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

Two-sided combinatorial volume bounds for non-obtuse hyperbolic polyhedra

We give a method for computing upper and lower bounds for the volume of a non-obtuse hyperbolic polyhedron in terms of the combinatorics of the 1-skeleton. We introduce an algorithm that detects the geometric decomposition of good 3-orbifolds with planar singular locus and underlying manifold the 3-sphere. The volume bounds follow from techniques related to the proof of Thurston's Orbifold Theorem, Schl\"afli's formula, and previous results of the author giving volume bounds for right-angled hyperbolic polyhedra.Comment: 36 pages, 19 figure

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

Metabolic and immunological responses of Drosophila melanogaster to dietary restriction and bacterial infection differ substantially between genotypes in a population

To respond to changing environmental conditions, a population may either shift toward better-adapted genotypes or adapt on an individual level. The present work aimed to quantify the relevance of these two processes by comparing the responses of defined Drosophila melanogaster populations to different stressors. To do this, we infected two homogeneous populations (isofemale lines), which differ significantly in fitness, and a synthetic heterogeneous population with a specific pathogen and/or exposed them to food restriction. Pectobacterium carotovorum was used to infect Drosophila larvae either fed standard or protein-restricted diet. In particular, the two homogeneous groups, which diverged in their fitness, showed considerable differences in all parameters assessed (survivorship, protein and lipid contents, phenol-oxidase (PO) activity, and antibacterial rate). Under fully nutritious conditions, larvae of the homogeneous population with low fitness exhibited lower survivorship and protein levels, as well as higher PO activity and antibacterial rate compared with the fitter population. A protein-restricted diet and bacterial infection provoked a decrease in survivorship, and antibacterial rate in most populations. Bacterial infection elicited an opposite response in protein and lipid content in both isofemale lines tested. Interestingly, the heterogeneous population showed a complex response pattern. The response of the heterogeneous population followed the fit genotype in terms of survival and antibacterial activity but followed the unfit genotype in terms of PO activity. In conclusion, our results show that defined genotypes exhibit highly divergent responses to varying stressors that are difficult to predict. Furthermore, the responses of heterogeneous populations do not follow a fixed pattern showing a very high degree of plasticity and differences between different genotypes

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

pp-sdsd shell gap reduction in neutron-rich systems and cross-shell excitations in 20^{20}O

Excited states in $^{20}$O were populated in the reaction $^{10}$Be($^{14}$C,$\alpha$) at Florida State University. Charged particles were detected with a particle telescope consisting of 4 annularly segmented Si surface barrier detectors and $\gamma$ radiation was detected with the FSU $\gamma$ detector array. Five new states were observed below 6 MeV from the $\alpha$-$\gamma$ and $\alpha$-$\gamma$-$\gamma$ coincidence data. Shell model calculations suggest that most of the newly observed states are core-excited 1p-1h excitations across the $N = Z = 8$ shell gap. Comparisons between experimental data and calculations for the neutron-rich O and F isotopes imply a steady reduction of the $p$-$sd$ shell gap as neutrons are added

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