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Significant advancements have been made towards exploitation of naturally available molecular motors and their associated cytoskeletal filaments in nanotechnological applications. For instance, myosin motors and actin filaments from muscle have been used with the aims to establish new approaches in biosensing and network-based biocomputation. The basis for these developments is a version of the in vitro motility assay (IVMA) where surface-adsorbed myosin motors propel the actin filaments along suitably derivatized nano-scale channels on nanostructured chips. These chips are generally assembled into custom-made microfluidic flow cells. For effective applications, particularly in biocomputation, it is important to appreciably prolong function of the biological system. Here, we systematically investigated potentially critical factors necessary to achieve this, such as biocompatibility of different components of the flow cell, the degree of air exposure, assay solution composition and nanofabrication methods. After optimizing these factors we prolonged the function of actin and myosin in nanodevices for biocomputation from <20 min to >60 min. In addition, we demonstrated that further optimizations could increase motility run times to >20 h. Of great importance for the latter development was a switch of glucose oxidase in the chemical oxygen scavenger system (glucose oxidase–glucose–catalase) to pyranose oxidase, combined with the use of blocking actin (non-fluorescent filaments that block dead motors). To allow effective testing of these approaches we adapted commercially available microfluidic channel slides, for the first time demonstrating their usefulness in the IVMA. As part of our study, we also demonstrate that myosin motor fragments can be stored at −80 °C for more than 10 years before use for nanotechnological purposes. This extended shelf-life is important for the sustainability of network-based biocomputation.

Molecular motor-driven filament systems have been extensively explored for biomedical and nanotechnological applications such as lab-on-chip molecular detection or network-based biocomputation. In these applications, filament transport conventionally occurs in two dimensions (2D), often guided along open, topographically and/or chemically structured channels which are coated by molecular motors. However, at crossing points of different channels the filament direction is less well determined and, though crucial to many applications, reliable guiding across the junction can often not be guaranteed. We here present a three-dimensional (3D) approach that eliminates the possibility for filaments to take wrong turns at junctions by spatially separating the channels crossing each other. Specifically, 3D junctions with tunnels and overpasses were manufactured on glass substrates by two-photon polymerization, a 3D fabrication technology where a tightly focused, femtosecond-pulsed laser is scanned in a layer-to-layer fashion across a photo-polymerizable inorganic–organic hybrid polymer (ORMOCER ® ) with µm resolution. Solidification of the polymer was confined to the focal volume, enabling the manufacturing of arbitrary 3D microstructures according to computer-aided design data. Successful realization of the 3D junction design was verified by optical and electron microscopy. Most importantly, we demonstrated the reliable transport of filaments, namely microtubules propelled by kinesin-1 motors, across these 3D junctions without junction errors. Our results open up new possibilities for 3D functional elements in biomolecular transport systems, in particular their implementation in biocomputational networks.

The 3‐satisfiability Problem (3‐SAT) is a demanding combinatorial problem that is of central importance among the nondeterministic polynomial (NP) complete problems, with applications in circuit design, artificial intelligence, and logistics. Even with optimized algorithms, the solution space that needs to be explored grows exponentially with the increasing size of 3‐SAT instances. Thus, large 3‐SAT instances require excessive amounts of energy to solve with serial electronic computers. Network‐based biocomputation (NBC) is a parallel computation approach with drastically reduced energy consumption. NBC uses biomolecular motors to propel cytoskeletal filaments through nanofabricated networks that encode mathematical problems. By stochastically exploring possible paths through the networks, the cytoskeletal filaments find possible solutions. However, to date, no NBC algorithm for 3‐SAT has been available. Herein, an algorithm that converts 3‐SAT into an NBC‐compatible network format is reported and four small 3‐SAT instances (with up to three variables and five clauses) using the actin–myosin biomolecular motor system are experimentally solved. Because practical polynomial conversions to 3‐SAT exist for many important NP complete problems, the result opens the door to enable NBC to solve small instances of a wide range of problems. Herein, an algorithm that encodes the 3‐satisfiability problem (3‐SAT) into network format is presented. Four small 3‐SAT instances with up to three variables and five clauses are solved experimentally; using myosin‐propelled actin filaments exploring nanofabricated 3‐SAT networks in a highly energy‐efficient manner.

