Community-developed checklists for publishing images and image analyses.

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.

A scalable phenotyping approach for female floral organ development and senescence in the absence of pollination in wheat.

In the absence of pollination, female reproductive organs senesce, leading to an irrevocable loss in the reproductive potential of the flower, which directly affects seed set. In self-pollinating crops like wheat (Triticum aestivum), the post-anthesis viability of unpollinated carpels has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which uses light-microscopy imaging and machine learning, for the analysis of floral organ traits in field-grown plants using fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase in which stigma area reaches its maximum and the radial expansion of the ovary slows, and a final deterioration phase. These developmental dynamics were consistent across years and could be used to classify male-sterile cultivars. This phenotyping approach provides a new tool for examining carpel development, which we hope will advance research into female fertility of wheat.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

Providing open imaging data at scale: An EMBL-EBI perspective.

Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Pictures at an exhibition: How to share your imaging data.

Open access to data underpinning published results is a key pillar of scientific reproducibility. Making data available at scale also provides opportunities for data reuse, encouraging the development of new analysis approaches. In this poster article, accompanying a recorded talk, we will explain the benefits of publicly archiving your image data alongside your published manuscripts, as well as highlight what resources are available to do this. This will include the BioImage Archive, EMBL-EBI's new resource for biological image data, https://www.ebi.ac.uk/bioimage-archive/. We will look at how image data submission works, how to prepare in advance for archiving your data and upcoming developments.

Spontaneous periocular ecchymosis: a major review.

Periocular ecchymosis, or periocular bruising, is a common clinical finding. Periocular skin is thin with an extensive vascular network, making this anatomical region prone to bruising. The most common etiology is trauma, but rarely, patients can present with spontaneous periocular ecchymosis (SPE). The pathophysiology of SPE is complex and varied. In this literature review of 121 articles, we assessed the frequency and variety of causation of this infrequent entity. The main finding was that by far the most common diagnosis causing SPE is amyloidosis and neoplasm, most notably neuroblastoma. Amyloidosis accounted for 23% articles (28/121) and neuroblastoma for 17% articles (21/121). Overall, neoplastic processes accounted for 30% of the articles (36/121), raised intracranial pressure and vascular malformations for 19% of the articles (23/121), migraine and atypical headache for 7% of the articles (8/121), while iatrogenic accounted for 5% of the articles (6/121). Through exploration and appreciation of the pathophysiology, we hope to foster a greater understanding in the clinician to establish underlying etiology, from benign to life-threatening, when presented with SPE.

Impact of female contraception for population management on behavior and social interactions in a captive troop of Guinea baboons (Papio papio).

As part of a population management strategy for a troop of Guinea baboons (Papio papio) at Yorkshire Wildlife Park, adult females were given a Suprelorin contraceptive implant. There was no information on the effect of contraception on behavior in this species, so behavioral data were collected before and after implant placement to determine any impact on the troops' welfare. Social interactions, affiliative, agonistic, and self-directed displacement behaviors (SDBs) in all adults were monitored via focal sampling. Preimplant data were collected in August/September 2019 before Suprelorin placement on November 2019, and postimplant data were collected in August/September 2020, allowing for comparison while controlling for seasonal differences in behavior. We found a significant reduction of SDBs after intervention, suggesting that contraception has a positive impact on group welfare, as SDBs are key behavioral indicators of stress and anxiety in nonhuman primates. Additionally, the rate of substate change per hour (i.e., the number of times the baboon changed their behavior), duration of sentry behavior, and the frequency of agonistic bouts were significantly lower postimplant, further suggesting improvements in welfare. There were also no significant decreases in the duration of positive social interactions, such as allogrooming and play. The results show that Suprelorin did not have a detrimental impact on the behavior of adults in the troop and may have even improved welfare. The use of Suprelorin in females could be considered as an effective population management strategy for primates existing in similar social systems.

The use of quantitative imaging to investigate regulators of membrane trafficking in Arabidopsis stomatal closure.

Expansion of gene families facilitates robustness and evolvability of biological processes but impedes functional genetic dissection of signalling pathways. To address this, quantitative analysis of single cell responses can help characterize the redundancy within gene families. We developed high-throughput quantitative imaging of stomatal closure, a response of plant guard cells, and performed a reverse genetic screen in a group of Arabidopsis mutants to five stimuli. Focussing on the intersection between guard cell signalling and the endomembrane system, we identified eight clusters based on the mutant stomatal responses. Mutants generally affected in stomatal closure were mostly in genes encoding SNARE and SCAMP membrane regulators. By contrast, mutants in RAB5 GTPase genes played specific roles in stomatal closure to microbial but not drought stress. Together with timed quantitative imaging of endosomes revealing sequential patterns in FLS2 trafficking, our imaging pipeline can resolve non-redundant functions of the RAB5 GTPase gene family. Finally, we provide a valuable image-based tool to dissect guard cell responses and outline a genetic framework of stomatal closure.

