On the mechanism of protein fold-switching by a molecular sensor

Alternate frame folding (AFF) is a mechanism by which conformational change can be engineered into a protein. The protein structure switches from the wild-type fold (N) to a circularly-permuted fold (N\u27), or vice versa, in response to a signaling event such as ligand binding. Despite the fact that the two native states have similar structures, their interconversion involves folding and unfolding of large parts of the molecule. This rearrangement is reported by fluorescent groups whose relative proximities change as a result of the order-disorder transition. The nature of the conformational change is expected to be similar from protein to protein; thus, it may be possible to employ AFF as a general method to create optical biosensors. Toward that goal, we test basic aspects of the AFF mechanism using the AFF variant of calbindin D(9k). A simple three-state model for fold switching holds that N and N\u27 interconvert through the unfolded state. This model predicts that the fundamental properties of the switch--calcium binding affinity, signal response (i.e., fluorescence change upon binding), and switching rate--can be controlled by altering the relative stabilities of N and N\u27. We find that selectively destabilizing N or N\u27 changes the equilibrium properties of the switch (binding affinity and signal response) in accordance with the model. However, kinetic data indicate that the switching pathway does not require whole-molecule unfolding. The rate is instead limited by unfolding of a portion of the protein, possibly in concert with folding of a corresponding region

Microminiaturized, biopotential conditioning system (MBCS)

Multichannel, medical monitoring system allows almost complete freedom of movement for subject during monitoring periods. System comprises monitoring unit (biobelt), transmission line, and data acquisition unit. Belt, made of polybenzimidizole fabric, is wrapped around individual's waist and held in place by overlapping sections of Velcro closure material

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201

Theory of monolayers with boundaries: Exact results and Perturbative analysis

Domains and bubbles in tilted phases of Langmuir monolayers contain a class of textures knows as boojums. The boundaries of such domains and bubbles may display either cusp-like features or indentations. We derive analytic expressions for the textures within domains and surrounding bubbles, and for the shapes of the boundaries of these regions. The derivation is perturbative in the deviation of the bounding curve from a circle. This method is not expected to be accurate when the boundary suffers large distortions, but it does provide important clues with regard to the influence of various energetic terms on the order-parameter texture and the shape of the domain or bubble bounding curve. We also look into the effects of thermal fluctuations, which include a sample-size-dependent effective line tension.Comment: replaced with published version, 21 pages, 16 figures include

Cryptotomography: reconstructing 3D Fourier intensities from randomly oriented single-shot diffraction patterns

We reconstructed the 3D Fourier intensity distribution of mono-disperse prolate nano-particles using single-shot 2D coherent diffraction patterns collected at DESY's FLASH facility when a bright, coherent, ultrafast X-ray pulse intercepted individual particles of random, unmeasured orientations. This first experimental demonstration of cryptotomography extended the Expansion-Maximization-Compression (EMC) framework to accommodate unmeasured fluctuations in photon fluence and loss of data due to saturation or background scatter. This work is an important step towards realizing single-shot diffraction imaging of single biomolecules.Comment: 4 pages, 4 figure

Optomechanical coupling and damping of a carbon nanotube quantum dot

Carbon nanotubes are excellent nano-electromechanical systems, combining high resonance frequency, low mass, and large zero-point motion. At cryogenic temperatures they display high mechanical quality factors. Equally they are outstanding single electron devices with well-known quantum levels and have been proposed for the implementation of charge or spin qubits. The integration of these devices into microwave optomechanical circuits is however hindered by a mismatch of scales, between typical microwave wavelengths, nanotube segment lengths, and nanotube deflections. As experimentally demonstrated recently in [Blien et al., Nat. Comm. 11, 1363 (2020)], coupling enhancement via the quantum capacitance allows to circumvent this restriction. Here we extend the discussion of this experiment. We present the subsystems of the device and their interactions in detail. An alternative approach to the optomechanical coupling is presented, allowing to estimate the mechanical zero point motion scale. Further, the mechanical damping is discussed, hinting at hitherto unknown interaction mechanisms.Comment: 17 pages, 13 figures, 3 table

Adaptive Sampling Approach to the Negative Sign Problem in the Auxiliary Field Quantum Monte Carlo Method

We propose a new sampling method to calculate the ground state of interacting quantum systems. This method, which we call the adaptive sampling quantum monte carlo (ASQMC) method utilises information from the high temperature density matrix derived from the monte carlo steps. With the ASQMC method, the negative sign ratio is greatly reduced and it becomes zero in the limit $\Delta \tau$ goes to zero even without imposing any constraint such like the constraint path (CP) condition. Comparisons with numerical results obtained by using other methods are made and we find the ASQMC method gives accurate results over wide regions of physical parameters values.Comment: 8 pages, 7 figure