IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine

AI-empowered visualization of nucleic acid testing

AIMS

The visualization of nucleic acid testing (NAT) results plays a critical role in diagnosing and monitoring infectious and genetic diseases. The review aims to review the current status of AI-based NAT result visualization. It systematically introduces commonly used AI-based methods and techniques for NAT, emphasizing the importance of result visualization for accessible, clear, and rapid interpretation. This highlights the importance of developing a NAT visualization platform that is user-friendly and efficient, setting a clear direction for future advancements in making nucleic acid testing more accessible and effective for everyday applications.

METHOD

This review explores both the commonly used NAT methods and AI-based techniques for NAT result visualization. The focus then shifts to AI-based methodologies, such as color detection and result interpretation through AI algorithms. The article presents the advantages and disadvantages of these techniques, while also comparing the performance of various NAT platforms in different experimental contexts. Furthermore, it explores the role of AI in enhancing the accuracy, speed, and user accessibility of NAT results, highlighting visualization technologies adapted from other fields of experimentation.

SIGNIFICANCE

This review offers valuable insights for researchers and everyday users, aiming to develop effective visualization platforms for NAT, ultimately enhancing disease diagnosis and monitoring.

Non-targeted metabolomics and explainable artificial intelligence: Effects of processing and color on coniferyl aldehyde levels in Eucommiae cortex

Eucommia ulmoides, a plant native to China, is valued for its medicinal properties and has applications in food, health products, and traditional Chinese medicine. Processed Eucommiae Cortex (EC) has historically been a highly valued medicine. Ancient doctors had ample experience processing EC, especially with ginger juice, as documented in traditional Chinese medical texts. The combination of EC and ginger juice helps release and transform the active ingredients, strengthening the medicine's effectiveness and improving its taste and shelf life. However, the lack of quality control standards for Ginger-Eucommiae Cortex (G-EC), processed from EC and ginger, presents challenges for its industrial and clinical use. This study optimized G-EC processing using the CRITIC and Box-Behnken methods. Metabolomics showed 517 chemical changes between raw and processed G-EC, particularly an increase in coniferyl aldehyde (CFA). Explainable artificial intelligence techniques revealed the feasibility of using color to CFA content, providing insights into quality indicators.

SciJava Ops: an improved algorithms framework for Fiji and beyond

Decades of iteration on scientific imaging hardware and software has yielded an explosion in not only the size, complexity, and heterogeneity of image datasets but also in the tooling used to analyze this data. This wealth of image analysis tools, spanning different programming languages, frameworks, and data structures, is itself a problem for data analysts who must adapt to new technologies and integrate established routines to solve increasingly complex problems. While many "bridge" layers exist to unify pairs of popular tools, there exists a need for a general solution to unify new and existing toolkits. The SciJava Ops library presented here addresses this need through two novel principles. Algorithm implementations are declared as plugins called Ops, providing a uniform interface regardless of the toolkit they came from. Users express their needs declaratively to the Op environment, which can then find and adapt available Ops on demand. By using these principles instead of direct function calls, users can write streamlined workflows while avoiding the translation boilerplate of bridge layers. Developers can easily extend SciJava Ops to introduce new libraries and more efficient, specialized algorithm implementations, even immediately benefitting existing workflows. We provide several use cases showing both user and developer benefits, as well as benchmarking data to quantify the negligible impact on overall analysis performance. We have initially deployed SciJava Ops on the Fiji platform, however it would be suitable for integration with additional analysis platforms in the future.

Measurement of protein concentration in bacteria and small organelles under a light transmission microscope

Protein concentration (PC) is an essential characteristic of cells and organelles; it determines the extent of macromolecular crowding effects and serves as a sensitive indicator of cellular health. A simple and direct way to quantify PC is provided by brightfield-based transport-of-intensity equation (TIE) imaging combined with volume measurements. However, since TIE is based on geometric optics, its applicability to micrometer-sized particles is not clear. Here, we show that TIE can be used on particles with sizes comparable to the wavelength. At the same time, we introduce a new ImageJ plugin that allows TIE image processing without resorting to advanced mathematical programs. To convert TIE data to PC, knowledge of particle volumes is essential. The volumes of bacteria or other isolated particles can be measured by displacement of an external absorbing dye ("transmission-through-dye" or TTD microscopy), and for spherical intracellular particles, volumes can be estimated from their diameters. We illustrate the use of TIE on Escherichia coli, mammalian nucleoli, and nucleolar fibrillar centers. The method is easy to use and achieves high spatial resolution.

