Prevalence of interpersonal violence against children in sport in six European countries.

BACKGROUND: Investigating prevalence of child abuse in sport is a relatively new field of research, born from the need for credible data on this phenomenon. OBJECTIVE: To establish prevalence rates of interpersonal violence against children in sport in six European countries. PARTICIPANTS AND SETTING: The sample (N = 10,302) consists of individuals aged 18-30 who had participated in organized sport prior to age 18 (49.3 % male, 50 % female). METHODS: A self-report questionnaire was developed (the Interpersonal Violence Against Children in Sport Questionnaire or IVACS-Q) to measure prevalence of five categories of interpersonal violence (neglect, psychological violence, physical violence, non-contact sexual violence, and contact sexual violence) against children who participate in sport. Validation testing (published separately) showed reasonable levels of convergent and divergent validity. Prevalence rates are calculated by national context, whether inside or outside sport, and by sex (male/female). RESULTS: Prevalence of IVACS inside sport differed by category: psychological violence (65 %, n = 6679), physical violence (44 %, n = 4514), neglect (37 %, n = 3796), non-contact sexual violence (35 %, n = 3565), and contact sexual violence (20 %, n = 2060). Relatively small geographical differences were found. Across all categories, males (79 %, n = 4018) reported significantly more experiences inside sport than females (71 %, n = 3653) (χ2(1) = 92.507, p < .000). Strong correlations were found between experiencing violence inside and outside sport. CONCLUSIONS: Interpersonal violence against children in sport is widespread. The sector's approach to prevention must recognize the risks to female and male children (and all children) and the additional vulnerabilities of abused children. Further comparative and longitudinal research within sport is required.

A framework for evaluating the performance of SMLM cluster analysis algorithms.

Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.

Correction of multiple-blinking artifacts in photoactivated localization microscopy.

Photoactivated localization microscopy (PALM) produces an array of localization coordinates by means of photoactivatable fluorescent proteins. However, observations are subject to fluorophore multiple blinking and each protein is included in the dataset an unknown number of times at different positions, due to localization error. This causes artificial clustering to be observed in the data. We present a 'model-based correction' (MBC) workflow using calibration-free estimation of blinking dynamics and model-based clustering to produce a corrected set of localization coordinates representing the true underlying fluorophore locations with enhanced localization precision, outperforming the state of the art. The corrected data can be reliably tested for spatial randomness or analyzed by other clustering approaches, and descriptors such as the absolute number of fluorophores per cluster are now quantifiable, which we validate with simulated data and experimental data with known ground truth. Using MBC, we confirm that the adapter protein, the linker for activation of T cells, is clustered at the T cell immunological synapse.

Steer'n'Detect: fast 2D template detection with accurate orientation estimation.

MOTIVATION: Rotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly degrades in noisy image data. RESULTS: We introduce Steer'n'Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images. Steer'n'Detect provides a faster and more robust substitute to template matching. By adapting to the statistics of the image background, it guarantees accurate results even in the presence of noise. The plugin comes with an intuitive user interface facilitating results analysis and further post-processing. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biomedical-Imaging-Group/Steer-n-Detect. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ.

DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .

In pancreatic islets from type 2 diabetes patients, the dampened circadian oscillators lead to reduced insulin and glucagon exocytosis.

Circadian clocks operative in pancreatic islets participate in the regulation of insulin secretion in humans and, if compromised, in the development of type 2 diabetes (T2D) in rodents. Here we demonstrate that human islet α- and ß-cells that bear attenuated clocks exhibit strongly disrupted insulin and glucagon granule docking and exocytosis. To examine whether compromised clocks play a role in the pathogenesis of T2D in humans, we quantified parameters of molecular clocks operative in human T2D islets at population, single islet, and single islet cell levels. Strikingly, our experiments reveal that islets from T2D patients contain clocks with diminished circadian amplitudes and reduced in vitro synchronization capacity compared to their nondiabetic counterparts. Moreover, our data suggest that islet clocks orchestrate temporal profiles of insulin and glucagon secretion in a physiological context. This regulation was disrupted in T2D subjects, implying a role for the islet cell-autonomous clocks in T2D progression. Finally, Nobiletin, an agonist of the core-clock proteins RORα/γ, boosted both circadian amplitude of T2D islet clocks and insulin secretion by these islets. Our study emphasizes a link between the circadian clockwork and T2D and proposes that clock modulators hold promise as putative therapeutic agents for this frequent disorder.

Publisher Correction: Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

In the version of this paper originally published, Figure 4a contained errors that were introduced during typesetting. The bottom 11° ThunderSTORM image is an xz view but was incorrectly labeled as xy, and the low x-axis value in the four line profiles was incorrectly set as -60 instead of -50. These errors have been corrected in the PDF and HTML versions of the paper.

Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

With the widespread uptake of two-dimensional (2D) and three-dimensional (3D) single-molecule localization microscopy (SMLM), a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. We generated realistic simulated datasets for popular imaging modalities-2D, astigmatic 3D, biplane 3D and double-helix 3D-and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D SMLM software and provides a holistic view of how the latest 2D and 3D SMLM packages perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against the current state of the art, and provides insight into the current limits of the field.

