@article{21245, abstract = {Flickering light is a new promising, fully non-invasive brain stimulation technique that utilizes intermittent sensory stimulation to induce brainwave synchronization (entrainment). While the effects of 40 Hz externally induced neural entrainment have been extensively described, little is known about 60 Hz entrainment in humans. This study presents preliminary observations on the neural and somatic response to flickering 60 Hz light in healthy volunteers over a 3-week period. Fourteen volunteers were randomized to receive either 60 Hz flickering white light or constant light as sham (30-min sessions, 3 weeks, 5 days/week on weekdays). Neural entrainment was assessed with EEG on days 1, 5 and 19. Salivary cortisol and C-reactive protein (CRP) levels, measured with ELISA, assessed the somatic response to stimulation. Side effects and well-being were monitored via questionnaires. EEG recordings showed neural entrainment and synchrony in response to 60 Hz flickering light across multiple cortical regions, including occipital, central, temporal, and frontal areas. The entrainment power and synchronization between different cortical regions declined significantly by day 19 compared to day 1, indicating possible neural habituation. Cortisol and CRP salivary levels were unchanged, and minor side effects were reported with equal frequency in the active and sham groups. Our findings show that 60 Hz flickering light can induce significant neural entrainment and synchrony in healthy adults and is well tolerated. The decline in entrainment strength and neural synchrony observed with repeated 60 Hz stimulations suggests plastic changes in the cortex. To the best of our knowledge, this is the first study to characterize neural and somatic responses to repeated 60 Hz flickering visual stimuli. Given the well-known connection between 60 Hz brain oscillations and cognition, neuroplasticity, and their role in neuropsychiatric disorders, additional research in both preclinical and clinical settings is warranted.}, author = {Alamalhoda, MohammadAmin and Leesch, Friederike and Giovanetti, Francesca and Dunne, Eoghan and Pilloni, Giuseppina and Caffrey, Mark and O’Keeffe, Jack and Venturino, Alessandro and Ferretti, Maria Teresa}, issn = {1932-6203}, journal = {PLOS One}, number = {10}, publisher = {Public Library of Science}, title = {{Exploring neural entrainment and synchrony in response to repeated 60 Hz flickering white light in healthy volunteers}}, doi = {10.1371/journal.pone.0332310}, volume = {20}, year = {2025}, } @article{12706, abstract = {Allometric settings of population dynamics models are appealing due to their parsimonious nature and broad utility when studying system level effects. Here, we parameterise the size-scaled Rosenzweig-MacArthur differential equations to eliminate prey-mass dependency, facilitating an in depth analytic study of the equations which incorporates scaling parameters’ contributions to coexistence. We define the functional response term to match empirical findings, and examine situations where metabolic theory derivations and observation diverge. The dynamical properties of the Rosenzweig-MacArthur system, encompassing the distribution of size-abundance equilibria, the scaling of period and amplitude of population cycling, and relationships between predator and prey abundances, are consistent with empirical observation. Our parameterisation is an accurate minimal model across 15+ orders of mass magnitude.}, author = {Mckerral, Jody C. and Kleshnina, Maria and Ejov, Vladimir and Bartle, Louise and Mitchell, James G. and Filar, Jerzy A.}, issn = {1932-6203}, journal = {PLoS One}, number = {2}, pages = {e0279838}, publisher = {Public Library of Science}, title = {{Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations}}, doi = {10.1371/journal.pone.0279838}, volume = {18}, year = {2023}, } @article{12758, abstract = {AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is “solved”. However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.}, author = {Pak, Marina A. and Markhieva, Karina A. and Novikova, Mariia S. and Petrov, Dmitry S. and Vorobyev, Ilya S. and Maksimova, Ekaterina and Kondrashov, Fyodor and Ivankov, Dmitry N.}, issn = {1932-6203}, journal = {PLoS ONE}, number = {3}, publisher = {Public Library of Science}, title = {{Using AlphaFold to predict the impact of single mutations on protein stability and function}}, doi = {10.1371/journal.pone.0282689}, volume = {18}, year = {2023}, } @article{12759, abstract = {Stereological methods for estimating the 3D particle size and density from 2D projections are essential to many research fields. These methods are, however, prone to errors arising from undetected particle profiles due to sectioning and limited resolution, known as ‘lost caps’. A potential solution developed by Keiding, Jensen, and Ranek in 1972, which we refer to as the Keiding model, accounts for lost caps by quantifying the smallest detectable profile in terms of its limiting ‘cap angle’ (ϕ), a size-independent measure