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

Quantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild-type and speechless leaves, and Drosophila amnioserosa cells. To validate our method's applicability to large populations, we analysed computer-generated tissues. By controlling in silico cell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue.

STARCH SYNTHASE 4 is required for normal starch granule initiation in amyloplasts of wheat endosperm.

Starch granule initiation is poorly understood at the molecular level. The glucosyltransferase, STARCH SYNTHASE 4 (SS4), plays a central role in granule initiation in Arabidopsis leaves, but its function in cereal endosperms is unknown. We investigated the role of SS4 in wheat, which has a distinct spatiotemporal pattern of granule initiation during grain development. We generated TILLING mutants in tetraploid wheat (Triticum turgidum) that are defective in both SS4 homoeologs. The morphology of endosperm starch was examined in developing and mature grains. SS4 deficiency led to severe alterations in endosperm starch granule morphology. During early grain development, while the wild-type initiated single 'A-type' granules per amyloplast, most amyloplasts in the mutant formed compound granules due to multiple initiations. This phenotype was similar to mutants deficient in B-GRANULE CONTENT 1 (BGC1). SS4 deficiency also reduced starch content in leaves and pollen grains. We propose that SS4 and BGC1 are required for the proper control of granule initiation during early grain development that leads to a single A-type granule per amyloplast. The absence of either protein results in a variable number of initiations per amyloplast and compound granule formation.

Pavement cells and the topology puzzle.

D'Arcy Thompson emphasised the importance of surface tension as a potential driving force in establishing cell shape and topology within tissues. Leaf epidermal pavement cells grow into jigsaw-piece shapes, highly deviating from such classical forms. We investigate the topology of developing Arabidopsis leaves composed solely of pavement cells. Image analysis of around 50,000 cells reveals a clear and unique topological signature, deviating from previously studied epidermal tissues. This topological distribution is established early during leaf development, already before the typical pavement cell shapes emerge, with topological homeostasis maintained throughout growth and unaltered between division and maturation zones. Simulating graph models, we identify a heuristic cellular division rule that reproduces the observed topology. Our parsimonious model predicts how and when cells effectively place their division plane with respect to their neighbours. We verify the predicted dynamics through in vivo tracking of 800 mitotic events, and conclude that the distinct topology is not a direct consequence of the jigsaw piece-like shape of the cells, but rather owes itself to a strongly life history-driven process, with limited impact from cell-surface mechanics.

Masqueraders of multiple evanescent white dot syndrome (MEWDS).

PURPOSE: To describe disorders that can masquerade as multiple evanescent white dot syndrome (MEWDS). DESIGN: Retrospective, multicenter case series. PARTICIPANTS: Patients who presented with clinical findings compatible with a diagnosis of MEWDS but were ultimately diagnosed with an alternative inflammatory, infectious, or neoplastic disorder. METHODS: Clinical records and multimodal imaging findings including fundus photography, fundus autofluorescence (FAF), fluorescein angiography (FA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and OCT angiography (OCTA) were analyzed. MAIN OUTCOME MEASURES: Inclusion criteria to be defined as a masquerade syndrome for MEWDS included the presence of disseminated grayish-white outer retinal spots that were hyperautofluorescent on FAF and associated with ellipsoid zone (EZ) disruption on OCT. RESULTS: Twenty-two eyes of 13 patients were identified. All patients presented with the classic findings of MEWDS listed above. A MEWDS-like presentation was bilateral in nine of 13 patients (69%). Final diagnosis was determined on the basis of additional investigations including serologies and biopsy. These diagnoses included syphilis (three patients), lymphoma (three patients), idiopathic multifocal choroiditis (two patients), idiopathic retinal phlebitis (one patient), idiopathic acute zonal occult outer retinopathy (one patient), sarcoidosis (one patient), tuberculosis (one patient), and cancer-associated retinopathy (one patient). The outer retinal lesions and imaging findings resolved with treatment for the associated systemic disorders. CONCLUSIONS: Widespread grayish-white outer retinal spots associated with hyperautofluorescence on FAF and disruption of the EZ on OCT are not pathognomonic for MEWDS. A high index of suspicion must be maintained for masqueraders of MEWDS, which can include serious inflammatory, infectious, and neoplastic disorders.

FoldAtlas: a repository for genome-wide RNA structure probing data.