Cell-type-specific loops linked to RNA polymerase II elongation in human neural differentiation

DNA is folded into higher-order structures that shape and are shaped by genome function. The role of long-range loops in the establishment of new gene expression patterns during cell fate transitions remains poorly understood. Here, we investigate the link between cell-specific loops and RNA polymerase II (RNA Pol II) during neural lineage commitment. We find thousands of loops decommissioned or gained de novo upon differentiation of human induced pluripotent stem cells (hiPSCs) to neural progenitor cells (NPCs) and post-mitotic neurons. During hiPSC-to-NPC and NPC-to-neuron transitions, genes changing from RNA Pol II initiation to elongation are >4-fold more likely to anchor cell-specific loops than repressed genes. Elongated genes exhibit significant mRNA upregulation when connected in cell-specific promoter-enhancer loops but not invariant promoter-enhancer loops or promoter-promoter loops or when unlooped. Genes transitioning from repression to RNA Pol II initiation exhibit a slight mRNA increase independent of loop status. Our data link cell-specific loops and robust RNA Pol II-mediated elongation during neural cell fate transitions.

Virtual Orientation Tools (VOTj): Fiji plugins for object centering and alignment

Standardizing image datasets is essential for facilitating overall visual comparisons and enhancing compatibility with image-processing workflows. One way to achieve homogeneity for images containing a single object is to align the object to a common orientation. Here, we propose the Virtual Orientation Tools (VOTj): a set of Fiji plugins to center and align an object of interest in images to a vertical or horizontal orientation. To process an image, the plugin requires either a mask outlining the object or a rough annotation of the object directly drawn by the user in the image. The current object orientation is retrieved using Principal Component Analysis (PCA), from which the optimal alignment is derived. The plugins support multi-dimensional images to allow, e.g., aligning individual time points of a time-lapse. The tools can be used for a variety of samples and imaging modalities. Besides, the plugins enable the interactive alignment of a list of images from a directory for batch execution and can be included in custom image-processing workflows using macro-recording.

Circulating Tumor Cell Identification Based on Deep Learning

As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low cost and repeatability, CTC test has an important reference value in determining clinical results and studying the mechanism of drug resistance. However, the determination of CTC usually requires a big effort from pathologist and is also error-prone due to inexperience and fatigue. In this study, we developed a novel convolutional neural network (CNN) method to automatically detect CTCs in patients’ peripheral blood based on immunofluorescence in situ hybridization (imFISH) images. We collected the peripheral blood of 776 patients from Chifeng Municipal Hospital in China, and then used Cyttel to delete leukocytes and enrich CTCs. CTCs were identified by imFISH with CD45+, DAPI+ immunofluorescence staining and chromosome 8 centromeric probe (CEP8+). The sensitivity and specificity based on traditional CNN prediction were 95.3% and 91.7% respectively, and the sensitivity and specificity based on transfer learning were 97.2% and 94.0% respectively. The traditional CNN model and transfer learning method introduced in this paper can detect CTCs with high sensitivity, which has a certain clinical reference value for judging prognosis and diagnosing metastasis.

An OpenCV-Based Approach for Automated Cardiac Rhythm Measurement in Zebrafish from Video Datasets

Cardiac arrhythmia has been defined as one of the abnormal heart rhythm symptoms, which is a common problem dealt with by cardiologists. Zebrafish were established as a powerful animal model with a transparent body that enables optical observation to analyze cardiac morphology and cardiac rhythm regularity. Currently, research has observed heart-related parameters in zebrafish, which used different approaches, such as starting from the use of fluorescent transgenic zebrafish, different software, and different observation methods. In this study, we developed an innovative approach by using the OpenCV library to measure zebrafish larvae heart rate and rhythm. The program is designed in Python, with the feature of multiprocessing for simultaneous region-of-interest (ROI) detection, covering both the atrium and ventricle regions in the video, and was designed to be simple and user-friendly, having utility even for users who are unfamiliar with Python. Results were validated with our previously published method using ImageJ, which observes pixel changes. In summary, the results showed good consistency in heart rate-related parameters. In addition, the established method in this study also can be widely applied to other invertebrates (like Daphnia) for cardiac rhythm measurement.

Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology

The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

Establishment of a comprehensive analysis method for the microfaunal movement in activated sludge

Microfaunal identification and analysis are very complex; thus, an image analysis method was utilized in this paper to overcome the shortcomings of using the number, dominant species, and diversity of population structure of microfauna as activated sludge indicators. Based on a classification of microfaunal movement, the quantitative processing and analysis of the micro-video frame image of microfaunal movement were carried out by using the Image J software. Background subtraction method was utilized to detect target microfauna by matching target area features to track microfaunal movement. Three parameters, namely, motion trajectory (L), consecutive frame of motion paths (S_i), and average change rate of extent [Formula: see text], were selected to represent the motion trajectory and mass center of microfauna. Four motion-velocity parameters, namely, the left and right rotation angles of adjacent frames (∆α_i), instantaneous velocity (V_i), average linear velocity ([Formula: see text]), and average angular velocity ([Formula: see text]), were selected to characterize the movement modes of microfauna. Finally, a motion analysis method based on the Image J software was established to demonstrate the different motion modes of microfauna in activated sludge. This study provides a methodological foundation for the establishment of a new method of microfauna as indicator. Based on this method, the correlation between the microfaunal motion velocity and activated sludge flocs was analyzed.

The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.

Automated quantification of vomeronasal glomeruli number, size, and color composition after immunofluorescent staining

Glomeruli are neuropil-rich regions of the main or accessory olfactory bulbs (AOB) where the axons of olfactory or vomeronasal neurons and dendrites of mitral/tufted cells form synaptic connections. In the main olfactory system, olfactory sensory neurons (OSNs) expressing the same receptor innervate 1 or 2 glomeruli. However, in the accessory olfactory system, vomeronasal sensory neurons (VSNs) expressing the same receptor can innervate up to 30 different glomeruli in the AOB. Genetic mutation disrupting genes with a role in defining the identity/diversity of olfactory and vomeronasal neurons can alter the number and size of glomeruli. Interestingly, 2 cell surface molecules, Kirrel2 and Kirrel3, have been indicated as playing a critical role in the organization of axons into glomeruli in the AOB. Being able to quantify differences in glomeruli features, such as number, size, or immunoreactivity for specific markers, is an important experimental approach to validate the role of specific genes in controlling neuronal connectivity and circuit formation in either control or mutant animals. Since the manual recognition and quantification of glomeruli on digital images is a challenging and time-consuming task, we generated a program in Python able to identify glomeruli in digital images and quantify their properties, such as size, number, and pixel intensity. Validation of our program indicates that our script is a fast and suitable tool for high-throughput quantification of glomerular features of mouse lines with different genetic makeup.

A New Method for CTC Images Recognition Based on Machine Learning

Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python’s openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.

Correlation between clinical risk factors and tracheal intubation difficulty in infants with Pierre-Robin syndrome: a retrospective study

BACKGROUND

Difficult tracheal intubation is a common problem encountered by anesthesiologists in the clinic. This study was conducted to assess the difficulty of tracheal intubation in infants with Pierre Robin syndrome (PRS) by incorporating computed tomography (CT) to guide airway management for anesthesia.

METHODS

In this retrospective study, we analyzed case-level clinical data and CT images of 96 infants with PRS. First, a clinically experienced physician labeled CT images, after which the color space conversion, binarization, contour acquisition, and area calculation processing were performed on the annotated files. Finally, the correlation coefficient between the seven clinical factors and tracheal intubation difficulty, as well as the differences in each risk factor under tracheal intubation difficulty were calculated.

RESULTS

The absolute value of the correlation coefficient between the throat area and tracheal intubation difficulty was 0.54; the observed difference was statistically significant. Body surface area, weight, and gender also showed significant difference under tracheal intubation difficulty.

CONCLUSIONS

There is a significant correlation between throat area and tracheal intubation difficulty in infants with PRS. Body surface area, weight and gender may have an impact on tracheal intubation difficulty in infants with PRS.