of a particle’s distance from the section surface. However, this simple solution has not been widely adopted nor tested. Rather, model-independent design-based stereological methods, which do not explicitly account for lost caps, have come to the fore. Here, we provide the first experimental validation of the Keiding model by comparing the size and density of particles estimated from 2D projections with direct measurement from 3D EM reconstructions of the same tissue. We applied the Keiding model to estimate the size and density of somata, nuclei and vesicles in the cerebellum of mice and rats, where high packing density can be problematic for design-based methods. Our analysis reveals a Gaussian distribution for ϕ rather than a single value. Nevertheless, curve fits of the Keiding model to the 2D diameter distribution accurately estimate the mean ϕ and 3D diameter distribution. While systematic testing using simulations revealed an upper limit to determining ϕ, our analysis shows that estimated ϕ can be used to determine the 3D particle density from the 2D density under a wide range of conditions, and this method is potentially more accurate than minimum-size-based lost-cap corrections and disector methods. Our results show the Keiding model provides an efficient means of accurately estimating the size and density of particles from 2D projections even under conditions of a high density.}, author = {Rothman, Jason Seth and Borges Merjane, Carolina and Holderith, Noemi and Jonas, Peter M and Angus Silver, R.}, issn = {1932-6203}, journal = {PLoS ONE}, number = {3 March}, publisher = {Public Library of Science}, title = {{Validation of a stereological method for estimating particle size and density from 2D projections with high accuracy}}, doi = {10.1371/journal.pone.0277148}, volume = {18}, year = {2023}, } @article{11704, abstract = {In Fall 2020, several European countries reported rapid increases in COVID-19 cases along with growing estimates of the effective reproduction rates. Such an acceleration in epidemic spread is usually attributed to time-dependent effects, e.g. human travel, seasonal behavioral changes, mutations of the pathogen etc. In this case however the acceleration occurred when counter measures such as testing and contact tracing exceeded their capacity limit. Considering Austria as an example, here we show that this dynamics can be captured by a time-independent, i.e. autonomous, compartmental model that incorporates these capacity limits. In this model, the epidemic acceleration coincides with the exhaustion of mitigation efforts, resulting in an increasing fraction of undetected cases that drive the effective reproduction rate progressively higher. We demonstrate that standard models which does not include this effect necessarily result in a systematic underestimation of the effective reproduction rate.}, author = {Budanur, Nazmi B and Hof, Björn}, issn = {1932-6203}, journal = {PLoS ONE}, number = {7}, publisher = {Public Library of Science}, title = {{An autonomous compartmental model for accelerating epidemics}}, doi = {10.1371/journal.pone.0269975}, volume = {17}, year = {2022}, } @article{9362, abstract = {A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.}, author = {Chalk, Matthew J and Tkačik, Gašper and Marre, Olivier}, issn = {1932-6203}, journal = {PLoS ONE}, number = {4}, publisher = {Public Library of Science}, title = {{Inferring the function performed by a recurrent neural network}}, doi = {10.1371/journal.pone.0248940}, volume = {16}, year = {2021}, } @article{9816, abstract = {Aims: Mass antigen testing programs have been challenged because of an alleged insufficient specificity, leading to a large number of false positives. The objective of this study is to derive a lower bound of the specificity of the SD Biosensor Standard Q Ag-Test in large scale practical use. Methods: Based on county data from the nationwide tests for SARS-CoV-2 in Slovakia between 31.10.–1.11. 2020 we calculate a lower confidence bound for the specificity. As positive test results were not systematically verified by PCR tests, we base the lower bound on a worst case assumption, assuming all positives to be false positives. Results: 3,625,332 persons from 79 counties were tested. The lowest positivity rate was observed in the county of Rožňava where 100 out of 34307 (0.29%) tests were positive. This implies a test specificity of at least 99.6% (97.5% one-sided lower confidence bound, adjusted for multiplicity). Conclusion: The obtained lower bound suggests a higher specificity compared to earlier studies in spite of the underlying worst case assumption and the application in a mass testing setting. The actual specificity is expected to exceed 99.6% if the prevalence in the respective regions was non-negligible at the time of testing. To our knowledge, this estimate constitutes the first bound obtained from large scale practical use of an antigen test.