Most RNA molecules form internal base pairs, leading to a folded secondary structure. Some of these structures have been demonstrated to be functionally significant. High-throughput RNA structure chemical probing methods generate millions of sequencing reads to provide structural constraints for RNA secondary structure prediction. At present, processed data from these experiments are difficult to access without computational expertise. Here we present FoldAtlas, a web interface for accessing raw and processed structural data across thousands of transcripts. FoldAtlas allows a researcher to easily locate, view, and retrieve probing data for a given RNA molecule. We also provide in silico and in vivo secondary structure predictions for comparison, visualized in the browser as circle plots and topology diagrams. Data currently integrated into FoldAtlas are from a new high-depth Structure-seq data analysis in Arabidopsis thaliana, released with this work. AVAILABILITY AND IMPLEMENTATION: The FoldAtlas website can be accessed at www.foldatlas.com Source code is freely available at github.com/mnori/foldatlas under the MIT license. Raw reads data are available under the NCBI SRA accession SRP066985. CONTACT: yiliang.ding@jic.ac.uk or matthew.norris@jic.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Outcomes of Mohs micrographic surgery for periocular squamous cell carcinoma.

INTRODUCTION: Periocular cutaneous squamous cell carcinoma (cSCC) accounts for 5-10% of all eyelid malignancies. Periocular cSCC carries a low mortality rate but can be destructive to local tissues. Due to the unique function and anatomy of the eyelids, Mohs micrographic surgery (MMS) is gold standard for treating cSCC to preserve healthy tissue and reduce rates of local recurrence. In this study, we describe the success and outcomes of MMS and subsequent oculoplastic reconstruction for periocular cSCC in the North East of England. METHODS: Retrospective analysis of 34 patients who underwent MMS for periocular cSCC in the North of England between 2013 and 2020. Primary outcome measure of success is defined as no recurrence of cSCC after minimum 24 months' time elapsed post-MMS. Secondary outcome measures included analysis of disease characteristics, describing the surgical techniques utilised for oculoplastic reconstruction following MMS and surgical complications should they occur. RESULTS: Two patients (5.9%) had local recurrence of periocular cSCC. Median time elapsed since MMS was 60 months. A variety of oculoplastic surgical techniques were utilised in the repair of the Mohs defect. One patient (2.9%) developed a significant post-operative reconstruction complication. CONCLUSION: Periocular cSCC recurrence following MMS in the North of England is 5.9%, which is comparable to the literature. Significant post-operative complications following oculoplastic reconstruction of periocular MMS are very low, occurring in 2.9% of cases in this study.

dtool and dserver: A flexible ecosystem for findable data.

Making data FAIR-findable, accessible, interoperable, reproducible-has become the recurring theme behind many research data management efforts. dtool is a lightweight data management tool that packages metadata with immutable data to promote accessibility, interoperability, and reproducibility. Each dataset is self-contained and does not require metadata to be stored in a centralised system. This decentralised approach means that finding datasets can be difficult. dtool's lookup server, short dserver, as defined by a REST API, makes dtool datasets findable, hence rendering the dtool ecosystem fit for a FAIR data management world. Its simplicity, modularity, accessibility and standardisation via API distinguish dtool and dserver from other solutions and enable it to serve as a common denominator for cross-disciplinary research data management. The dtool ecosystem bridges the gap between standardisation-free data management by individuals and FAIR platform solutions with rigid metadata requirements.

Public archiving of volume EM data.

Volume electron microscopy (vEM) techniques produce scientifically important datasets which are time and resource intensive to generate (Peddie et al., 2022). Public archival of such datasets, usually described in the literature, provides many benefits to the data depositors, to those making use of research results based on the datasets, and to the vEM community at large, both now and in the future. In this chapter we discuss these benefits, explain how EMBL-EBI's image data services support archival of both vEM and correlative imaging data, and discuss how future developments will unlock more value from these vEM datasets.

Classification of wheat diseases using deep learning networks with field and glasshouse images.

Crop diseases can cause major yield losses, so the ability to detect and identify them in their early stages is important for disease control. Deep learning methods have shown promise in classifying multiple diseases; however, many studies do not use datasets that represent real field conditions, necessitating either further image processing or reducing their applicability. In this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch. This dataset was used to train a deep learning model. The resulting model, named CerealConv, reached a 97.05% classification accuracy. When tested against trained pathologists on a subset of images from the larger dataset, the model delivered an accuracy score 2% higher than the best-performing pathologist. Image masks were used to show that the model was using the correct information to drive its classifications. These results show that deep learning networks are a viable tool for disease detection and classification in the field, and disease quantification is a logical next step.