The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images.

Quantification and modelling of curvilinear structures in 2D and 3D images is a common challenge in a wide range of biomedical applications. Image enhancement is a crucial pre-processing step for curvilinear structure quantification. Many of the existing state-of-the-art enhancement approaches still suffer from contrast variations and noise. In this paper, we propose to address such problems via the use of a multiscale image processing approach, called Multiscale Top-Hat Tensor (MTHT). MTHT produces a better quality enhancement of curvilinear structures in low contrast and noisy images compared with other approaches in a range of 2D and 3D biomedical images. The proposed approach combines multiscale morphological filtering with a local tensor representation of curvilinear structure. The MTHT approach is validated on 2D and 3D synthetic and real images, and is also compared to the state-of-the-art curvilinear structure enhancement approaches. The obtained results demonstrate that the proposed approach provides high-quality curvilinear structure enhancement, allowing high accuracy segmentation and quantification in a wide range of 2D and 3D image datasets.

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

CDC-42 and RAC-1 regulate opposite chemotropisms in Neurospora crassa.

Cell polarization and fusion are crucial developmental processes that occur in response to intracellular and extracellular signals. Asexual spores (conidia) of the mold Neurospora crassa differentiate two types of polarized cell protrusions, germ tubes and conidial anastomosis tubes (CATs), which exhibit negative and positive chemotropism, respectively. We provide the first evidence that shared and separate functions of the Rho-type GTPases CDC-42 and RAC-1 regulate these opposite chemotropisms. We demonstrate that RAC-1 is essential for CAT formation and cell fusion, whereas CDC-42 is necessary and sufficient for normal germ tube development. Cdc42-Rac-interactive-binding (CRIB) reporters were constructed to exclusively label locally activated GTP-bound GTPases. Time course analyses showed that repositioning of these activated GTPase clusters within germ tube and CAT tip apices controls directional growth in the absence of a tip-localized vesicle supply center (Spitzenkörper). We propose a model in which the local assembly of a plasma-membrane-associated GTPase-PAK-MAPK signaling platform regulates chemoattractant perception and secretion in order to synchronize oscillatory cell-cell communication and directional CAT tip growth.

Uncertainty-aware image classification on 3D CT lung.

Early detection is crucial for lung cancer to prolong the patient's survival. Existing model architectures used in such systems have shown promising results. However, they lack reliability and robustness in their predictions and the models are typically evaluated on a single dataset, making them overconfident when a new class is present. With the existence of uncertainty, uncertain images can be referred to medical experts for a second opinion. Thus, we propose an uncertainty-aware framework that includes three phases: data preprocessing and model selection and evaluation, uncertainty quantification (UQ), and uncertainty measurement and data referral for the classification of benign and malignant nodules using 3D CT images. To quantify the uncertainty, we employed three approaches; Monte Carlo Dropout (MCD), Deep Ensemble (DE), and Ensemble Monte Carlo Dropout (EMCD). We evaluated eight different deep learning models consisting of ResNet, DenseNet, and the Inception network family, all of which achieved average F1 scores above 0.832, and the highest average value of 0.845 was obtained using InceptionResNetV2. Furthermore, incorporating the UQ demonstrated significant improvement in the overall model performance. Upon evaluation of the uncertainty estimate, MCD outperforms the other UQ models except for the metric, URecall, where DE and EMCD excel, implying that they are better at identifying incorrect predictions with higher uncertainty levels, which is vital in the medical field. Finally, we show that using a threshold for data referral can greatly improve the performance further, increasing the accuracy up to 0.959.

Inhibition of PDIs Downregulates Core LINC Complex Proteins, Promoting the Invasiveness of MDA-MB-231 Breast Cancer Cells in Confined Spaces In Vitro.

