearing agents into tissue, such as physical methods, chemical-penetration enhancers and combination of physical and chemical methods are introduced. Combining the tissue optical clearing technique with advanced microscopy image or labeling technique, applications for 3D microstructure of whole tissues such as brain and central nervous system with unprecedented resolution are demonstrated. Moreover, the difference in diffusion and/or clearing ability of selected agents in healthy versus pathological tissues can provide a highly sensitive indicator of the tissue health/pathology condition. Finally, recent advances in optical clearing of soft or hard tissue for in vivo imaging and phototherapy are introduced. [ABSTRACT FROM AUTHOR]