Dynamic Deconvolution Research Articles

Terahertz waves regulate the mechanical unfolding of tau pre-mRNA hairpins

Intermolecular interactions, including hydrogen bonds, dominate the pairing and unpairing of nucleic acid chains in the transfer process of genetic information. The energy of THz waves just matches with the weak interactions, so THz waves may interact with biomolecules. Here, the dynamic effects of THz electromagnetic (EM) waves on the mechanical unfolding process of RNA hairpins (WT-30nt and its mutants, rHP, SARS-CoV-2, and SRV-1 SF206) are investigated using steered molecular dynamics (SMD) simulations. The results show that THz waves can either promote the unfolding of the double helix of the RNA hairpin during the initial unfolding phase (4-21.8 THz) or significantly enhance (23.8 and 25.5 THz) or weaken (37.4 and 41.2 THz) its structural stability during unfolding. Our findings have important implications for applying THz waves to regulate dynamic deconvolution processes, such as gene replication, transcription, and translation.

Dynamic deconvolution and identification of independent autoregressive sources

We consider a multi‐variate system , where the unobserved components are independent AR(1) processes and the number of sources is greater than the number of observed outputs. We show that the mixing matrix , the AR(1) coefficients and distributions of can be identified (up to scale factors of ), which solves the dynamic deconvolution problem. The proof is constructive and allows us to introduce simple consistent estimators of all unknown scalar and functional parameters of the model. The approach is illustrated by an estimation and identification of the dynamics of unobserved short‐ and long‐run components in a time series. Applications to causal models with structural innovations are also discussed, such as the identification in error‐in‐variables models and causal mediation models.

Pixel Voting Decoder: A novel decoder that regresses pixel relationships for segmentation

With the rapid development of the convolutional neural network, both instance segmentation and semantic segmentation have achieved remarkable performances. Recently, many efforts have been made to use a unified Encoder-Decoder architecture to solve these two segmentation tasks simultaneously. The encoder extracts high-level features from the input images for both tasks. However, existing decoders cannot meet the performance requirements of these two tasks: the semantic segmentation decoder is not flexible enough for instance segmentation, and the instance segmentation decoder lacks the precision of semantic segmentation. Therefore, we introduce a novel Pixel Voting Decoder to satisfy both precision and flexibility. The proposed decoder regresses the interlayer pixel relationships between the input and output feature maps across the convolutional layers. Then, the pixel relationships are regarded as the pixel votes for dynamically decoding the higher level information from the encoder. Finally, we propose the dynamic deconvolution to make full use of the votes for each pixel during the decoding process. Meanwhile, the matrix computation for the dynamic deconvolution is designed to boost the calculation. Experiments show that the proposed method can achieve better performance than the well-known methods on both instance segmentation on the COCO dataset and semantic segmentation on the Cityscapes dataset. The matrix implementation of the dynamic deconvolution also shows its high efficiency and feasibility.

FIGS: Featured Ion-Guided Stoichiometry for Data-Independent Proteomics through Dynamic Deconvolution.

Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.

Time-Frequency Domain Deconvolution based on Synchrosqueezing Generalized S Transform

Complex geological characteristics and deepening of the mining depth are the difficulties of oil and gas exploration at this stage, so high-resolution processing of seismic data is needed to obtain more effective information. Starting from the time-frequency analysis method, we propose a time-frequency domain dynamic deconvolution based on the Synchrosqueezing generalized S transform (SSGST). Combined with spectrum simulation to estimate the wavelet amplitude spectrum, the dynamic convolution model is used to eliminate the influence of dynamic wavelet on seismic records, and the seismic signal with higher time-frequency resolution can be obtained. Through the verification of synthetic signals and actual signals, it is concluded that the time-frequency domain dynamic deconvolution based on the SSGST algorithm has a good effect in improving the resolution and vertical resolution of the thin layer of seismic data.

Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Advances in single cell RNA-sequencing (scRNA-seq) allow probing of these immune interactions, such as cell-type compositions, which are then interpreted by deconvolution algorithms using bulk RNA-seq measurements. However, not all aspects of immune surveillance are represented by current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we develop a deconvolution algorithm for inferring cell-type specific infection responses from bulk measurements. We apply our dynamic deconvolution algorithm to a cohort of healthy individuals challenged ex vivo with Salmonella, and to three cohorts of tuberculosis patients during different stages of disease. We reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and human infection outcomes.

