Biology subjects

Hansen, L. K.

Publications and source records attributed to Hansen, L. K..

4 recordsLinked to original sources

bioRxiv2023

A novel high-throughput assay identifies small molecules with activity against persister cells

Persister cells are a subpopulation of transiently antibiotic tolerant bacteria, which are believed to be the main cause of relapsing bacterial infections. Due to the importance of persister cells in human infections, there is a need for new antibiotics that kill bacteria independently of their activity. However, high-throughput assays to screen for drugs with such activity are missing. This is partly due to the transient nature of the phenotype, which makes it is difficult to prepare a concentrated population of persister cells that remain inactive during incubation with antibiotics in standard growth media. The purpose of this study was to develop a simple and high-throughput assay to identify compounds with antimicrobial activity against persister cells during a 24 h incubation period. Subsequently, this assay was used to screen a selection of small molecules with hypothesized antimicrobial activity. The fraction of S. aureus that tolerate bactericidal concentrations of ciprofloxacin were defined as persister cells. We first quantified how the cell concentration, growth phase, antibiotic concentration, duration of antibiotic exposure, and presence/absence of nutrients during antibiotic exposure affected the fraction of persister cells in a population. After optimizing these parameters, we compared our approach to generate persister cells, to a process that generated persister cells by a short exposure to rifampicin. Finally, we used the optimised protocol to identify molecular structures that have anti-persister activity by performing screening on initially compound fragments and then selecting compounds that incorporated the fragments that displayed activity. We show that exponential- and stationary-phase cultures transferred to nutrient-rich media only contain a small fraction (0.001 to 0.07 %) of persister cells that tolerated 10, 50 and 100 x MIC ciprofloxacin. Exponential-phase cultures displayed a bi-phasic time-kill curve, which plateaued after 5 h exposure, while stationary phase cultures displayed a low, but constant death rate at 50 and 100 x MIC ciprofloxacin. Inducing the persister phenotype with a short rifampicin treatment resulted in 100% persister cells when evaluated after [≤]5 h exposure to ciprofloxacin. However, after longer incubation times, cells resumed activity and lost their tolerance to ciprofloxacin. Tolerance was only maintained in the majority of the population for the full 24 h incubation period if cells were transferred to a carbon-free minimal medium before exposure to ciprofloxacin. We conclude that keeping cells starved in a carbon-free medium enables generation of high concentrations of S. aureus cells that tolerate 50 x MIC ciprofloxacin, and we find this protocol easily applicable for rapid screening of anti-persister drugs that act on dormant or non-dividing cells.

microbiology↗

bioRxiv2019

A framework for closed-loop neurofeedback for real-time EEG decoding

Neurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroen-cephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.\n\nThe software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenters needs.\n\nAs a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre- and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.\n\nThe proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

neuroscience↗

bioRxiv2018

Single Trial Decoding of Scalp EEG Under Naturalistic Stimuli

There is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. EEG signals are subject-specific, sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that Support Vector Machine (SVM) classifiers trained on a relatively small set of de-noised (averaged) pseudo-trials perform on par with classifiers trained on a large set of noisy single-trial samples. For visualization of EEG signatures exploited by SVM classifiers, we propose a novel method for computing sensitivity maps of EEG-based SVM classifiers. Moreover, we apply the NPAIRS resampling framework for estimation of map uncertainty and show that effect sizes of sensitivity maps for classifiers trained on small samples of de-noised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudo-trial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization and unbiased performance evaluation in machine learning approaches for brain decoding.

neuroscience↗

bioRxiv2018

Microstate EEGlab toolbox: An introductory guide

EEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important aspects about the currently applied methods are not readily comprehensible. Here we aim to increase the transparency about the methods to facilitate widespread application and reproducibility of EEG microstate analysis by introducing a new EEGlab toolbox for Matlab. EEGlab and the Microstate toolbox are open source, allowing the user to keep track of all details in every analysis step. The toolbox is specifically designed to facilitate the development of new methods. While the toolbox can be controlled with a graphical user interface (GUI), making it easier for newcomers to take their first steps in exploring the possibilities of microstate analysis, the Matlab framework allows advanced users to create scripts to automatise analysis for multiple subjects to avoid tediously repeating steps for every subject. This manuscript provides an overview of the most commonly applied microstate methods as well as a tutorial consisting of a comprehensive walk-through of the analysis of a small, publicly available dataset.

neuroscience↗