Online input design for discrimination of linear models using concave minimization

Stochastic Closed-Loop Active Fault Diagnosis (CLAFD) aims to select the input sequentially in order to improve the discrimination of different models by minimizing the predicted error probability. As computation of these error probabilities encompasses the evaluation of multidimensional probability integrals, relaxation methods are of interest. This manuscript presents a new method that allows to make an improved trade-off between three factors -- namely maximized accuracy of diagnosis, minimized number of consecutive measurements to achieve that accuracy, and minimized computational effort per time step -- with respect to the state-of-the-art. It relies on minimizing an upper bound on the error probability, which is in the case of linear models with Gaussian noise proven to be concave in the most challenging discrimination conditions. A simulation study is conducted both for open-loop and feedback controlled candidate models. The results demonstrate the favorable trade-off using the new contributions in this manuscript

A 4D view on mRNA

Imaging single molecules in live cells in 4+ D (space, time and colors) is crucial for studying various biological processes, especially for observing the behavior of RNA molecules within the nuclear landscape [1]. RNA molecules are known to serve a multitude of tasks such as being templates for protein translation or to act as enzymes for regulating countless reactions in the nucleus [1]. Studying RNA kinetics in living cells can provide new information on RNA function or even human diseases, for instance caused by viruses such as the human immunodeficiency virus (HIV) [2]. A challenge to imaging nuclear RNA function is that the nucleus as a whole undergoes major reformation during the cell cycle [1] but the time required to step through the sample limits the capability to image large numbers of rapidly moving particles in a 3D space

Syncrip/hnRNP Q is required for activity-induced Msp300/Nesprin-1 expression and new synapse formation.

Memory and learning involve activity-driven expression of proteins and cytoskeletal reorganization at new synapses, requiring posttranscriptional regulation of localized mRNA a long distance from corresponding nuclei. A key factor expressed early in synapse formation is Msp300/Nesprin-1, which organizes actin filaments around the new synapse. How Msp300 expression is regulated during synaptic plasticity is poorly understood. Here, we show that activity-dependent accumulation of Msp300 in the postsynaptic compartment of the Drosophila larval neuromuscular junction is regulated by the conserved RNA binding protein Syncrip/hnRNP Q. Syncrip (Syp) binds to msp300 transcripts and is essential for plasticity. Single-molecule imaging shows that msp300 is associated with Syp in vivo and forms ribosome-rich granules that contain the translation factor eIF4E. Elevated neural activity alters the dynamics of Syp and the number of msp300:Syp:eIF4E RNP granules at the synapse, suggesting that these particles facilitate translation. These results introduce Syp as an important early acting activity-dependent regulator of a plasticity gene that is strongly associated with human ataxias

Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking

Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of \u3e 70% and a false-positive detection rate of \u3c 5% under conditions down to 17 photons/pixel background and 180 photons/molecule signal, which is beneficial for any kind of photon-limited application. Examples include limited brightness and photostability, phototoxicity in live-cell single-molecule imaging, and use of new labels for nanoscopy. We present simulations, experimental data, and tracking of low-signal mRNAs in yeast cells

Single-molecule FISH in Drosophila muscle reveals location dependent mRNA composition of megaRNPs [preprint]

Single-molecule fluorescence in-situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, methods for its accurate application to tissues are lacking. The utility of smFISH in thick tissue sections must overcome several challenges, including probe penetration of fixed tissue, accessibility of target mRNAs for probe hybridization, high fluorescent background, spherical aberration along the optical axis, and image segmentation of organelles. Here we describe how we overcame these obstacles to study mRNA localization in Drosophila larval muscle samples that approach 50 μm thickness. We use sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3-dimensional multiple-organelle segmentation. The latter allows using independent thresholds for different regions of interest within an image stack. Our approach therefore facilitates accurate measurement of mRNA location in thick tissues

An Automated Bayesian Pipeline for Rapid Analysis of Single-Molecule Binding Data [preprint]

Single-molecule binding assays enable the study of how molecular machines assemble and function. Current algorithms can identify and locate individual molecules, but require tedious manual validation of each spot. Moreover, no solution for high-throughput analysis of single-molecule binding data exists. Here, we describe an automated pipeline to analyze single-molecule data over a wide range of experimental conditions. We benchmarked the pipeline by measuring the binding properties of the well-studied, DNA-guided DNA endonuclease, TtAgo, an Argonaute protein from the Eubacterium Thermus thermophilus. We also used the pipeline to extend our understanding of TtAgo by measuring the protein\u27s binding kinetics at physiological temperatures and for target DNAs containing multiple, adjacent binding sites

Single-molecule FISH in Drosophila muscle reveals location dependent mRNA composition of megaRNPs [preprint]

Single-molecule fluorescence in-situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, methods for its accurate application to tissues are lacking. The utility of smFISH in thick tissue sections must overcome several challenges, including probe penetration of fixed tissue, accessibility of target mRNAs for probe hybridization, high fluorescent background, spherical aberration along the optical axis, and image segmentation of organelles. Here we describe how we overcame these obstacles to study mRNA localization in Drosophila larval muscle samples that approach 50 μm thickness. We use sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3-dimensional multiple-organelle segmentation. The latter allows using independent thresholds for different regions of interest within an image stack. Our approach therefore facilitates accurate measurement of mRNA location in thick tissues

Laser-Induced Cavitation for Controlling Crystallization from Solution

We demonstrate that a cavitation bubble initiated by a Nd:YAG laser pulse below breakdown threshold induces crystallization from supersaturated aqueous solutions with supersaturation and laser-energy dependent nucleation kinetics. Combining high-speed video microscopy and simulations, we argue that a competition between the dissipation of absorbed laser energy as latent and sensible heat dictates the solvent evaporation rate and creates a momentary supersaturation peak at the vapor-liquid interface. The number and morphology of crystals correlate to the characteristics of the simulated supersaturation peak

Low-cost fluorescence microscope with microfluidic device fabrication for optofluidic applications

Optofluidic devices have revolutionized the manipulation and transportation of fluid at smaller length scales ranging from micrometers to millimeters. We describe a dedicated optical setup for studying laser-induced cavitation inside a microchannel. In a typical experiment, we use a tightly focused laser beam to locally evaporate the solution laced with a dye resulting in the formation of a microbubble. The evolving bubble interface is tracked using high-speed microscopy and digital image analysis. Furthermore, we extend this system to analyze fluid flow through fluorescence$-$Particle Image Velocimetry (PIV) technique with minimal adaptations. In addition, we demonstrate the protocols for the in-house fabrication of a microchannel tailored to function as a sample holder in this optical setup. In essence, we present a complete guide for constructing a fluorescence microscope from scratch using standard optical components with flexibility in the design and at a lower cost compared to its commercial analogues.Comment: N. Nagalingam and A. Raghunathan contributed equally to this wor