Inverse problem for Goupillaud‐layered earth model and dynamic deconvolution

ABSTRACTThis paper presents, in a tutorial form, some analytical inversion techniques for the Goupillaud‐layered earth model. Finding the reflection coefficients from the reflection seismogram is the inverse problem for the model. For this reason we present a thorough description of the inverse problem for the Goupillaud model, two solutions to solve the inverse scattering problem using linear discrete equations and the solution obtained using the classic dynamic deconvolution method. The inversion is achieved using Robinson's polynomials Pk(z), Qk(z), Ak(z) and their reverse polynomials, as well as some properties of the model (the Lorentz transformation and the Einstein subtraction formula). The method of dynamic deconvolution, which makes the inversion of the model very simple computationally, is based on the physical structure of the reflection seismogram. We present the classic dynamic deconvolution algorithm for the non‐free‐surface Goupillaud model to show that the dynamic deconvolution method can provide efficient discrete procedures for the inversion. For this reason, though the inverse dynamic deconvolution procedures are old algorithms, they could be useful today for solving inverse scattering problems arising in exploration geophysics and various fields.

Gravimetric and dynamic deconvolution of global EQCM response of carbon nanotube based electrodes by Ac-electrogravimetry

The capacitive charge storage of carbon nanotube (CNT) based electrodes was investigated by ac-electrogravimetry which couples fast quartz crystal microbalance (QCM) and electrochemical impedance spectroscopy (EIS). In contact with an aqueous NaCl electrolyte, evidence was found that there are two types of cations (Na+·H2O and H+) electroadsorbed with different kinetics for cathodic potentials and the Cl− ions for anodic potentials together with free water molecules. The reconstruction of the total mass response from independent ac-electrogravimetry measurements agrees perfectly well with the global EQCM response. Our findings reveal the unique sensitivity of the ac-electrogravimetry to provide a fair gravimetric and dynamic deconvolution of the global EQCM responses.

Dynamic covalent organocatalysts discovered from catalytic systems through rapid deconvolution screening.

The first example of a bifunctional organocatalyst assembled through dynamic covalent chemistry (DCC) is described. The catalyst is based on reversible imine chemistry and can catalyze the Morita–Baylis–Hillman (MBH) reaction of enones with aldehydes or N-tosyl imines. Furthermore, these dynamic catalysts were shown to be optimizable through a systemic screening approach, in which large mixtures of catalyst structures were generated, and the optimal catalyst could be directly identified by using dynamic deconvolution. This strategy allowed one-pot synthesis and in situ evaluation of several potential catalysts without the need to separate, characterize, and purify each individual structure. The systems were furthermore shown to catalyze and re-equilibrate their own formation through a previously unknown thiourea-catalyzed transimination process.

Dynamic deconvolution of seismic data based on generalized S-transform

Abstract The commonly used methods to improve resolution of seismic data are based on stationary convolution model that is inconsistent with the actual propagation law of seismic wavelet in inhomogeneous media. Therefore, to address this artifact, in this paper authors take seismic scattering and attenuation into integrative consideration, and propose an adaptively dynamic deconvolution method based on generalized S-transform (GST). This method introduces the favorable multi-resolution characteristics of GST into the dynamic deconvolution. Firstly, GST of nonstationary seismic trace can be approximately presented as the product of GST of the dynamic propagating wavelet and the reflectivity in time–frequency domain. We then use polynomial fitting to model the time–frequency spectra of nonstationary seismic trace to obtain the estimation of dynamic propagation wavelet and the reflectivity. This method, without calculating directly the value of Q , is applicable to the case when Q varies, and is robust in the presence of noise. Applications to synthetic and field data validate that this method can effectively enhance seismic data resolution without boosting noise, and restore the attenuated energy of nonstationary seismic signal.

Dominant behaviours in the expression of human carbonic anhydrase hCA I activity.

Here we describe the screening via Dynamic Deconvolution of DCLs of inhibitors (CAIs) and activators (CAAs) of hCA I. The inhibitory effects dominate over the activating ones, while the CAAs may be identified in the absence of CAIs.

