TraCurate: Efficiently curating cell tracks

TraCurate is an open-source software tool to curate and manually annotate cell tracking data from time-lapse microscopy. Although many studies of cellular behavior require high-quality, long-term observations across generations of cells, automated cell tracking is often imperfect and typically yields fragmented results that still contain many errors. TraCurate provides the functionality for the curation and correction of cell tracking data with minimal user interaction and expenditure of time and supports the extraction of complete cell tracks and cellular genealogies from experimental data. Source code and binary packages for Linux, macOS and Windows are available at https://tracurate.gitlab.io/, as well as all other complementary tools described herein

Increase in national intravenous thrombolysis rates for ischaemic stroke between 2005 and 2012: Is bigger better?

Background: Intravenous thrombolytic therapy after ischaemic stroke significantly reduces mortality and morbidity. Actual thrombolysis rates are disappointingly low in many western countries. It has been suggested that higher patient volume is related to shorter door-to-needle-time (DNT) and increased thrombolysis rates. We address a twofold research question: a) What are trends in national thrombolysis rates and door-to-needle times in the Netherlands between 2005-2012? and b) Is there a relationship between stroke patient volume per hospital, thrombolysis rates and DNT? Methods: We used data from the Stroke Knowledge Network Netherlands dataset. Information on volume, intravenous thrombolysis rates, and admission characteristics per hospital is acquired through yearly surveys, in up to 65 hospitals between January 2005 and December 2012. We used linear regression to determine a possible relationship between hospital stroke admission volume, hospital thrombolysis rates and mean hospital DNT, adjusted for patient characteristics. Results: Information on 121.887 stroke admissions was available, ranging from 7.393 admissions in 2005 to 24.067 admissions in 2012. Mean national thrombolysis rate increased from 6.4 % in 2005 to 14.6 % in 2012. Patient characteristics (mean age, gender, type of stroke) remained stable. Mean DNT decreased from 72.7 min in 2005 to 41.4 min in 2012. Volume of stroke admissions was not an independent predictor for mean thrombolysis rate nor for mean DNT. Conclusion: Intravenous thrombolysis rates in the Netherlands more than doubled between 2005 and 2012, in parallel with a large decline in mean DNT. We found no convincing evidence for a relationship between stroke patient volume per hospital and thrombolysis rate or DNT

Simulating local deformations in the human cortex due to blood flow-induced changes in mechanical tissue properties: Impact on functional magnetic resonance imaging

Investigating human brain tissue is challenging due to the complexity and the manifold interactions between structures across different scales. Increasing evidence suggests that brain function and microstructural features including biomechanical features are related. More importantly, the relationship between tissue mechanics and its influence on brain imaging results remains poorly understood. As an important example, the study of the brain tissue response to blood flow could have important theoretical and experimental consequences for functional magnetic resonance imaging (fMRI) at high spatial resolutions. Computational simulations, using realistic mechanical models can predict and characterize the brain tissue behavior and give us insights into the consequent potential biases or limitations of in vivo, high-resolution fMRI. In this manuscript, we used a two dimensional biomechanical simulation of an exemplary human gyrus to investigate the relationship between mechanical tissue properties and the respective changes induced by focal blood flow changes. The model is based on the changes in the brain’s stiffness and volume due to the vasodilation evoked by neural activity. Modeling an exemplary gyrus from a brain atlas we assessed the influence of different potential mechanisms: (i) a local increase in tissue stiffness (at the level of a single anatomical layer), (ii) an increase in local volume, and (iii) a combination of both effects. Our simulation results showed considerable tissue displacement because of these temporary changes in mechanical properties. We found that the local volume increase causes more deformation and consequently higher displacement of the gyrus. These displacements introduced considerable artifacts in our simulated fMRI measurements. Our results underline the necessity to consider and characterize the tissue displacement which could be responsible for fMRI artifacts

linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data

Pan-embryo cell dynamics of germlayer formation in zebrafish

Cell movements are coordinated across spatio-temporal scales to achieve precise positioning of organs during vertebrate gastrulation. In zebrafish, mechanisms governing such morphogenetic movements have so far only been studied within a local region or a single germlayer. Here, we present pan-embryo analyses of fate specification and dynamics of all three germlayers simultaneously within a gastrulating embryo, showing that cell movement characteristics are predominantly determined by its position within the embryo, independent of its germlayer identity. The spatially confined fate specification establishes a distinct distribution of cells in each germlayer during early gastrulation. The differences in the initial distribution are subsequently amplified by a unique global movement, which organizes the organ precursors along the embryonic body axis, giving rise to the blueprint of organ formation

Quality control for more reliable integration of deep learning-based image segmentation into medical workflows

Machine learning algorithms underpin modern diagnostic-aiding software, whichhas proved valuable in clinical practice, particularly in radiology. However,inaccuracies, mainly due to the limited availability of clinical samples fortraining these algorithms, hamper their wider applicability, acceptance, andrecognition amongst clinicians. We present an analysis of state-of-the-artautomatic quality control (QC) approaches that can be implemented within thesealgorithms to estimate the certainty of their outputs. We validated the mostpromising approaches on a brain image segmentation task identifying whitematter hyperintensities (WMH) in magnetic resonance imaging data. WMH are acorrelate of small vessel disease common in mid-to-late adulthood and areparticularly challenging to segment due to their varied size, anddistributional patterns. Our results show that the aggregation of uncertaintyand Dice prediction were most effective in failure detection for this task.Both methods independently improved mean Dice from 0.82 to 0.84. Our workreveals how QC methods can help to detect failed segmentation cases andtherefore make automatic segmentation more reliable and suitable for clinicalpractice.<br

How to predict relapse in leukemia using time series data: A comparative in silico study

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients

Iron-induced relaxation mechanisms in the human substantia nigra: Towards quantifying iron load in dopaminergic neurons

Pathological iron accumulation in the human brain is a biomarker for neurodegeneration. Several diagnostically promising MR- based methods for in vivo iron quantification were proposed, based on the empirical relationship between R 2 * and iron concentration. However, these do not account for different chemical forms and cellular distribution of iron. We combined post mortem MRI, advanced quantitative histology and biophysical modeling to develop a generative theory linking obtained iron concentrations to quantitative MR parameters. The impact of nanoscale molecular interaction of water with iron and of iron-rich dopaminergic neurons was quantified in substantia nigra

Aromatic polymers made by reductive polydehalogenation of oligocyclic monomers as conjugated polymers of intrinsic microporosity (C-PIMs)

Reductive dehalogenation polycondensation of a series of penta-or hexacyclic, bisgeminal tetrachlorides with dicobalt octacarbonyl leads to the formation of homopolymers and copolymers with very different optical spectra. While the formation of tetrabenzoheptafulvalene connectors introduces efficient conjugation barriers due to their strongly folded structure, linking of 5-membered ring-based pentacyclic building blocks via bifluorenylidene connectors allows for an extended π-conjugation along the main chain. A comparison of homopolymer P57 and copolymer P55/77 indicates a quite different reactivity for dichloromethylene functions if incorporated into 5-or 7-membered rings. Interestingly, all investigated (co)polymers show an intrinsic microporosity in the solid-state (forming so-called Conjugated Polymers of Intrinsic Microporosity C-PIMs) and have SBET values of up to 760 m2 g-1 for homopolymer P77. This value is one of the highest reported values to date for C-PIMs