scholarly journals smBEVO: A computer vision approach to rapid baseline correction of single-molecule time series

2021 ◽  
Author(s):  
Khue Tran ◽  
Argha Bandyopadhyay ◽  
Marcel P Goldschen-Ohm
Keyword(s):  
Computer Vision ◽  
Time Series ◽  
Single Molecule ◽  
Molecular Mechanisms ◽  
Markov Models ◽  
Active Contours ◽  
Low Frequency ◽  
Lane Detection ◽  
Baseline Correction ◽  
Baseline Drift

Single-molecule time series inform on the dynamics of molecular mechanisms that are occluded in ensemble-averaged measures. Amplitude-based methods and hidden Markov models (HMMs) frequently used for interpreting these time series require removal of low frequency drift that can be difficult to completely avoid in real world experiments. Current approaches for drift correction primarily involve either tedious manual assignment of the baseline or unsupervised frameworks such as infinite HMMs coupled with baseline nodes that are computationally expensive and unreliable. Here, we develop an image-based method for baseline correction using techniques from computer vision such as lane detection and active contours. The approach is remarkably accurate and efficient, allowing for rapid analysis of single-molecule time series contaminated with nearly any type of slow baseline drift.

NOISE ANALYSIS IN STUDIES OF PROTEIN DYNAMICS AND MOLECULAR TRANSPORT

2004 ◽  
Vol 04 (01) ◽  
pp. L23-L31 ◽  
Author(s):  
SERGEY M. BEZRUKOV
Keyword(s):  
Protein Dynamics ◽  
Single Molecule ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Single Channel ◽  
Noise Analysis ◽  
Ionic Current ◽  
Low Frequency ◽  
Molecular Transport ◽  
Protein Molecule

Understanding the role of noise at cellular and higher hierarchical levels depends on our knowledge of the physical mechanisms of its generation. Conversely, noise is a rich source of information about these mechanisms. Using channel-forming protein molecules reconstituted into artificial 5-nm-thick insulating lipid films, it is possible to investigate noise in single-molecule experiments and to relate its origins to protein function. Recent progress in this field is reviewed with an emphasis on how this experimental technique can be used to study low-frequency protein dynamics, including not only reversible ionization of sites on the channel-forming protein molecule, but also molecular mechanisms of 1/f-noise generation. Several new applications of the single-molecule noise analysis to membrane transport problem are also addressed. Among those is a study on antibiotic translocation across bacterial walls. High-resolution recording of ionic current through the single channel, formed by the general bacterial porin, OmpF, enables us to resolve single-molecule events of antibiotic translocation.

scholarly journals A role for the fusogen eff-1 in epidermal stem cell number robustness in Caenorhabditis elegans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sneha L. Koneru ◽  
Fu Xiang Quah ◽  
Ritobrata Ghose ◽  
Mark Hintze ◽  
Nicola Gritti ◽  
...  
Keyword(s):  
Stem Cell ◽  
Caenorhabditis Elegans ◽  
Cell Fate ◽  
Single Molecule ◽  
Phenotypic Variability ◽  
Low Frequency ◽  
Time Lapse ◽  
Cell Number ◽  
Seam Cell ◽  
Neuronal Tissues

AbstractDevelopmental patterning in Caenorhabditis elegans is known to proceed in a highly stereotypical manner, which raises the question of how developmental robustness is achieved despite the inevitable stochastic noise. We focus here on a population of epidermal cells, the seam cells, which show stem cell-like behaviour and divide symmetrically and asymmetrically over post-embryonic development to generate epidermal and neuronal tissues. We have conducted a mutagenesis screen to identify mutants that introduce phenotypic variability in the normally invariant seam cell population. We report here that a null mutation in the fusogen eff-1 increases seam cell number variability. Using time-lapse microscopy and single molecule fluorescence hybridisation, we find that seam cell division and differentiation patterns are mostly unperturbed in eff-1 mutants, indicating that cell fusion is uncoupled from the cell differentiation programme. Nevertheless, seam cell losses due to the inappropriate differentiation of both daughter cells following division, as well as seam cell gains through symmetric divisions towards the seam cell fate were observed at low frequency. We show that these stochastic errors likely arise through accumulation of defects interrupting the continuity of the seam and changing seam cell shape, highlighting the role of tissue homeostasis in suppressing phenotypic variability during development.

scholarly journals Hopping and Flipping of RNA Polymerase on DNA during Recycling for Reinitiation after Intrinsic Termination in Bacterial Transcription