Auto- and cross-power spectral analysis of dual trap optical tweezer experiments using Bayesian inference

The thermal fluctuations of micron-sized beads in dual trap optical tweezer experiments contain complete dynamic information about the viscoelastic properties of the embedding medium and-if present-macromolecular constructs connecting the two beads. To quantitatively interpret the spectral properties of the measured signals, a detailed understanding of the instrumental characteristics is required. To this end, we present a theoretical description of the signal processing in a typical dual trap optical tweezer experiment accounting for polarization crosstalk and instrumental noise and discuss the effect of finite statistics. To infer the unknown parameters from experimental data, a maximum likelihood method based on the statistical properties of the stochastic signals is derived. In a first step, the method can be used for calibration purposes: We propose a scheme involving three consecutive measurements (both traps empty, first one occupied and second empty, and vice versa), by which all instrumental and physical parameters of the setup are determined. We test our approach for a simple model system, namely a pair of unconnected, but hydrodynamically interacting spheres. The comparison to theoretical predictions based on instantaneous as well as retarded hydrodynamics emphasizes the importance of hydrodynamic retardation effects due to vorticity diffusion in the fluid. For more complex experimental scenarios, where macromolecular constructs are tethered between the two beads, the same maximum likelihood method in conjunction with dynamic deconvolution theory will in a second step allow one to determine the viscoelastic properties of the tethered element connecting the two beads.

Deconvolution of dynamic mechanical networks

Time-resolved single-molecule biophysical experiments yield data that contain a wealth of dynamic information, in addition to the equilibrium distributions derived from histograms of the time series. In typical force spectroscopic setups the molecule is connected via linkers to a readout device, forming a mechanically coupled dynamic network. Deconvolution of equilibrium distributions, filtering out the influence of the linkers, is a straightforward and common practice. We have developed an analogous dynamic deconvolution theory for the more challenging task of extracting kinetic properties of individual components in networks of arbitrary complexity and topology. Our method determines the intrinsic linear response functions of a given object in the network, describing the power spectrum of conformational fluctuations. The practicality of our approach is demonstrated for the particular case of a protein linked via DNA handles to two optically trapped beads at constant stretching force, which we mimic through Brownian dynamics simulations. Each well in the protein free energy landscape (corresponding to folded, unfolded, or possibly intermediate states) will have its own characteristic equilibrium fluctuations. The associated linear response function is rich in physical content, because it depends both on the shape of the well and its diffusivity-a measure of the internal friction arising from such processes as the transient breaking and reformation of bonds in the protein structure. Starting from the autocorrelation functions of the equilibrium bead fluctuations measured in this force clamp setup, we show how an experimentalist can accurately extract the state-dependent protein diffusivity using a straightforward two-step procedure.

COMPARISON OF THE METHODS OF 1-D PERMITTIVITY PROFILE RECONSTRUCTION: REAL MEASUREMENT DATA PROCESSING

Free-space measurements of reflection coefficient were carried out in frequency band of 17−25.5 GHz for testing layered structures and then reconstruction of permittivity profiles was performed using three well-known methods. The methods are dynamic deconvolution method, discrete analog of Gel’fand-Levitan method and Newton-Kantorovich iterative scheme. The results of reconstruction are discussed.

Glycosyldisulfides from dynamic combinatorial libraries as O-glycoside mimetics for plant and endogenous lectins: Their reactivities in solid-phase and cell assays and conformational analysis by molecular dynamics simulations

Dynamic combinatorial library design exploiting the thiol–disulfide exchange readily affords access to glycosyldisulfides. In order to reveal lectin-binding properties of this type of non-hydrolyzable sugar derivative, libraries originating from a mixture of common building blocks of natural glycans and thiocompounds were tested against three plant agglutinins with specificity to galactose, fucose or N-acetylgalactosamine, respectively, in a solid-phase assay. Extent of lectin binding to matrix-immobilized neoglycoprotein presenting the cognate sugar could be reduced, and evidence for dependence on type of carbohydrate was provided by dynamic deconvolution. Glycosyldisulfides also maintained activity in assays of increased physiological relevance, that is, using native tumor cells and also adding to the test panel an endogenous lectin (galectin-3) involved in tumor spread and cardiac dysfunction. N-Acetylgalactosamine was pinpointed as the most important building block of libraries for the human lectin and the digalactoside as most potent compound acting on the toxic mistletoe agglutinin which is closely related to the biohazard ricin. Because this glycosyldisulfide, which even surpasses lactose in inhibitory capacity, rivals thiodigalactoside as inhibitor, their degrees of intramolecular flexibility were comparatively analyzed by computational calculations. Molecular dynamics runs with explicit consideration of water molecules revealed a conspicuously high degree of potential for shape alterations by the disulfide’s three-bond system at the interglycosidic linkage. The presented evidence defines glycosyldisulfides as biologically active ligands for lectins.