Guanosine triphosphate (GTP) cyclohydrolase I (GCH1) catalyzes the conversion of GTP to dihydroneopterin triphosphate (H2NTP), the initiating step in the biosynthesis of tetrahydrobiopterin (BH4). Besides other roles, BH4 functions as cofactor in neurotransmitter biosynthesis. The BH4 biosynthetic pathway and GCH1 have been identified as promising targets to treat pain disorders in patients. The function of mammalian GCH1s is regulated by a metabolic sensing mechanism involving a regulator protein, GCH1 feedback regulatory protein (GFRP). GFRP binds to GCH1 to form inhibited or activated complexes dependent on availability of cofactor ligands, BH4 and phenylalanine, respectively. We determined high-resolution structures of human GCH1−GFRP complexes by cryoelectron microscopy (cryo-EM). Cryo-EM revealed structural flexibility of specific and relevant surface lining loops, which previously was not detected by X-ray crystallography due to crystal packing effects. Further, we studied allosteric regulation of isolated GCH1 by X-ray crystallography. Using the combined structural information, we are able to obtain a comprehensive picture of the mechanism of allosteric regulation. Local rearrangements in the allosteric pocket upon BH4 binding result in drastic changes in the quaternary structure of the enzyme, leading to a more compact, tense form of the inhibited protein, and translocate to the active site, leading to an open, more flexible structure of its surroundings. Inhibition of the enzymatic activity is not a result of hindrance of substrate binding, but rather a consequence of accelerated substrate binding kinetics as shown by saturation transfer difference NMR (STD-NMR) and site-directed mutagenesis. We propose a dissociation rate controlled mechanism of allosteric, noncompetitive inhibition.

Significant advancements have been made towards exploitation of naturally available molecular motors and their associated cytoskeletal filaments in nanotechnological applications. For instance, myosin motors and actin filaments from muscle have been used with the aims to establish new approaches in biosensing and network-based biocomputation. The basis for these developments is a version of the in vitro motility assay (IVMA) where surface-adsorbed myosin motors propel the actin filaments along suitably derivatized nano-scale channels on nanostructured chips. These chips are generally assembled into custom-made microfluidic flow cells. For effective applications, particularly in biocomputation, it is important to appreciably prolong function of the biological system. Here, we systematically investigated potentially critical factors necessary to achieve this, such as biocompatibility of different components of the flow cell, the degree of air exposure, assay solution composition and nanofabrication methods. After optimizing these factors we prolonged the function of actin and myosin in nanodevices for biocomputation from <20 min to >60 min. In addition, we demonstrated that further optimizations could increase motility run times to >20 h. Of great importance for the latter development was a switch of glucose oxidase in the chemical oxygen scavenger system (glucose oxidase–glucose–catalase) to pyranose oxidase, combined with the use of blocking actin (non-fluorescent filaments that block dead motors). To allow effective testing of these approaches we adapted commercially available microfluidic channel slides, for the first time demonstrating their usefulness in the IVMA. As part of our study, we also demonstrate that myosin motor fragments can be stored at −80 °C for more than 10 years before use for nanotechnological purposes. This extended shelf-life is important for the sustainability of network-based biocomputation.