Investigating Focal Adhesion Substructures by Localization Microscopy.

Cells rely on focal adhesions (FAs) to carry out a variety of important tasks, including motion, environmental sensing, and adhesion to the extracellular matrix. Although attaining a fundamental characterization of FAs is a compelling goal, their extensive complexity and small size, which can be below the diffraction limit, have hindered a full understanding. In this study we have used single-molecule localization microscopy (SMLM) to investigate integrin ß3 and paxillin in rat embryonic fibroblasts growing on two different extracellular matrix-representing substrates (i.e., fibronectin-coated substrates and specifically biofunctionalized nanopatterned substrates). To quantify the substructure of FAs, we developed a clustering method based on expectation maximization of a Gaussian mixture that accounts for localization uncertainty and background. Analysis of our SMLM data indicates that the structures within FAs, characterized as a Gaussian mixture, typically have areas between 0.01 and 1 µm2, contain 10-100 localizations, and can exhibit substantial eccentricity. Our approach based on SMLM opens new avenues for studying structural and functional biology of molecular assemblies that display substantial varieties in size, shape, and density.

Pancreatic α- and ß-cellular clocks have distinct molecular properties and impact on islet hormone secretion and gene expression.

A critical role of circadian oscillators in orchestrating insulin secretion and islet gene transcription has been demonstrated recently. However, these studies focused on whole islets and did not explore the interplay between α-cell and ß-cell clocks. We performed a parallel analysis of the molecular properties of α-cell and ß-cell oscillators using a mouse model expressing three reporter genes: one labeling α cells, one specific for ß cells, and a third monitoring circadian gene expression. Thus, phase entrainment properties, gene expression, and functional outputs of the α-cell and ß-cell clockworks could be assessed in vivo and in vitro at the population and single-cell level. These experiments showed that α-cellular and ß-cellular clocks are oscillating with distinct phases in vivo and in vitro. Diurnal transcriptome analysis in separated α and ß cells revealed that a high number of genes with key roles in islet physiology, including regulators of glucose sensing and hormone secretion, are differentially expressed in these cell types. Moreover, temporal insulin and glucagon secretion exhibited distinct oscillatory profiles both in vivo and in vitro. Altogether, our data indicate that differential entrainment characteristics of circadian α-cell and ß-cell clocks are an important feature in the temporal coordination of endocrine function and gene expression.

DeconvolutionLab2: An open-source software for deconvolution microscopy.

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples. Unfortunately, dealing with deconvolution tools is not straightforward. Among others, end users have to select the appropriate algorithm, calibration and parametrization, while potentially facing demanding computational tasks. To make deconvolution more accessible, we have developed a practical platform for deconvolution microscopy called DeconvolutionLab. Freely distributed, DeconvolutionLab hosts standard algorithms for 3D microscopy deconvolution and drives them through a user-oriented interface. In this paper, we take advantage of the release of DeconvolutionLab2 to provide a complete description of the software package and its built-in deconvolution algorithms. We examine several standard algorithms used in deconvolution microscopy, notably: Regularized inverse filter, Tikhonov regularization, Landweber, Tikhonov-Miller, Richardson-Lucy, and fast iterative shrinkage-thresholding. We evaluate these methods over large 3D microscopy images using simulated datasets and real experimental images. We distinguish the algorithms in terms of image quality, performance, usability and computational requirements. Our presentation is completed with a discussion of recent trends in deconvolution, inspired by the results of the Grand Challenge on deconvolution microscopy that was recently organized.

Transforms and Operators for Directional Bioimage Analysis: A Survey.

We give a methodology-oriented perspective on directional image analysis and rotation-invariant processing. We review the state of the art in the field and make connections with recent mathematical developments in functional analysis and wavelet theory. We unify our perspective within a common framework using operators. The intent is to provide image-processing methods that can be deployed in algorithms that analyze biomedical images with improved rotation invariance and high directional sensitivity. We start our survey with classical methods such as directional-gradient and the structure tensor. Then, we discuss how these methods can be improved with respect to robustness, invariance to geometric transformations (with a particular interest in scaling), and computation cost. To address robustness against noise, we move forward to higher degrees of directional selectivity and discuss Hessian-based detection schemes. To present multiscale approaches, we explain the differences between Fourier filters, directional wavelets, curvelets, and shearlets. To reduce the computational cost, we address the problem of matching directional patterns by proposing steerable filters, where one might perform arbitrary rotations and optimizations without discretizing the orientation. We define the property of steerability and give an introduction to the design of steerable filters. We cover the spectrum from simple steerable filters through pyramid schemes up to steerable wavelets. We also present illustrations on the design of steerable wavelets and their application to pattern recognition.

Variational Phase Imaging Using the Transport-of-Intensity Equation.