}, author = {Hledik, Michal and Polechova, Jitka and Beiglböck, Mathias and Herdina, Anna Nele and Strassl, Robert and Posch, Martin}, issn = {1932-6203}, journal = {PLoS ONE}, number = {7}, publisher = {Public Library of Science}, title = {{Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program}}, doi = {10.1371/journal.pone.0255267}, volume = {16}, year = {2021}, } @article{9821, abstract = {Heart rate variability (hrv) is a physiological phenomenon of the variation in the length of the time interval between consecutive heartbeats. In many cases it could be an indicator of the development of pathological states. The classical approach to the analysis of hrv includes time domain methods and frequency domain methods. However, attempts are still being made to define new and more effective hrv assessment tools. Persistent homology is a novel data analysis tool developed in the recent decades that is rooted at algebraic topology. The Topological Data Analysis (TDA) approach focuses on examining the shape of the data in terms of connectedness and holes, and has recently proved to be very effective in various fields of research. In this paper we propose the use of persistent homology to the hrv analysis. We recall selected topological descriptors used in the literature and we introduce some new topological descriptors that reflect the specificity of hrv, and we discuss their relation to the standard hrv measures. In particular, we show that this novel approach provides a collection of indices that might be at least as useful as the classical parameters in differentiating between series of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering from a stroke episode.}, author = {Graff, Grzegorz and Graff, Beata and Pilarczyk, Pawel and Jablonski, Grzegorz and Gąsecki, Dariusz and Narkiewicz, Krzysztof}, issn = {1932-6203}, journal = {PLoS ONE}, number = {7}, publisher = {Public Library of Science}, title = {{Persistent homology as a new method of the assessment of heart rate variability}}, doi = {10.1371/journal.pone.0253851}, volume = {16}, year = {2021}, } @article{906, abstract = {The origin and evolution of novel biochemical functions remains one of the key questions in molecular evolution. We study recently emerged methacrylate reductase function that is thought to have emerged in the last century and reported in Geobacter sulfurreducens strain AM-1. We report the sequence and study the evolution of the operon coding for the flavin-containing methacrylate reductase (Mrd) and tetraheme cytochrome (Mcc) in the genome of G. sulfurreducens AM-1. Different types of signal peptides in functionally interlinked proteins Mrd and Mcc suggest a possible complex mechanism of biogenesis for chromoproteids of the methacrylate redox system. The homologs of the Mrd and Mcc sequence found in δ-Proteobacteria and Deferribacteres are also organized into an operon and their phylogenetic distribution suggested that these two genes tend to be horizontally transferred together. Specifically, the mrd and mcc genes from G. sulfurreducens AM-1 are not monophyletic with any of the homologs found in other Geobacter genomes. The acquisition of methacrylate reductase function by G. sulfurreducens AM-1 appears linked to a horizontal gene transfer event. However, the new function of the products of mrd and mcc may have evolved either prior or subsequent to their acquisition by G. sulfurreducens AM-1.}, author = {Arkhipova, Oksana and Meer, Margarita and Mikoulinskaia, Galina and Zakharova, Marina and Galushko, Alexander and Akimenko, Vasilii and Kondrashov, Fyodor}, issn = {1932-6203}, journal = {PLoS One}, number = {5}, publisher = {Public Library of Science}, title = {{Recent origin of the methacrylate redox system in Geobacter sulfurreducens AM-1 through horizontal gene transfer}}, doi = {10.1371/journal.pone.0125888}, volume = {10}, year = {2015}, } @article{3906, author = {Cremer, Sylvia and Ugelvig, Line V and Drijfhout, Falko and Schlick Steiner, Birgit and Steiner, Florian and Seifert, Bernhard and Hughes, David and Schulz, Andreas and Petersen, Klaus and Konrad, Heino and Stauffer, Christian and Kiran, Kadri and Espadaler, Xavier and D'Ettorre, Patrizia and Aktaç, Nihat and Eilenberg, Jørgen and Jones, Graeme and Nash, David and Pedersen, Jes and Boomsma, Jacobus}, issn = {1932-6203}, journal = {PLoS One}, number = {12}, publisher = {Public Library of Science}, title = {{The evolution of invasiveness in garden ants}}, doi = {10.1371/journal.pone.0003838}, volume = {3}, year = {2008}, } @article{3970, abstract = {While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.}, author = {Dequéant, Mary and Ahnert, Sebastian and Edelsbrunner, Herbert and Fink, Thomas and Glynn, Earl and Hattem, Gaye and Kudlicki, Andrzej and Mileyko, Yuriy and Morton, Jason and Mushegian, Arcady and Pachter, Lior and Rowicka, Maga and Shiu, Anne and Sturmfels, Bernd and Pourquie, Olivier}, issn = {1932-6203}, journal = {PLoS One}, number = {8}, publisher = {Public Library of Science}, title = {{Comparison of pattern detection methods in microarray time series of the segmentation clock}}, doi = {10.1371/journal.pone.0002856}, volume = {3}, year = {2008}, }