Network-based biocomputing (NBC) presents an energy-efficient, parallel computing approach for solving nondeterministic polynomial time (NP) complete problems by leveraging motor-driven cytoskeletal filaments that explore all possible solutions through nanofabricated networks in a massively parallel fashion. However, guiding errors at pass junctions, where filaments deviate from their intended path, currently limit the scalability of NBC systems. In this study, we addressed this critical challenge by fabricating sub-200 nm channel geometries using modified electron-beam-lithography and reactive-ion-etching protocols to physically constrain the trajectories of kinesin-driven microtubules and enhance path fidelity. Investigating junction designs with varying channel widths, we demonstrate that reducing channel width significantly lowers junction error rates. Practically error-free junction performance was achieved by scaling down the entire network geometry by a factor of two. These optimized junctions were incorporated into NBC networks that successfully solved 24- and 25-set instances of the Exact Cover problem, representing solution spaces of approximately 16 million and 33 million, respectively. This work establishes a new benchmark in NBC performance and represents a computational scale far beyond what has been achieved in prior demonstrations.

The 3-Satisfiability Problem (3-SAT) is a demanding combinatorial problem, of central importance among the non-deterministic polynomial (NP) complete problems, with applications in circuit design, artificial intelligence and logistics. Even with optimized algorithms, the solution space that needs to be explored grows exponentially with increasing size of 3-SAT instances. Thus, large 3-SAT instances require excessive amounts of energy to solve with serial electronic computers. Network-based biocomputation (NBC) is a multidisciplinary parallel computation approach with drastically reduced energy consumption. NBC uses biomolecular motors to propel cytoskeletal filaments through nanofabricated networks that encode the mathematical problems. By stochastically exploring possible paths through the networks, the cytoskeletal filaments find possible solutions to the encoded problem instance. Here we first report a novel algorithm that converts 3-SAT into NBC-compatible network format. We demonstrate that this algorithm works in practice, by experimentally solving four small 3-SAT instances (with up to 3 variables and 5 clauses) using the actin-myosin biomolecular motor system. This is a key step towards the broad general applicability of NBC because polynomial conversions to 3-SAT exist for a wide set of important NP-complete problems.

Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters. •We address the problem of relating neural oscillations to cognitive function.•Standard approaches are shown to be sub-optimal for EEG/MEG data.•A novel source power correlation (SPoC) approach is presented.•SPoC is validated in realistic EEG simulations, where it outperforms other methods.•Furthermore, SPoC is shown to outperform other methods in a real EEG experiment.

The visible matter in the universe is turbulent and magnetized. Turbulence in galaxy clusters is produced by mergers and by jets of the central galaxies and believed responsible for the amplification of magnetic fields. We report on experiments looking at the collision of two laser-produced plasma clouds, mimicking, in the laboratory, a cluster merger event. By measuring the spectrum of the density fluctuations, we infer developed, Kolmogorov-like turbulence. From spectral line broadening, we estimate a level of turbulence consistent with turbulent heating balancing radiative cooling, as it likely does in galaxy clusters. We show that the magnetic field is amplified by turbulent motions, reaching a nonlinear regime that is a precursor to turbulent dynamo. Thus, our experiment provides a promising platform for understanding the structure of turbulence and the amplification of magnetic fields in the universe.

Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the cross-correlogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one. The filters can be used to compute a multivariate extension of the cross-correlogram, the canonical correlogram, between data sources that have different dimensionalities and temporal resolutions. The canonical correlogram reflects the coupling dynamics between the two sources. The temporal filter reveals which features in the data give rise to these couplings and when they do so. We present results from simulations and neuroscientific experiments showing that tkCCA yields easily interpretable temporal filters and correlograms. In the experiments, we simultaneously performed electrode recordings and functional magnetic resonance imaging (fMRI) in primary visual cortex of the non-human primate. While electrode recordings reflect brain activity directly, fMRI provides only an indirect view of neural activity via the Blood Oxygen Level Dependent (BOLD) response. Thus it is crucial for our understanding and the interpretation of fMRI signals in general to relate them to direct measures of neural activity acquired with electrodes. The results computed by tkCCA confirm recent models of the hemodynamic response to neural activity and allow for a more detailed analysis of neurovascular coupling dynamics.