We introduce a variational phase retrieval algorithm for the imaging of transparent objects. Our formalism is based on the transport-of-intensity equation (TIE), which relates the phase of an optical field to the variation of its intensity along the direction of propagation. TIE practically requires one to record a set of defocus images to measure the variation of intensity. We first investigate the effect of the defocus distance on the retrieved phase map. Based on our analysis, we propose a weighted phase reconstruction algorithm yielding a phase map that minimizes a convex functional. The method is nonlinear and combines different ranges of spatial frequencies - depending on the defocus value of the measurements - in a regularized fashion. The minimization task is solved iteratively via the alternating-direction method of multipliers. Our simulations outperform commonly used linear and nonlinear TIE solvers. We also illustrate and validate our method on real microscopy data of HeLa cells.

SpotCaliper: fast wavelet-based spot detection with accurate size estimation.

MOTIVATION: SpotCaliper is a novel wavelet-based image-analysis software providing a fast automatic detection scheme for circular patterns (spots), combined with the precise estimation of their size. It is implemented as an ImageJ plugin with a friendly user interface. The user is allowed to edit the results by modifying the measurements (in a semi-automated way), extract data for further analysis. The fine tuning of the detections includes the possibility of adjusting or removing the original detections, as well as adding further spots. RESULTS: The main advantage of the software is its ability to capture the size of spots in a fast and accurate way. AVAILABILITY AND IMPLEMENTATION: http://bigwww.epfl.ch/algorithms/spotcaliper/ CONTACT: zsuzsanna.puspoki@epfl.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative evaluation of software packages for single-molecule localization microscopy.

The quality of super-resolution images obtained by single-molecule localization microscopy (SMLM) depends largely on the software used to detect and accurately localize point sources. In this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. Our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software. We prepared synthetic data that represent three-dimensional structures modeled after biological components, taking excitation parameters, noise sources, point-spread functions and pixelation into account. We then asked developers to run their software on our data; most responded favorably, allowing us to present a broad picture of the methods available. We evaluated their results using quantitative and user-interpretable criteria: detection rate, accuracy, quality of image reconstruction, resolution, software usability and computational resources. These metrics reflect the various tradeoffs of SMLM software packages and help users to choose the software that fits their needs.

Analysis of S. pombe SIN protein association to the SPB reveals two genetically separable states of the SIN.

The Schizosaccharomyces pombe septation initiation network (SIN) regulates cytokinesis, and asymmetric association of SIN proteins with the mitotic spindle pole bodies (SPBs) is important for its regulation. Here, we have used semi-automated image analysis to study SIN proteins in large numbers of wild-type and mutant cells. Our principal conclusions are: first, that the association of Cdc7p with the SPBs in early mitosis is frequently asymmetric, with a bias in favour of the new SPB; second, that the early association of Cdc7p-GFP to the SPB depends on Plo1p but not Spg1p, and is unaffected by mutations that influence its asymmetry in anaphase; third, that Cdc7p asymmetry in anaphase B is delayed by Pom1p and by activation of the spindle assembly checkpoint, and is promoted by Rad24p; and fourth, that the length of the spindle, expressed as a fraction of the length of the cell, at which Cdc7p becomes asymmetric is similar in cells dividing at different sizes. These data reveal that multiple regulatory mechanisms control the SIN in mitosis and lead us to propose a two-state model to describe the SIN.

Understanding and enhancing future infrastructure resiliency: a socio-ecological approach.

The resilience of any system, human or natural, centres on its capacity to adapt its structure, but not necessarily its function, to a new configuration in response to long-term socio-ecological change. In the long term, therefore, enhancing resilience involves more than simply improving a system's ability to resist an immediate threat or to recover to a stable past state. However, despite the prevalence of adaptive notions of resilience in academic discourse, it is apparent that infrastructure planners and policies largely continue to struggle to comprehend longer-term system adaptation in their understanding of resilience. Instead, a short-term, stable system (STSS) perspective on resilience is prevalent. This paper seeks to identify and problematise this perspective, presenting research based on the development of a heuristic 'scenario-episode' tool to address, and challenge, it in the context of United Kingdom infrastructure resilience. The aim is to help resilience practitioners to understand better the capacities of future infrastructure systems to respond to natural, malicious threats.

Persistent pharmacokinetic challenges to pediatric drug development.

The development of new therapeutic agents for the mitigation of pediatric disorders is largely hindered by the inability for investigators to assess pediatric pharmacokinetics (PK) in healthy patients due to substantial safety concerns. Pediatric patients are a clinical moving target for drug delivery due to changes in absorption, distribution, metabolism and excretion (ADME) and the potential for PK related toxicological (T) events to occur throughout development. These changes in ADMET can have profound effects on drug delivery, and may lead to toxic or sub-therapeutic outcomes. Ethical, economical, logistical, and technical barriers have resulted in insufficient investigation of these changes by industrial, regulatory, and academic bodies, leading to the classification of pediatric patients as therapeutic orphans. In response to these concerns, regulatory agencies have incentivized investigation into these ontogenic changes and their effects on drug delivery in pediatric populations. The intent of this review is to briefly present a synopsis of the development changes that occur in pediatric patients, discuss the effects of these changes on ADME and drug delivery strategies, highlight the hurdles that are still being faced, and present some opportunities to overcome these challenges.