Chemometric challenges in development of paper-based analytical devices: Optimization and image processing

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry.

Multi-template matching: a versatile tool for object-localization in microscopy images

BACKGROUND

The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in case-specific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity.

RESULTS

We provide a new implementation of template matching that offers higher detection capacity than single template approach, by enabling the detection of multiple template images. To provide an easy-to-use method for the automatic localization of objects of interest in microscopy images, we implemented multi-template matching as a Fiji plugin, a KNIME workflow and a python package. We demonstrate its application for the localization of entire, partial and multiple biological objects in zebrafish and medaka high-content screening datasets. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow is available on nodepit and KNIME Hub. Source codes and documentations are available on GitHub (https://github.com/multi-template-matching).

CONCLUSION

The novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions.

DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification

BACKGROUND AND OBJECTIVE

Deep learning techniques have been successfully applied to tackle several image classification problems in bioimaging. However, the models created from deep learning frameworks cannot be easily accessed from bioimaging tools such as ImageJ or Icy; this means that life scientists are not able to take advantage of the results obtained with those models from their usual tools. In this paper, we aim to facilitate the interoperability of bioimaging tools with deep learning frameworks.

METHODS

In this project, called DeepClas4Bio, we have developed an extensible API that provides a common access point for classification models of several deep learning frameworks. In addition, this API might be employed to compare deep learning models, and to extend the functionality of bioimaging programs by creating plugins.

RESULTS

Using the DeepClas4Bio API, we have developed a metagenerator to easily create ImageJ plugins. In addition, we have implemented a Java application that allows users to compare several deep learning models in a simple way using the DeepClas4Bio API. Moreover, we present three examples where we show how to work with different models and frameworks included in the DeepClas4Bio API using several bioimaging tools - namely, ImageJ, Icy and ImagePy.

CONCLUSIONS

This project brings to the table benefits from several perspectives. Developers of deep learning models can disseminate those models using well-known tools widely employed by life-scientists. Developers of bioimaging programs can easily create plugins that use models from deep learning frameworks. Finally, users of bioimaging tools have access to powerful tools in a known environment for them.

Evaluation of a genus-specific ELISA and a commercial Aspergillus Western blot IgG® immunoblot kit for the diagnosis of aspergillosis in common bottlenose dolphins (Tursiops truncatus)

Aspergillosis is a fungal infection with high mortality and morbidity rates. As in humans, its definitive diagnosis is difficult in animals, and thus new laboratory tools are required to overcome the diagnostic limitations due to low specificity and lack of standardization. In this study of common bottlenose dolphins (Tursiops truncatus), we evaluated the diagnostic performance of a new commercial immunoblot kit that had been initially developed for the serologic diagnosis of chronic aspergillosis in humans. Using this in a quantitative approach, we first established its positive cutoff within an observation cohort of 32 serum samples from dolphins with "proven" or "probable" diagnosis of aspergillosis and 55 negative controls. A novel enzyme-linked immunosorbent assay (ELISA) test was also developed for detecting anti-Aspergillus antibodies, and results were compared between the two assays. Overall, the diagnostic performance of immunoblot and ELISA were strongly correlated (P < .0001). The former showed lower sensitivity (65.6% versus 90.6%), but higher specificity (92.7% vs. 69.1%), with no cross-reaction with other fungal infections caused by miscellaneous non-Aspergillus genera. When assessing their use in a validation cohort, the immunoblot kit and the ELISA enabled positive diagnosis before mycological cultures in 42.9% and 33.3% subjects addressed for suspicion of aspergillosis, respectively. There was also significant impact of antifungal treatment on the results of the two tests (P < .05). In all, these new serological methods show promise in aiding in the diagnosis of aspergillosis in dolphins, and illustrate the opportunity to adapt commercial reagents directed for human diagnostics to detect similar changes in other animals.

ImageJ2: ImageJ for the next generation of scientific image data

BACKGROUND

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science.

RESULTS

We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace.

CONCLUSIONS

Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

ImageJ2: ImageJ for the next generation of scientific image data

BACKGROUND

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science.

RESULTS

We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace.

CONCLUSIONS

Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.