Eukaryotic cells tether the nucleoskeleton to the cytoskeleton via a conserved molecular bridge, called the LINC complex. The core of the LINC complex comprises SUN-domain and KASH-domain proteins that directly associate within the nuclear envelope lumen. Intra- and inter-chain disulphide bonds, along with KASH-domain protein interactions, both contribute to the tertiary and quaternary structure of vertebrate SUN-domain proteins. The significance of these bonds and the role of PDIs (protein disulphide isomerases) in LINC complex biology remains unclear. Reducing and non-reducing SDS-PAGE analyses revealed a prevalence of SUN2 homodimers in non-tumorigenic breast epithelia MCF10A cells, but not in the invasive triple-negative breast cancer MDA-MB-231 cell line. Furthermore, super-resolution microscopy revealed SUN2 staining alterations in MCF10A, but not in MDA-MB-231 nuclei, upon reducing agent exposure. While PDIA1 levels were similar in both cell lines, pharmacological inhibition of PDI activity in MDA-MB-231 cells led to SUN-domain protein down-regulation, as well as Nesprin-2 displacement from the nucleus. This inhibition also caused changes in perinuclear cytoskeletal architecture and lamin downregulation, and increased the invasiveness of PDI-inhibited MDA-MB-231 cells in space-restrictive in vitro environments, compared to untreated cells. These results emphasise the key roles of PDIs in regulating LINC complex biology, cellular architecture, biomechanics, and invasion.

Bacterial cell identification in differential interference contrast microscopy images.

BACKGROUND: Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding. RESULTS: We have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides. CONCLUSIONS: Results demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.

Quantified colocalization reveals heterotypic histocompatibility class I antigen associations on trophoblast cell membranes: relevance for human pregnancy.

Human placental syncytiotrophoblasts lack expression of most types of human leukocyte antigen (HLA) class I and class II molecules; this is thought to contribute to a successful pregnancy. However, the HLA class Ib antigens HLA-G, -E, and -F and the HLA class Ia antigen HLA-C are selectively expressed on extravillous trophoblast cells, and they are thought to play a major role in controlling feto-maternal tolerance. We have hypothesized that selective expression, coupled with the preferential physical association of pairs of HLA molecules, contribute to the function of HLA at the feto-maternal interface and the maternal recognition of the fetus. We have developed a unique analytical model that allows detection and quantification of the heterotypic physical associations of HLA class I molecules expressed on the membrane of human trophoblast choriocarcinoma cells, ACH-3P and JEG-3. Automated image analysis was used to estimate the degree of overlap of HLA molecules labeled with different fluorochromes. This approach yields an accurate measurement of the degree of colocalization. In both JEG-3 and ACH-3P cells, HLA-C, -E, and -G were detected on the cell membrane, while the expression of HLA-F was restricted to the cytoplasm. Progesterone treatment alone induced a significant increase in the expression level of the HLA-G/HLA-E association, suggesting that this heterotypic association is modulated by this hormone. Our data shows that the cell-surface HLA class I molecules HLA-G, -E, and -C colocalize with each other and have the potential to form preferential heterotypic associations.

A bioimage informatics approach to automatically extract complex fungal networks.

MOTIVATION: Fungi form extensive interconnected mycelial networks that scavenge efficiently for scarce resources in a heterogeneous environment. The architecture of the network is highly responsive to local nutritional cues, damage or predation, and continuously adapts through growth, branching, fusion or regression. These networks also provide an example of an experimental planar network system that can be subjected to both theoretical analysis and experimental manipulation in multiple replicates. For high-throughput measurements, with hundreds of thousands of branches on each image, manual detection is not a realistic option, especially if extended time series are captured. Furthermore, branches typically show considerable variation in contrast as the individual cords span several orders of magnitude and the compressed soil substrate is not homogeneous in texture making automated segmentation challenging. RESULTS: We have developed and evaluated a high-throughput automated image analysis and processing approach using Phase Congruency Tensors and watershed segmentation to characterize complex fungal networks. The performance of the proposed approach is evaluated using complex images of saprotrophic fungal networks with 10(5)-10(6) edges. The results obtained demonstrate that this approach provides a fast and robust solution for detection and graph-based representation of complex curvilinear networks. AVAILABILITY AND IMPLEMENTATION: The Matlab toolbox is freely available through the Oxford e-Research Centre website: http://www.oerc.ox.ac.uk/research/bioimage/software CONTACTS: boguslaw.obara@oerc.ox.ac.uk.

A novel method for quantified, superresolved, three-dimensional colocalisation of isotropic, fluorescent particles.