2021 ◽  
Vol 22 (5) ◽  
pp. 2398
Author(s):  
Wooyoung Kang ◽  
Seungha Hwang ◽  
Jin Young Kang ◽  
Changwon Kang ◽  
Sungchul Hohng
Keyword(s):  
Single Molecule ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Facilitated Diffusion ◽  
One Dimensional ◽  
Bacterial Transcription ◽  
Fluorescence Measurements ◽  
Dimensional Diffusion ◽  
Single Molecule Studies ◽  
Hopping Diffusion

Two different molecular mechanisms, sliding and hopping, are employed by DNA-binding proteins for their one-dimensional facilitated diffusion on nonspecific DNA regions until reaching their specific target sequences. While it has been controversial whether RNA polymerases (RNAPs) use one-dimensional diffusion in targeting their promoters for transcription initiation, two recent single-molecule studies discovered that post-terminational RNAPs use one-dimensional diffusion for their reinitiation on the same DNA molecules. Escherichia coli RNAP, after synthesizing and releasing product RNA at intrinsic termination, mostly remains bound on DNA and diffuses in both forward and backward directions for recycling, which facilitates reinitiation on nearby promoters. However, it has remained unsolved which mechanism of one-dimensional diffusion is employed by recycling RNAP between termination and reinitiation. Single-molecule fluorescence measurements in this study reveal that post-terminational RNAPs undergo hopping diffusion during recycling on DNA, as their one-dimensional diffusion coefficients increase with rising salt concentrations. We additionally find that reinitiation can occur on promoters positioned in sense and antisense orientations with comparable efficiencies, so reinitiation efficiency depends primarily on distance rather than direction of recycling diffusion. This additional finding confirms that orientation change or flipping of RNAP with respect to DNA efficiently occurs as expected from hopping diffusion.

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

scholarly journals Locally and Remotely Forced Subtropical AMOC Variability: A Matter of Time Scales

2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
North Atlantic ◽  
Time Scale ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Open Boundary ◽  
Overturning Circulation ◽  
Decadal Scale ◽  
Meridional Overturning

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).

scholarly journals Infrared nanospectroscopy reveals the molecular interaction fingerprint of an aggregation inhibitor with single Aβ42 oligomers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Simone Ruggeri ◽  
Johnny Habchi ◽  
Sean Chia ◽  
Robert I. Horne ◽  
Michele Vendruscolo ◽  
...  
Keyword(s):  
Small Molecules ◽  
Single Molecule ◽  
Protein Misfolding ◽  
Molecular Mechanisms ◽  
Chemical Sensitivity ◽  
Incomplete Knowledge ◽  
Parkinson’S Diseases ◽  
Aggregation Inhibitor ◽  
Misfolding Diseases

AbstractSignificant efforts have been devoted in the last twenty years to developing compounds that can interfere with the aggregation pathways of proteins related to misfolding disorders, including Alzheimer’s and Parkinson’s diseases. However, no disease-modifying drug has become available for clinical use to date for these conditions. One of the main reasons for this failure is the incomplete knowledge of the molecular mechanisms underlying the process by which small molecules interact with protein aggregates and interfere with their aggregation pathways. Here, we leverage the single molecule morphological and chemical sensitivity of infrared nanospectroscopy to provide the first direct measurement of the structure and interaction between single Aβ42 oligomeric and fibrillar species and an aggregation inhibitor, bexarotene, which is able to prevent Aβ42 aggregation in vitro and reverses its neurotoxicity in cell and animal models of Alzheimer’s disease. Our results demonstrate that the carboxyl group of this compound interacts with Aβ42 aggregates through a single hydrogen bond. These results establish infrared nanospectroscopy as a powerful tool in structure-based drug discovery for protein misfolding diseases.

scholarly journals Reconstruction of protein structures from single-molecule time series

2020 ◽  
Vol 153 (19) ◽  
pp. 194102
Author(s):  
Maximilian Topel ◽  
Andrew L. Ferguson
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Protein Structures

Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes

2012 ◽  
Vol 25 (6) ◽  
pp. 1814-1826 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Long Range ◽  
Ocean Circulation ◽  
Climate Models ◽  
Markov Models ◽  
Low Frequency ◽  
Model Error ◽  
Ocean Model ◽  
Coarse Grained ◽  
Predictive Skill ◽  
Relaxation To Equilibrium

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.

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