Measurement of cerebral circulation by dynamic susceptibility contrast-enhanced MRI: effect of tracer delay

Abstract Cerebral blood flow (CBF) and mean transit time (MTT) can be determined by dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) and deconvolution with an arterial input function (AIF). However, deconvolution with a singular value decomposition (SVD) method is sensitive to the tracer delay that often occurs in patients with cerebrovascular disease. We developed a delay correction method that determines tracer delay by means of least-squares fitting pixel-by-pixel. The corrected CBF was determined by SVD deconvolution after time-shifting of the measured concentration curve. The simulations showed that the corrected CBF was insensitive to tracer delay irrespective of the vascular model, although CBF fluctuation increased slightly. We applied the delay correction to the CBF and MTT images acquired for a patient with hyperacute stroke and unilateral occlusion of the middle cerebral artery. The delay correction modulated the contrast of perfusion images. For hyperacute stroke patients, tracer delay correction is essential to obtain reliable CBF and MTT images when SVD deconvolution is used.

Dynamic combinatorial carbohydrate libraries: probing the binding site of the concanavalin A lectin.

Dynamic combinatorial chemistry (DCC) has emerged as an efficient approach to receptor/ligand identification based on the generation of combinatorial libraries by reversible interconversion of the library constituents. In this study, the implementation of such libraries on carbohydrate-lectin interactions was examined with the plant lectin Concanavalin A as a target species. Dynamic carbohydrate libraries were generated from a pool of carbohydrate aldehydes and hydrazide linker/scaffold components through reversible acylhydrazone exchange, resulting in libraries containing up to 474 constituents. Dynamic deconvolution allowed the efficient identification of the structural features required for binding to Concanavalin A and the selection of a strong binder, a tritopic mannoside, showing an IC(50)-value of 22 microM.

Generation of bis-cationic heterocyclic inhibitors of Bacillus subtilis HPr kinase/phosphatase from a ditopic dynamic combinatorial library.

Ditopic dynamic combinatorial libraries were generated and screened toward inhibition of the bifunctional enzyme HPr kinase/phosphatase from Bacillus subtilis. The libraries were composed of all possible combinations resulting from the dynamic interconversion of 16 hydrazides and five monoaldehyde or dialdehyde building blocks, resulting in libraries containing up to 440 different constituents. Of all possible acyl hydrazones formed, active compounds containing two terminal cationic heterocyclic recognition groups separated by a spacer of appropriate structure could be rapidly identified using a dynamic deconvolution procedure. Thus, parallel testing of sublibraries where one specific component was excluded basically revealed all the essential components. A potent ditopic inhibitor, based on 2-aminobenzimidazole, was identified from the process.

Dynamic Deconvolution of a Pre-Equilibrated Dynamic Combinatorial Library of Acetylcholinesterase Inhibitors

A dynamic combinatorial library composed of interconverting acylhydrazones has been generated and screened towards inhibition of acetylcholinesterase from the electric ray Torpedo marmorata. Starting from a small set (13) of initial hydrazide and aldehyde building blocks, a library containing possibly 66 different species was obtained in a single operation. Of all possible acylhydrazones formed, active compounds containing two terminal cationic recognition groups separated by an appropriate distance, permitting two-site binding, could be rapidly identified by using a dynamic deconvolution–screening procedure, based on the sequential removal of starting building blocks. A very potent bis-pyridinium inhibitor (Ki=1.09 nM, αKi=2.80 nM) was selected from the process and the contribution of various structural features to inhibitory potency was evaluated.

Dynamic deconvolution of a pre-equilibrated dynamic combinatorial library of acetylcholinesterase inhibitors.

A dynamic combinatorial library composed of interconverting acylhydrazones has been generated and screened towards inhibition of acetylcholinesterase from the electric ray Torpedo marmorata. Starting from a small set (13) of initial hydrazide and aldehyde building blocks, a library containing possibly 66 different species was obtained in a single operation. Of all possible acylhydrazones formed, active compounds containing two terminal cationic recognition groups separated by an appropriate distance, permitting two-site binding, could be rapidly identified by using a dynamic deconvolution--screening procedure, based on the sequential removal of starting building blocks. A very potent bis-pyridinium inhibitor (K(i)=1.09 nM, alphaK(i)=2.80 nM) was selected from the process and the contribution of various structural features to inhibitory potency was evaluated.