Colocalisation, the overlap of subcellular structures labelled with different colours, is a key step to characterise cellular phenotypes. We have developed a novel bioimage informatics approach for quantifying colocalisation of round, blob-like structures in two-colour, highly resolved, three-dimensional fluorescence microscopy datasets. First, the algorithm identifies isotropic fluorescent particles, of relative brightness compared to their immediate neighbourhood, in three dimensions and for each colour. The centroids of these spots are then determined, and each object in one location of a colour image is checked for a corresponding object in the other colour image. Three-dimensional distance maps between the centroids of differently coloured spots then display where and how closely they colocalise, while histograms allow to analyse all colocalisation distances. We use the method to reveal sparse colocalisation of different human leukocyte antigen receptors in choriocarcinoma cells. It can also be applied to other isotropic subcellular structures such as vesicles, aggresomes and chloroplasts. The simple, robust and fast approach yields superresolved, object-based colocalisation maps and provides a first indication of protein-protein interactions of fluorescent, isotropic particles.

Variability in the control of cell division underlies sepal epidermal patterning in Arabidopsis thaliana.

How growth and proliferation are precisely controlled in organs during development and how the regulation of cell division contributes to the formation of complex cell type patterns are important questions in developmental biology. Such a pattern of diverse cell sizes is characteristic of the sepals, the outermost floral organs, of the plant Arabidopsis thaliana. To determine how the cell size pattern is formed in the sepal epidermis, we iterate between generating predictions from a computational model and testing these predictions through time-lapse imaging. We show that the cell size diversity is due to the variability in decisions of individual cells about when to divide and when to stop dividing and enter the specialized endoreduplication cell cycle. We further show that altering the activity of cell cycle inhibitors biases the timing and changes the cell size pattern as our model predicts. Models and observations together demonstrate that variability in the time of cell division is a major determinant in the formation of a characteristic pattern.

Biomedical Data Annotation: An OCT Imaging Case Study.

In ophthalmology, optical coherence tomography (OCT) is a widely used imaging modality, allowing visualisation of the structures of the eye with objective and quantitative cross-sectional three-dimensional (3D) volumetric scans. Due to the quantity of data generated from OCT scans and the time taken for an ophthalmologist to inspect for various disease pathology features, automated image analysis in the form of deep neural networks has seen success for the classification and segmentation of OCT layers and quantification of features. However, existing high-performance deep learning approaches rely on huge training datasets with high-quality annotations, which are challenging to obtain in many clinical applications. The collection of annotations from less experienced clinicians has the potential to alleviate time constraints from more senior clinicians, allowing faster data collection of medical image annotations; however, with less experience, there is the possibility of reduced annotation quality. In this study, we evaluate the quality of diabetic macular edema (DME) intraretinal fluid (IRF) biomarker image annotations on OCT B-scans from five clinicians with a range of experience. We also assess the effectiveness of annotating across multiple sessions following a training session led by an expert clinician. Our investigation shows a notable variance in annotation performance, with a correlation that depends on the clinician's experience with OCT image interpretation of DME, and that having multiple annotation sessions has a limited effect on the annotation quality.

Expansile gas kinetics for pneumatic retinopexy.

PURPOSE: To study the behaviour of expansile intravitreal gases and air used in treating rhegmatogenous retinal detachment. METHODS: A validated mathematical model of gas expansion and absorption in human eyes was used to simulate the effect of varying volumes of pure air, SF6, C2F6 and C3F8 injected into the vitreous cavity. Variation in axial length was accounted for by using three different vitreous cavity volumes to represent hypermetropic, emmetropic and myopic eyes. RESULTS: The time course of varying volumes of pure air and fluorinated gases injected into the vitreous cavity were tabulated, with calculated parameters including volume of gas, percentage gas fills and corresponding retinal contact angles at different time points. CONCLUSION: We produced a comprehensive compilation of expansive gas kinetics aiming to facilitate surgeon selection of the most suitable choice of gas and volume to use, tailored to an individual patient's clinical need.

Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study.

OBJECTIVE: Advanced laryngeal cancers are clinically complex; there is a paucity of modern decision-making models to guide tumour-specific management. This pilot study aims to identify computed tomography-based radiomic features that may predict survival and enhance prognostication. METHODS: Pre-biopsy, contrast-enhanced computed tomography scans were assembled from a retrospective cohort (n = 72) with advanced laryngeal cancers (T3 and T4). The LIFEx software was used for radiomic feature extraction. Two features: shape compacity (irregularity of tumour volume) and grey-level zone length matrix - grey-level non-uniformity (tumour heterogeneity) were selected via least absolute shrinkage and selection operator-based Cox regression and explored for prognostic potential. RESULTS: A greater shape compacity (hazard ratio 2.89) and grey-level zone length matrix - grey-level non-uniformity (hazard ratio 1.64) were significantly associated with worse 5-year disease-specific survival (p < 0.05). Cox regression models yielded a superior C-index when incorporating radiomic features (0.759) versus clinicopathological variables alone (0.655). CONCLUSIONS: Two radiomic features were identified as independent prognostic biomarkers. A multi-centre prospective study is necessary for further exploration. Integrated radiomic models may refine the treatment of advanced laryngeal cancers.

Flagellar hook flexibility is essential for bundle formation in swimming Escherichia coli cells.

Swimming Escherichia coli cells are propelled by the rotary motion of their flagellar filaments. In the normal swimming pattern, filaments positioned randomly over the cell form a bundle at the posterior pole. It has long been assumed that the hook functions as a universal joint, transmitting rotation on the motor axis through up to ∼90° to the filament in the bundle. Structural models of the hook have revealed how its flexibility is expected to arise from dynamic changes in the distance between monomers in the helical lattice. In particular, each of the 11 protofilaments that comprise the hook is predicted to cycle between short and long forms, corresponding to the inside and outside of the curved hook, once each revolution of the motor when the hook is acting as a universal joint. To test this, we genetically modified the hook so that it could be stiffened by binding streptavidin to biotinylated monomers, impeding their motion relative to each other. We found that impeding the action of the universal joint resulted in atypical swimming behavior as a consequence of disrupted bundle formation, in agreement with the universal joint model.

Big data for human security: The case of COVID-19.

The COVID-19 epidemic has changed the world dramatically since societies are changing their behaviour according to the new normal, which comes along with numerous challenges and uncertainties. These uncertainties have led to instabilities in several facets of society, most notably health, economy and public order. Measures to contain the pandemic by governments have occasionally met with increasing discontent from societies and have triggered social unrest, imposing serious threats to human security. Big Data Analytics can provide a powerful force multiplier to support policy and decision makers to contain the virus while at the same time dealing with such threats to human security. This paper presents the utilisation of a big data forecasting and analytics framework and its utilisation to deal with COVID-19 triggered social unrest. The paper is an extended version of paper Cárdenas et al. (2021) presented at the 2021 International Conference on Computational Science.

Silencing of CDK5 reduces neurofibrillary tangles in transgenic alzheimer's mice.

Alzheimer's disease is a major cause of dementia for which treatments remain unsatisfactory. Cyclin-dependent kinase 5 (CDK5) is a relevant kinase that has been hypothesized to contribute to the tau pathology. Several classes of chemical inhibitors for CDK5 have been developed, but they generally lack the specificity to distinguish among various ATP-dependent kinases. Therefore, the efficacy of these compounds when tested in animal models cannot definitively be attributed to an effect on CDK5. However, RNA interference (RNAi) targeting of CDK5 is specific and can be used to validate CDK5 as a possible treatment target. We delivered a CDK5 RNAi by lentiviral or adenoassociated viral vectors and analyzed the results in vitro and in vivo. Silencing of CDK5 reduces the phosphorylation of tau in primary neuronal cultures and in the brain of wild-type C57BL/6 mice. Furthermore, the knockdown of CDK5 strongly decreased the number of neurofibrillary tangles in the hippocampi of triple-transgenic mice (3×Tg-AD mice). Our data suggest that this downregulation may be attributable to the reduction of the CDK5 availability in the tissue, without affecting the CDK5 kinase activity. In summary, our findings validate CDK5 as a reasonable therapeutic target for ameliorating tau pathology.

Bisque: a platform for bioimage analysis and management.

MOTIVATION: Advances in the field of microscopy have brought about the need for better image management and analysis solutions. Novel imaging techniques have created vast stores of images and metadata that are difficult to organize, search, process and analyze. These tasks are further complicated by conflicting and proprietary image and metadata formats, that impede analyzing and sharing of images and any associated data. These obstacles have resulted in research resources being locked away in digital media and file cabinets. Current image management systems do not address the pressing needs of researchers who must quantify image data on a regular basis. RESULTS: We present Bisque, a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend Bisque with both data model and analysis extensions in order to adapt the system to local needs. Bisque's extensibility stems from two core concepts: flexible metadata facility and an open web-based architecture. Together these empower researchers to create, develop and share novel bioimage analyses. Several case studies using Bisque with specific applications are presented as an indication of how users can expect to extend Bisque for their own purposes.

Benchmarking automated detection of the retinal external limiting membrane in a 3D spectral domain optical coherence tomography image dataset of full thickness macular holes.

In this article, we present a new benchmark for the segmentation of the retinal external limiting membrane (ELM) using an image dataset of spectral domain optical coherence tomography (OCT) scans in a patient population with idiopathic full-thickness macular holes. Specifically, the dataset used contains OCT images from one eye of 107 patients with an idiopathic full-thickness macular hole. In total, the dataset contains 5243 individual 2-dimensional (2-D) OCT image slices, with each patient contributing 49 individual spectral-domain OCT tagged image slices. We display precise image-wise binary annotations to segment the ELM line. The OCT images present high variations in image contrast, motion, brightness, and speckle noise which can affect the robustness of applied algorithms, so we performed an extensive OCT imaging and annotation data quality analysis. Imaging data quality control included noise, blurriness and contrast scores, motion estimation, darkness and average pixel scores, and anomaly detection. Annotation quality was measured using gradient mapping of ELM line annotation confidence, and idiopathic full-thickness macular hole detection. Finally, we compared qualitative and quantitative results with seven state-of-the-art machine learning-based segmentation methods to identify the ELM line with an automated system.

The application of radiomics in laryngeal cancer.

OBJECTIVES: Radiomics is the conversion of medical images into quantitative high-dimensional data. Laryngeal cancer, one of the most common head and neck cancers, has risen globally by 58.7%. CT, MRI and PET are acquired during the diagnostic process providing potential data for radiomic analysis and correlation with outcomes.This review aims to examine the applications of this technique to laryngeal cancer and the future considerations for translation into clinical practice. METHODS: A comprehensive systematic review-informed search of the MEDLINE and EMBASE databases was undertaken. Keywords "laryngeal cancer" OR "larynx" OR "larynx cancer" OR "head and neck cancer" were combined with "radiomic" OR "signature" OR "machine learning" OR "artificial intelligence". Additional articles were obtained from bibliographies using the "snowball method". RESULTS: The included studies (n = 15) demonstrated that radiomic features are significantly associated with various clinical outcomes (including stage, overall survival, treatment response, progression-free survival) and that predictive models incorporating radiomic features are superior to those that do not. Two studies demonstrated radiomics could improve laryngeal cancer staging whilst 12 studies affirmed its predictive capability for clinical outcomes. CONCLUSIONS: Radiomics has potential for improving multiple aspects of laryngeal cancer care; however, the heterogeneous cohorts and lack of data on laryngeal cancer exclusively inhibits firm conclusions. Large prospective well-designed studies in laryngeal cancer are required to progress this field. Furthermore, to implement radiomics into clinical practice, a unified research effort is required to standardise radiomics practice. ADVANCES IN KNOWLEDGE: This review has highlighted the value of radiomics in enhancing laryngeal cancer care (including staging, prognosis and predicting treatment response).

Biallelic SYNE2 Missense Mutations Leading to Nesprin-2 Giant Hypo-Expression Are Associated with Intellectual Disability and Autism.

Autism spectrum disorder (ASD) is a group of neurological and developmental disabilities characterised by clinical and genetic heterogeneity. The current study aimed to expand ASD genotyping by investigating potential associations with SYNE2 mutations. Specifically, the disease-causing variants of SYNE2 in 410 trios manifesting neurodevelopmental disorders using whole-exome sequencing were explored. The consequences of the identified variants were studied at the transcript level using quantitative polymerase chain reaction (qPCR). For validation, immunofluorescence and immunoblotting were performed to analyse mutational effects at the protein level. The compound heterozygous variants of SYNE2 (NM_182914.3:c.2483T>G; p.(Val828Gly) and NM_182914.3:c.2362G>A; p.(Glu788Lys)) were identified in a 4.5-year-old male, clinically diagnosed with autism spectrum disorder, developmental delay and intellectual disability. Both variants reside within the nesprin-2 giant spectrin repeat (SR5) domain and are predicted to be highly damaging using in silico tools. Specifically, a significant reduction of nesprin-2 giant protein levels is revealed in patient cells. SYNE2 transcription and the nuclear envelope localisation of the mutant proteins was however unaffected as compared to parental control cells. Collectively, these data provide novel insights into the cardinal role of the nesprin-2 giant in neurodevelopment and suggest that the biallelic hypomorphic SYNE2 mutations may be a new cause of intellectual disability and ASD.